{"id":287481,"date":"2026-01-22T10:24:14","date_gmt":"2026-01-22T15:24:14","guid":{"rendered":"https:\/\/monday.com\/blog\/?p=287481"},"modified":"2026-07-05T09:57:09","modified_gmt":"2026-07-05T14:57:09","slug":"how-to-build-ai-agents-for-beginners","status":"publish","type":"post","link":"https:\/\/monday.com\/blog\/ai-agents\/how-to-build-ai-agents-for-beginners\/","title":{"rendered":"How to build an AI agent for beginners in 2026"},"content":{"rendered":"<div class=\"text-block\" id=\"text-block-1\">\n<h2 class=\"h2 text-block__title\">Key takeaways<\/h2>\n<ul>\n<li>\n<p><strong>Start with a single use case:<\/strong> beginners get the best results by automating one clear, repetitive task before expanding to more complex agent workflows<\/p>\n<\/li>\n<li>\n<p><strong>No-code lowers the barrier:<\/strong> modern platforms let anyone build an AI agent using plain language and visual tools, without technical skills<\/p>\n<\/li>\n<li>\n<p><strong>Custom agents beat generic tools:<\/strong> building your own agents gives you tighter control over data access, behavior, and integrations with existing systems<\/p>\n<\/li>\n<li>\n<p><strong>Guardrails and safety matter:<\/strong> input validation, output filtering, and human-in-the-loop checkpoints keep your agents reliable and trustworthy from day one<\/p>\n<\/li>\n<li>\n<p><strong>Team-based agents scale better:<\/strong> the monday AI Work Platform makes it easier to create multiple focused agents that work together across everyday workflows<\/p>\n<\/li>\n<\/ul>\n<a class=\"cta-button blue-button\" aria-label=\"Get started with monday.com\" href=\"https:\/\/auth.monday.com\/users\/sign_up_new\" target=\"_blank\">Get started with monday.com<\/a>\n\n<\/div>\n<div class=\"text-block\" id=\"text-block-2\">\n<img width=\"1024\" height=\"571\" src=\"https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/agent20factory20features-create20your20own-2-1024x571.jpg\" class=\"attachment-large size-large\" alt=\"\" loading=\"lazy\" decoding=\"async\" srcset=\"https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/agent20factory20features-create20your20own-2-1024x571.jpg 1024w, https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/agent20factory20features-create20your20own-2-300x167.jpg 300w, https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/agent20factory20features-create20your20own-2-768x429.jpg 768w, https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/agent20factory20features-create20your20own-2.jpg 1405w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/>\n<\/div>\n<div class=\"text-block\" id=\"text-block-3\">\n<h2 class=\"h2 text-block__title\">What are AI agents and how do they work?<\/h2>\n<p>AI agents are software programs that can complete tasks and make decisions without constant oversight, and the market is projected to <a href=\"https:\/\/www.grandviewresearch.com\/industry-analysis\/ai-agents-market-report\" target=\"_blank\">reach USD 50.31 billion<\/a> by 2030. They use artificial intelligence to understand your needs, take action, and learn from the results.<\/p>\n<p>Think of these agents as smart assistants that don&#8217;t just wait for commands. Instead, they actively work toward <a href=\"https:\/\/monday.com\/blog\/project-management\/goal-setting-template\/\" target=\"_blank\">goals you set<\/a>, make choices based on context, and handle complex workflows from start to finish.<\/p>\n<p>Every AI agent follows a core operating cycle. First, the agent perceives inputs from its environment, like an incoming email, a database update, or a user message. Then it reasons about the best course of action by weighing options against its goals and instructions. Next, it acts by executing a task, sending a response, or updating a system. Finally, it learns from the outcome and uses that feedback to improve future decisions.<\/p>\n<p>To understand how agents operate autonomously, it&#8217;s important to know their core components. These work together to process information, make decisions, and take action on your behalf:<\/p>\n<ul>\n<li>\n<p><strong>Language understanding:<\/strong> AI agents process natural language, so you can communicate with them as you would with a colleague<\/p>\n<\/li>\n<li>\n<p><strong>Memory systems:<\/strong> they remember past interactions and use that context to make smarter decisions<\/p>\n<\/li>\n<li>\n<p><strong>Integration capabilities:<\/strong> agents connect to your existing tools, such as calendars, email, and databases, to take real action<\/p>\n<\/li>\n<li>\n<p><strong>Decision logic:<\/strong> they evaluate options and choose the best path forward based on your goals<\/p>\n<\/li>\n<\/ul>\n<p>For\u00a0more context, imagine you build an AI agent to manage customer inquiries. It reads incoming messages, understands the customer&#8217;s need, checks your knowledge base for answers, and either responds directly or routes the inquiry to the right team member,\u00a0all without you lifting a finger.<\/p>\n\n<\/div>\n<div class=\"text-block\" id=\"text-block-4\">\n<img width=\"1024\" height=\"430\" src=\"https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/agent20factory20features20screenshot202-1-1024x430.jpg\" class=\"attachment-large size-large\" alt=\"\" loading=\"lazy\" decoding=\"async\" srcset=\"https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/agent20factory20features20screenshot202-1-1024x430.jpg 1024w, https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/agent20factory20features20screenshot202-1-300x126.jpg 300w, https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/agent20factory20features20screenshot202-1-768x323.jpg 768w, https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/agent20factory20features20screenshot202-1-1536x646.jpg 1536w, https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/agent20factory20features20screenshot202-1.jpg 1920w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/>\n<\/div>\n<div class=\"text-block\" id=\"text-block-5\">\n<h2 class=\"h2 text-block__title\">Why you need to build your own AI agents<\/h2>\n<p>Generic AI platforms work well for common processes. However, your business has unique processes, specific requirements, and proprietary information that off-the-shelf solutions can&#8217;t handle properly.<\/p>\n<p>Building custom AI agents gives you control over exactly how they work. You decide what data they access, how they make decisions, and which systems they connect to.<\/p>\n<p>The business case for custom AI agents comes down to several key advantages:<\/p>\n<ul>\n<li>\n<p><strong>Perfect fit for your workflows:<\/strong> design agents that match your exact processes instead of changing how you work<\/p>\n<\/li>\n<li>\n<p><strong>Complete data control:<\/strong> keep sensitive information within your systems rather than sending it to third-party services<\/p>\n<\/li>\n<li>\n<p><strong>Seamless integration:<\/strong> connect directly to your existing tools without compatibility headaches<\/p>\n<\/li>\n<li>\n<p><strong>Long-term cost savings:<\/strong> eliminate monthly subscriptions for multiple <a href=\"https:\/\/monday.com\/blog\/project-management\/top-ai-platforms\/\" target=\"_blank\">AI platforms<\/a><\/p>\n<\/li>\n<li>\n<p><strong>Competitive edge:<\/strong> create capabilities your competitors can&#8217;t buy or copy<\/p>\n<\/li>\n<\/ul>\n<p>Consider a <a href=\"https:\/\/monday.com\/blog\/project-management\/real-estate-ai\/\" target=\"_blank\">real estate<\/a> team that needs to qualify leads, schedule property viewings, and follow up with prospects. A generic chatbot might handle basic questions, but it can&#8217;t manage the entire workflow.<\/p>\n<p>A custom agent, however, can access your property database, check agent calendars, and send personalized follow-ups based on viewing history. It can even track which approaches convert most effectively, providing valuable data for your team.\u00a0When you build an AI agent around your own processes, you capture institutional knowledge that generic tools simply can&#8217;t replicate.<\/p>\n<p>With the monday AI Work Platform, this customization becomes even easier. You describe what you need\u00a0in plain language, and the platform creates specialized agents that work together as a team, each handling specific tasks while sharing information seamlessly.<\/p>\n<a class=\"cta-button blue-button\" aria-label=\"Get started with monday.com\" href=\"https:\/\/auth.monday.com\/users\/sign_up_new\" target=\"_blank\">Get started with monday.com<\/a>\n\n<\/div>\n<div class=\"text-block\" id=\"text-block-6\">\n<img width=\"1024\" height=\"567\" src=\"https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/agent20factory20screenshot204-1024x567.png\" class=\"attachment-large size-large\" alt=\"\" loading=\"lazy\" decoding=\"async\" srcset=\"https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/agent20factory20screenshot204-1024x567.png 1024w, https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/agent20factory20screenshot204-300x166.png 300w, https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/agent20factory20screenshot204-768x425.png 768w, https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/agent20factory20screenshot204.png 1241w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/>\n<\/div>\n<div class=\"text-block\" id=\"text-block-7\">\n<h2 class=\"h2 text-block__title\">Building AI agents without coding experience<\/h2>\n<p>You don&#8217;t need to be a programmer to create AI agents. Modern no-code platforms let you build powerful agents using visual interfaces and plain language instructions, with Gartner predicting that <a href=\"https:\/\/www.gartner.com\/en\/articles\/the-rise-of-business-technologists\" target=\"_blank\">70% of new enterprise applications will use no-code or low-code technologies<\/a> by 2026.<\/p>\n<p>No-code development works by abstracting away the technical complexity. Instead of writing code, you describe what you want your AI agent builder to do, connect it to your tools, and test it through user-friendly interfaces.<\/p>\n<p>The key features that make no-code AI development accessible include:<\/p>\n<ul>\n<li>\n<p><strong>Visual workflow designers:<\/strong> drag and drop logic blocks to create agent behaviors<\/p>\n<\/li>\n<li>\n<p><strong>Pre-built templates:<\/strong> start with <a href=\"https:\/\/monday.com\/blog\/project-management\/project-management-framework\/\" target=\"_blank\">proven frameworks<\/a> for common scenarios<\/p>\n<\/li>\n<li>\n<p><strong>Natural language setup:<\/strong> configure agents by describing goals in plain English<\/p>\n<\/li>\n<li>\n<p><strong>One-click integrations:<\/strong> connect to popular business tools without technical knowledge<\/p>\n<\/li>\n<\/ul>\n<p>Here&#8217;s how no-code compares to traditional development approaches:<\/p>\n<table style=\"min-width: 75px\">\n<colgroup>\n<col style=\"min-width: 25px\" \/>\n<col style=\"min-width: 25px\" \/>\n<col style=\"min-width: 25px\" \/><\/colgroup>\n<tbody>\n<tr>\n<th colspan=\"1\" rowspan=\"1\">\n<p>Aspect<\/p>\n<\/th>\n<th colspan=\"1\" rowspan=\"1\">\n<p>Traditional coding<\/p>\n<\/th>\n<th colspan=\"1\" rowspan=\"1\">\n<p>No-code platforms<\/p>\n<\/th>\n<\/tr>\n<tr>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Time to build<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Weeks to months<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Hours to days<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Skills needed<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Programming expertise<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Basic computer skills<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Maintenance<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Requires developer support<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Self-service updates<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Flexibility<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Complete control<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>High within platform capabilities<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>The real advantage? You can start small and iterate quickly. Build a simple agent today, test it with real work, and expand its capabilities as you learn what works best.\u00a0Many teams start with a single-purpose agent that handles a single task, such as sorting incoming requests or summarizing meeting notes, and then add complexity once they see results.<\/p>\n\n<\/div>\n<div class=\"text-block\" id=\"text-block-8\">\n<h2 class=\"h2 text-block__title\">Essential tools and platforms for creating AI agents<\/h2>\n<p>Choosing the right platform shapes your entire AI agent experience. The best choice depends on your technical skills, specific needs, and growth plans.<\/p>\n<p>Different platforms serve different purposes. Some prioritize simplicity; others prioritize power. Understanding these differences helps you pick the right starting point.<\/p>\n<h3>No-code AI agent builders<\/h3>\n<p>Platforms designed for non-technical users prioritize ease of use and quick results. They&#8217;re ideal for getting started without a steep learning curve.<\/p>\n<p>What makes a great no-code platform? Look for these essential features:<\/p>\n<ul>\n<li>\n<p><strong>Intuitive interface:<\/strong> visual builders that feel familiar, like creating a presentation<\/p>\n<\/li>\n<li>\n<p><strong>Rich template library:<\/strong> pre-built agents for common business scenarios you can customize<\/p>\n<\/li>\n<li>\n<p><strong>Easy integrations:<\/strong> connect to tools you already use with simple authentication<\/p>\n<\/li>\n<li>\n<p><strong>Testing environment:<\/strong> safe spaces to experiment before going live<\/p>\n<\/li>\n<\/ul>\n<p><a href=\"https:\/\/monday.com\/blog\/ai-agents\/best-ai-agent-platform\/\" target=\"_blank\">AI agent platforms<\/a> like the monday AI Work Platform are great for beginners because they treat AI agents as team members rather than isolated tools. You can create multiple specialized agents, one for scheduling, another for research, and a third for <a href=\"https:\/\/monday.com\/blog\/crm-and-sales\/customer-relationship\/\" target=\"_blank\">building customer relationships<\/a>, that coordinate naturally.<\/p>\n<h3>Choosing the right AI model for your agent<\/h3>\n<p>The AI model is the &#8220;brain&#8221; behind your agent. It determines how well the agent understands instructions, processes context, and generates responses. Choosing the right model early saves you time and money as your agent scales.<\/p>\n<p>You have two broad categories to consider. Hosted models like GPT-4o, Claude, and Gemini are managed by their providers. You access them through an API, and they handle all the infrastructure. Open-source models like Llama and Mistral give you more control but require technical setup to run and maintain.<\/p>\n<p>Several factors should guide your decision within any AI agent framework:<\/p>\n<ul>\n<li>\n<p><strong>Cost per token:<\/strong> hosted models charge based on usage. Open-source models have infrastructure costs instead<\/p>\n<\/li>\n<li>\n<p><strong>Context window size:<\/strong> larger context windows let agents process more information at once, which matters for document-heavy tasks<\/p>\n<\/li>\n<li>\n<p><strong>Speed:<\/strong> faster models improve the user experience, especially for customer-facing agents<\/p>\n<\/li>\n<li>\n<p><strong>Multimodal capabilities:<\/strong> some models handle text, images, and audio, which expand what your agent can do<\/p>\n<\/li>\n<li>\n<p><strong>Compliance requirements:<\/strong> regulated industries may need models that keep data within specific regions<\/p>\n<\/li>\n<\/ul>\n<p>If you&#8217;re a beginner, start with a hosted model through a no-code platform. This approach removes infrastructure complexity so you can focus on designing the agent&#8217;s behavior. You can explore open-source options later if you need more control or want to reduce per-query costs at scale.<\/p>\n<p>One practical tip: test your agent with the cheapest suitable model first. If performance meets your needs, you&#8217;ve saved money. If it falls short, move up to a more capable model with clear evidence of where the cheaper one failed. This incremental approach prevents overspending on model capabilities you don&#8217;t actually need.<\/p>\n<h3>Development environments for AI agents<\/h3>\n<p>Technical teams might prefer platforms with more customization options. These require programming knowledge but offer greater control over agent behavior.<\/p>\n<p>Popular\u00a0AI agent frameworks include LangChain, CrewAI, and AutoGen. These tools support multiple programming languages and advanced features\u00a0like multi-agent orchestration, custom memory management, and fine-grained control over model behavior. They&#8217;re most effective when you have specific requirements that no-code platforms can&#8217;t meet.<\/p>\n<p>When evaluating a development environment, consider language support (Python dominates the AI agent ecosystem), community size (larger communities mean more tutorials and troubleshooting help), and how well the framework handles the specific agent pattern you need, whether that&#8217;s a simple tool-calling agent, a retrieval-augmented generation (RAG) pipeline, or a full multi-agent system.<\/p>\n<h3>Testing and deployment tools<\/h3>\n<p>Every AI agent needs proper testing before handling real work. Testing tools help you verify agent behavior, catch edge cases, and ensure reliable performance.<\/p>\n<p>Good testing covers normal operations, unusual scenarios, and integration points. Deployment tools then help you roll out agents gradually while monitoring their performance.<\/p>\n<a class=\"cta-button blue-button\" aria-label=\"Get started with monday.com\" href=\"https:\/\/auth.monday.com\/users\/sign_up_new\" target=\"_blank\">Get started with monday.com<\/a>\n\n<\/div>\n<div class=\"text-block\" id=\"text-block-9\">\n<img width=\"906\" height=\"388\" src=\"https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/agent-factory-7.jpg\" class=\"attachment-large size-large\" alt=\"\" loading=\"lazy\" decoding=\"async\" srcset=\"https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/agent-factory-7.jpg 906w, https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/agent-factory-7-300x128.jpg 300w, https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/agent-factory-7-768x329.jpg 768w\" sizes=\"auto, (max-width: 906px) 100vw, 906px\" \/>\n<\/div>\n<div class=\"text-block\" id=\"text-block-10\">\n<h2 class=\"h2 text-block__title\">Five steps to create your first AI agent<\/h2>\n<p>Understanding how to build an AI agent becomes straightforward when you follow a proven process. Each step below builds on the previous one, creating a clear path from idea to working agent.<\/p>\n<h3>Step 1. Define your agent&#8217;s purpose<\/h3>\n<p>Start with a specific problem you want to solve. Vague goals lead to disappointing results. Clear, focused purposes create agents that deliver real value.\u00a0Knowing how to create an AI agent starts with defining exactly what it should do and for whom.<\/p>\n<p>Choose your first project based on these criteria:<\/p>\n<ul>\n<li>\n<p><strong>Single, clear objective:<\/strong> pick one task rather than trying to solve everything<\/p>\n<\/li>\n<li>\n<p><strong>Frequent occurrence:<\/strong> target activities you do multiple times per week<\/p>\n<\/li>\n<li>\n<p><strong>Measurable success:<\/strong> define exactly what &#8220;working well&#8221; looks like<\/p>\n<\/li>\n<li>\n<p><strong>Existing process:<\/strong> start with tasks you already do manually<\/p>\n<\/li>\n<\/ul>\n<p>Once you&#8217;ve picked your use case, answer these scoping questions before building anything:<\/p>\n<ul>\n<li>\n<p>Who is the agent serving? (a specific team, department, or customer segment)<\/p>\n<\/li>\n<li>\n<p>What inputs does it need? (emails, form submissions, database records)<\/p>\n<\/li>\n<li>\n<p>What data sources will it access? (knowledge bases, CRM, spreadsheets)<\/p>\n<\/li>\n<li>\n<p>What tools must it connect to? (calendars, email, project boards, chat platforms)<\/p>\n<\/li>\n<li>\n<p>What does success look like? (response time under two minutes, 90% accuracy, 50% fewer manual tasks)<\/p>\n<\/li>\n<\/ul>\n<p>Instead of &#8220;help with sales,&#8221; try &#8220;qualify <a href=\"https:\/\/monday.com\/blog\/marketing\/inbound-marketing-strategy\/\" target=\"_blank\">inbound leads<\/a> from our website contact form and schedule demos with interested prospects.&#8221; That level of specificity gives you a clear target to build toward and a concrete way to measure whether the agent is working.<\/p>\n<h3>Step 2. Select the right platform<\/h3>\n<p>Your platform choice affects everything from development speed to long-term capabilities. Match the platform to your immediate needs while considering future growth.<\/p>\n<p>Base your selection on these factors:<\/p>\n<ul>\n<li>\n<p><strong>Personal use:<\/strong> choose platforms with strong individual productivity features.<\/p>\n<\/li>\n<li>\n<p><strong>Team coordination:<\/strong> look for collaboration, permissions, and sharing capabilities.<\/p>\n<\/li>\n<li>\n<p><strong>Customer-facing needs:<\/strong> prioritize security, reliability, and compliance features.<\/p>\n<\/li>\n<li>\n<p><strong>Data processing:<\/strong> ensure robust integration and data handling capabilities.<\/p>\n<\/li>\n<\/ul>\n<h3>Step 3. Design your agent workflow<\/h3>\n<p>Map out how your agent will handle different scenarios. <a href=\"https:\/\/monday.com\/blog\/productivity\/workflow\/\" target=\"_blank\">Good workflow design<\/a> prevents confusion and ensures smooth operation.<\/p>\n<p>Document these key elements:<\/p>\n<ul>\n<li>\n<p><strong>Triggers:<\/strong> what starts your agent working<\/p>\n<\/li>\n<li>\n<p><strong>Decisions:<\/strong> where your agent chooses between different actions<\/p>\n<\/li>\n<li>\n<p><strong>Actions:<\/strong> specific tasks your agent performs<\/p>\n<\/li>\n<li>\n<p><strong>Handoffs:<\/strong> when to involve people<\/p>\n<\/li>\n<\/ul>\n<p>Start simple. Map the happy path first, then add branches for exceptions and errors.<\/p>\n<h3>Step 4. Test and train your AI agent<\/h3>\n<p>Testing separates agents that work in theory from those that work in practice. Thorough testing reveals issues before they impact real work.<\/p>\n<p>Your testing approach should cover:<\/p>\n<ul>\n<li>\n<p><strong>Basic functionality:<\/strong> does the agent do what it&#8217;s supposed to?<\/p>\n<\/li>\n<li>\n<p><strong>Edge cases:<\/strong> how does it handle unusual situations?<\/p>\n<\/li>\n<li>\n<p><strong>Performance:<\/strong> is it fast and reliable enough?<\/p>\n<\/li>\n<li>\n<p><strong>Integration points:<\/strong> do connections to other systems work properly?<\/p>\n<\/li>\n<\/ul>\n<p>Document what works and what doesn&#8217;t. Use these insights to refine your agent&#8217;s behavior.<\/p>\n<h3>Step 5. Deploy and monitor your AI agent<\/h3>\n<p>Launch your agent gradually to minimize risk and gather feedback. Smart deployment builds confidence while maintaining stability.<\/p>\n<p>Follow these deployment practices:<\/p>\n<ul>\n<li>\n<p><strong>Start small:<\/strong> begin with a limited scope or user group<\/p>\n<\/li>\n<li>\n<p><strong>Monitor closely:<\/strong> track performance and watch for issues<\/p>\n<\/li>\n<li>\n<p><strong>Communicate clearly:<\/strong> tell users what the agent does and how to use it<\/p>\n<\/li>\n<li>\n<p><strong>Plan for problems:<\/strong> know how to quickly disable or modify the agent<\/p>\n<\/li>\n<\/ul>\n<p>Once your agent is live, production monitoring becomes essential. Log every decision the agent makes so you can audit behavior when something goes wrong. Track success rates for key tasks, like how often the agent correctly routes tickets or qualifies leads. Set up alerts for failures, such as API timeouts, unexpected outputs, or tasks the agent can&#8217;t complete. And version-control your agent&#8217;s configuration so you can roll back to a known-good state if a change introduces problems.<\/p>\n<p>Real-world feedback will reveal gaps you can&#8217;t predict during testing. Treat deployment as the beginning of an ongoing improvement cycle, not the finish line. Set up a simple dashboard or weekly summary that shows task completion rates, error frequencies, and any tasks the agent escalated to people. This data drives your next round of improvements.<\/p>\n\n<\/div>\n<div class=\"text-block\" id=\"text-block-11\">\n<img width=\"1024\" height=\"469\" src=\"https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/agent20factory20screenshot206-1024x469.jpg\" class=\"attachment-large size-large\" alt=\"\" loading=\"lazy\" decoding=\"async\" srcset=\"https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/agent20factory20screenshot206-1024x469.jpg 1024w, https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/agent20factory20screenshot206-300x137.jpg 300w, https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/agent20factory20screenshot206-768x352.jpg 768w, https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/agent20factory20screenshot206.jpg 1319w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/>\n<\/div>\n<div class=\"text-block\" id=\"text-block-12\">\n<h2 class=\"h2 text-block__title\">How to add guardrails and safety to your AI agents<\/h2>\n<p>An agent that works correctly 95% of the time can still cause serious problems in the other 5%. Guardrails are the boundaries and checks that keep your agent operating safely, preventing hallucinated answers, data leaks, and unintended actions before they reach users or downstream systems.<\/p>\n<p>When you build an AI agent, plan for five types of guardrails:<\/p>\n<ul>\n<li>\n<p><strong>Input validation:<\/strong> filter and sanitize what the agent receives. Reject inputs that fall outside the agent&#8217;s scope, contain malicious prompts, or include sensitive data it shouldn&#8217;t process. This is your first line of defense against misuse<\/p>\n<\/li>\n<li>\n<p><strong>Output filtering:<\/strong> check agent responses before they reach users. Flag or block outputs that contain confidential information, off-topic content, or language that doesn&#8217;t match your brand standards<\/p>\n<\/li>\n<li>\n<p><strong>Scope constraints:<\/strong> limit what tools and data the agent can access. An agent that only answers support questions shouldn&#8217;t be able to modify billing records. Enforce the principle of least privilege<\/p>\n<\/li>\n<li>\n<p><strong>Human-in-the-loop checkpoints:<\/strong> require approval for high-stakes actions. Sending a refund, publishing content, or escalating a customer issue should involve a person until you&#8217;ve built enough trust in the agent&#8217;s judgment<\/p>\n<\/li>\n<li>\n<p><strong>Rate limiting and cost controls:<\/strong> prevent runaway API usage. Set daily or hourly caps on the number of actions or API calls the agent can make, so a single misconfigured trigger doesn&#8217;t generate unexpected costs<\/p>\n<\/li>\n<\/ul>\n<p>Start with strict guardrails and loosen them gradually as you build confidence. It&#8217;s much easier to relax a rule that&#8217;s working well than to recover from an agent that acted outside its boundaries.<\/p>\n<p>When designing guardrails, document each rule clearly so anyone on your team can understand why it exists. A guardrail like &#8220;require manager approval for refunds over $100&#8221; is specific and auditable. A guardrail like &#8220;be careful with money stuff&#8221; isn&#8217;t actionable. Treat guardrails as living rules that evolve with your agent&#8217;s responsibilities.<\/p>\n<p>The monday AI Work Platform includes built-in guardrails with full transparency into agent actions, granular permissions, and human-in-the-loop controls. These are ready to use without custom development.<\/p>\n\n<\/div>\n<div class=\"text-block\" id=\"text-block-13\">\n<h2 class=\"h2 text-block__title\">How to evaluate and improve your AI agents<\/h2>\n<p>Deploying an agent is just the starting point. Ongoing evaluation tells you whether the agent is actually achieving its purpose or quietly underperforming.<\/p>\n<p>Track these key metrics to understand how your agent is doing:<\/p>\n<ul>\n<li>\n<p><strong>Task completion rate:<\/strong> what percentage of assigned tasks does the agent finish successfully?<\/p>\n<\/li>\n<li>\n<p><strong>Accuracy:<\/strong> how often does it produce the correct result?<\/p>\n<\/li>\n<li>\n<p><strong>Response time:<\/strong> how quickly does it complete each task?<\/p>\n<\/li>\n<li>\n<p><strong>User satisfaction:<\/strong> are the people who interact with the agent&#8217;s output happy with the quality?<\/p>\n<\/li>\n<li>\n<p><strong>Cost per task:<\/strong> how much does each completed task cost in API calls, compute, and credits?<\/p>\n<\/li>\n<\/ul>\n<p>Use a simple improvement loop to get better results over time. Collect feedback from users and logs. Identify failure patterns, like specific question types the agent handles poorly. Adjust prompts, logic, or data sources to address those patterns. Re-test the agent with the same scenarios to confirm the fix works. Then redeploy and continue monitoring.<\/p>\n<p>A\/B testing is especially useful for agents that interact with customers. Run two versions of the same agent with slight differences in prompts or decision logic, then compare their performance on the metrics above. This approach removes guesswork from optimization. For example, you might test whether a customer support agent performs better when it asks a clarifying question upfront versus when it attempts to answer immediately.<\/p>\n<p>For cadence, review agent performance weekly during the first month after deployment. Once the agent is stable, shift to monthly reviews. Any time you change the agent&#8217;s scope, data sources, or connected tools, return to weekly reviews until things settle.<\/p>\n<p>Keep a simple log of every change you make and the metric impact it had. Over time, this log becomes a playbook for optimizing future agents. Teams that document their improvement process build better agents faster because they stop repeating the same mistakes.<\/p>\n\n<\/div>\n<div class=\"text-block\" id=\"text-block-14\">\n<h2 class=\"h2 text-block__title\">AI agent tutorials for common examples<\/h2>\n<p>AI agents become most valuable when applied to real, everyday workflows. The examples below show how simple, task-focused agents can be used in common business scenarios, starting with marketing, where repetition and data-heavy processes make automation especially effective.<\/p>\n<h3>Building AI agents for marketing tasks<\/h3>\n<p>Marketing teams benefit from AI agents because much of their work follows predictable patterns. Agents handle routine tasks while people focus on strategy and creativity.\u00a0A well-configured marketing agent can run repetitive campaigns around the clock without oversight.<\/p>\n<p>Common marketing applications include:<\/p>\n<ul>\n<li>\n<p><strong>Content distribution:<\/strong> post to multiple channels at optimal times<\/p>\n<\/li>\n<li>\n<p><strong>Lead scoring:<\/strong> evaluate prospects based on behavior and demographics<\/p>\n<\/li>\n<li>\n<p><strong>Performance tracking:<\/strong> <a href=\"https:\/\/monday.com\/blog\/project-management\/best-campaign-management-software-head-of-marketing-tech-cm\/\" target=\"_blank\">monitor campaigns<\/a> and generate reports<\/p>\n<\/li>\n<li>\n<p><strong>Message personalization:<\/strong> customize content based on user data<\/p>\n<\/li>\n<\/ul>\n<h3>Creating customer service agents<\/h3>\n<p><a href=\"https:\/\/monday.com\/blog\/ai-agents\/ai-agent-for-customer-service\/\" target=\"_blank\">Customer service agents<\/a> excel at handling routine inquiries while ensuring complex issues reach the right people. They improve response times and consistency, and they&#8217;re one of the most common starting points for teams learning to build AI agents.<\/p>\n<p>Effective <a href=\"https:\/\/monday.com\/blog\/service\/service-agent\/\" target=\"_blank\">service agents<\/a> handle:<\/p>\n<ul>\n<li>\n<p><strong>FAQ responses:<\/strong> answer common questions instantly<\/p>\n<\/li>\n<li>\n<p><strong>Ticket routing:<\/strong> direct inquiries to appropriate team members<\/p>\n<\/li>\n<li>\n<p><strong>Follow-up sequences:<\/strong> check if issues were resolved<\/p>\n<\/li>\n<li>\n<p><strong>Escalation management:<\/strong> recognize when someone needs help<\/p>\n<\/li>\n<\/ul>\n<h3>Personal productivity AI agents<\/h3>\n<p>Personal productivity agents handle the small tasks that interrupt your flow. They work best for repetitive activities that don&#8217;t require creative thinking.<\/p>\n<p>The monday AI Work Platform lets you build specialized agents for different aspects of your work:<\/p>\n<ul>\n<li>\n<p><strong>Calendar management:<\/strong> schedule meetings and protect focus time<\/p>\n<\/li>\n<li>\n<p><strong>Email processing:<\/strong> sort messages and draft routine responses<\/p>\n<\/li>\n<li>\n<p><strong>Task tracking:<\/strong> manage to-do lists and send reminders<\/p>\n<\/li>\n<li>\n<p><strong>Research assistance:<\/strong> gather information and create summaries<\/p>\n<\/li>\n<\/ul>\n<h3>Team collaboration agents<\/h3>\n<p>Collaboration agents reduce the overhead of coordinating work across multiple people. They&#8217;re especially valuable for distributed teams\u00a0where time zone differences and communication gaps slow handoffs.<\/p>\n<p>Key collaboration applications:<\/p>\n<ul>\n<li>\n<p><strong>Meeting coordination:<\/strong> find times that work for everyone<\/p>\n<\/li>\n<li>\n<p><strong>Status updates:<\/strong> automatically gather progress reports<\/p>\n<\/li>\n<li>\n<p><strong>Document management:<\/strong> organize and share resources<\/p>\n<\/li>\n<li>\n<p><strong>Communication routing:<\/strong> direct messages to the right people<\/p>\n<\/li>\n<\/ul>\n\n<\/div>\n<div class=\"text-block\" id=\"text-block-15\">\n<img width=\"1024\" height=\"398\" src=\"https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/agent20factory20features-briefing-screenshot201-2-1024x398.jpg\" class=\"attachment-large size-large\" alt=\"\" loading=\"lazy\" decoding=\"async\" srcset=\"https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/agent20factory20features-briefing-screenshot201-2-1024x398.jpg 1024w, https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/agent20factory20features-briefing-screenshot201-2-300x117.jpg 300w, https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/agent20factory20features-briefing-screenshot201-2-768x299.jpg 768w, https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/agent20factory20features-briefing-screenshot201-2-1536x598.jpg 1536w, https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/agent20factory20features-briefing-screenshot201-2.jpg 1879w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/>\n<\/div>\n<div class=\"text-block\" id=\"text-block-16\">\n<h2 class=\"h2 text-block__title\">How to build an AI agent team<\/h2>\n<p>Single agents handle individual tasks well. But when you&#8217;re learning how to build AI agents for complex workflows, you&#8217;ll often need multiple agents working together, each contributing specialized capabilities.<\/p>\n<h3>Connecting multiple AI agents<\/h3>\n<p>Agent teams need clear communication and coordination. Well-designed teams share information smoothly while maintaining distinct responsibilities.<\/p>\n<p>Effective coordination patterns include:<\/p>\n<ul>\n<li>\n<p><strong>Sequential processing:<\/strong> one agent completes work and passes it to the next<\/p>\n<\/li>\n<li>\n<p><strong>Parallel execution:<\/strong> multiple agents work on different parts simultaneously<\/p>\n<\/li>\n<li>\n<p><strong>Hierarchical structure:<\/strong> a coordinator agent manages specialized workers<\/p>\n<\/li>\n<li>\n<p><strong>Event-driven collaboration:<\/strong> agents respond to specific triggers<\/p>\n<\/li>\n<\/ul>\n<h3>Orchestrating agent workflows<\/h3>\n<p>Before diving into multi-agent orchestration, consider whether you actually need it. A single agent equipped with multiple tools is simpler to debug, has lower latency, and handles most beginner use cases well. Multi-agent systems shine when tasks are genuinely independent, require different specialized models, or involve workflows that are too complex for a single agent&#8217;s context window. If your first instinct is to build three agents, ask whether one agent with three tools would work just as well.<\/p>\n<p>When you need multiple agents, managing them requires systems that coordinate, prevent conflicts, and monitor overall performance.<\/p>\n<p>Key orchestration considerations:<\/p>\n<ul>\n<li>\n<p><strong>Communication standards:<\/strong> how agents share information<\/p>\n<\/li>\n<li>\n<p><strong>Conflict resolution:<\/strong> what happens when agents disagree<\/p>\n<\/li>\n<li>\n<p><strong>Performance tracking:<\/strong> monitoring the entire team&#8217;s effectiveness.<\/p>\n<\/li>\n<li>\n<p><strong>Resource allocation:<\/strong> ensuring agents don&#8217;t overwhelm systems<\/p>\n<\/li>\n<\/ul>\n<h3>Scaling from one agent to many<\/h3>\n<p>Growing your agent team works best when you add capabilities gradually. Start with two agents that have clearly different roles.<\/p>\n<p>Follow this scaling approach:<\/p>\n<ul>\n<li>\n<p><strong>Identify handoff points:<\/strong> where does work move between different types of tasks?<\/p>\n<\/li>\n<li>\n<p><strong>Design for clarity:<\/strong> give each agent distinct responsibilities<\/p>\n<\/li>\n<li>\n<p><strong>Plan information flow:<\/strong> map how data moves between agents<\/p>\n<\/li>\n<li>\n<p><strong>Monitor everything:<\/strong> track team performance, not just individual agents<\/p>\n<\/li>\n<\/ul>\n\n<\/div>\n<div class=\"text-block\" id=\"text-block-17\">\n<img width=\"1024\" height=\"486\" src=\"https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/Agent20Factory20Homepage20Screenshot-6-1024x486.png\" class=\"attachment-large size-large\" alt=\"\" loading=\"lazy\" decoding=\"async\" srcset=\"https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/Agent20Factory20Homepage20Screenshot-6-1024x486.png 1024w, https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/Agent20Factory20Homepage20Screenshot-6-300x142.png 300w, https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/Agent20Factory20Homepage20Screenshot-6-768x364.png 768w, https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/Agent20Factory20Homepage20Screenshot-6-1536x728.png 1536w, https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/07\/Agent20Factory20Homepage20Screenshot-6.png 1900w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/>\n<\/div>\n<div class=\"text-block\" id=\"text-block-18\">\n<h2 class=\"h2 text-block__title\">Build smarter workflows with monday AI Work Platform<\/h2>\n<p>The monday AI Work Platform brings <a href=\"https:\/\/monday.com\/blog\/ai-agents\/ai-agents-for-business\/\" target=\"_blank\">business AI agents<\/a> into a unified environment where they become practical teammates you can design, deploy, and adjust without technical overhead. Instead of configuring abstract tools, you build focused agents that slot directly into how work actually gets done.<\/p>\n<p><img alt=\"monday AI Work Platform homepage showing AI agent capabilities\" src=\"https:\/\/cdn.airops.com\/rails\/active_storage\/blobs\/proxy\/eyJfcmFpbHMiOnsiZGF0YSI6NDEyNjkxNTYyLCJwdXIiOiJibG9iX2lkIn19--6ef1f9cc1e0a779785c7ad927162162468eb291d\/monday-homepage-screenshot.png\" \/>The platform gives you several AI capabilities that work together as a connected AI agent builder:<\/p>\n<ul>\n<li>\n<p><strong>monday agents:<\/strong> build custom agents using a simple three-step process. Describe what you need, connect the knowledge and tools your agent requires, then test and refine it. You can also start with pre-built agents for common tasks like ticket assignment, lead scoring, meeting summarization, risk analysis, sentiment detection, vendor research, customer support, and process automation.\u00a0Every agent includes built-in guardrails, full transparency into actions, and granular permissions<\/p>\n<\/li>\n<li>\n<p><strong>monday vibe:<\/strong> an AI-powered no-code builder that turns natural language prompts into fully functional custom apps. Describe the tool you need, and vibe builds it for you, no coding required. It&#8217;s open to all monday.com users across all tiers<\/p>\n<\/li>\n<li>\n<p><strong>monday sidekick:<\/strong> a context-aware AI assistant embedded directly in your workspace. Sidekick understands your organizational data, workflows, and history, so it can generate content, analyze data, suggest next steps, and execute work within the platform<\/p>\n<\/li>\n<li>\n<p><strong>monday MCP:<\/strong> connect your monday.com workspace to external AI tools like Claude, ChatGPT, Copilot, and Gemini. Your data stays secure while you extend your agents&#8217; reach across your full tool stack<\/p>\n<\/li>\n<\/ul>\n<p>In practice, this might look like one agent preparing <a href=\"https:\/\/monday.com\/blog\/work-management\/meeting-agenda\/\" target=\"_blank\">meeting agendas<\/a> and follow-ups, another scoring leads based on fit and engagement signals, and a third triaging customer support tickets. Each agent has a clear role, and together they reduce the day-to-day friction that slows teams down.<\/p>\n<p>Because everything runs on a shared data layer, agents have full context across departments. A sales agent can see project timelines, a support agent can check delivery status, and an operations agent can factor in resource availability. This cross-departmental visibility is what separates a coordinated AI workforce from a collection of disconnected bots.<\/p>\n<p>The result is a capable digital workforce that handles repetitive work reliably, so you can stay focused on decisions, strategy, and progress.<\/p>\n<a class=\"cta-button blue-button\" aria-label=\"Get started with monday.com\" href=\"https:\/\/auth.monday.com\/users\/sign_up_new\" target=\"_blank\">Get started with monday.com<\/a>\n\n<\/div>\n<div class=\"text-block\" id=\"text-block-19\">\n<div class=\"accordion faq\" id=\"faq-frequently-asked-questions\">\n  <h2 class=\"accordion__heading section-title text-left\">Frequently asked questions<\/h2>\n    <div class=\"accordion__item\">\n    <a class=\"accordion__button d-block\" data-toggle=\"collapse\" data-parent=\"#faq-frequently-asked-questions\" href=\"#q-frequently-asked-questions-1\" aria-expanded=\"false\">\n      <h3 class=\"accordion__question\">How much does it cost to build an AI agent?        \n          \n        \n      <\/h3>\n    <\/a>\n    <div id=\"q-frequently-asked-questions-1\" class=\"accordion__answer collapse collapse--md\" data-parent=\"#faq-frequently-asked-questions\">\n      <p>The cost ranges from free for basic no-code platforms to $50- $ 200 per month for advanced features. Most beginners start with free tiers before upgrading based on usage. If you're learning how to build an AI agent for the first time, look for platforms with free plans so you can experiment without financial commitment.<\/p>\n    <\/div>\n  <\/div>\n    <div class=\"accordion__item\">\n    <a class=\"accordion__button d-block\" data-toggle=\"collapse\" data-parent=\"#faq-frequently-asked-questions\" href=\"#q-frequently-asked-questions-2\" aria-expanded=\"false\">\n      <h3 class=\"accordion__question\">What's the difference between AI agents and chatbots?        \n          \n        \n      <\/h3>\n    <\/a>\n    <div id=\"q-frequently-asked-questions-2\" class=\"accordion__answer collapse collapse--md\" data-parent=\"#faq-frequently-asked-questions\">\n      <p>AI agents can take actions and make decisions autonomously across multiple systems, while chatbots primarily respond to questions within a single conversation interface.\u00a0An agent might read an email, look up a customer record, update a database, and send a follow-up. A chatbot answers what you ask it, one question at a time.<\/p>\n    <\/div>\n  <\/div>\n    <div class=\"accordion__item\">\n    <a class=\"accordion__button d-block\" data-toggle=\"collapse\" data-parent=\"#faq-frequently-asked-questions\" href=\"#q-frequently-asked-questions-3\" aria-expanded=\"false\">\n      <h3 class=\"accordion__question\">How long does it take to create your first AI agent?        \n          \n        \n      <\/h3>\n    <\/a>\n    <div id=\"q-frequently-asked-questions-3\" class=\"accordion__answer collapse collapse--md\" data-parent=\"#faq-frequently-asked-questions\">\n      <p>Creating your first AI agent typically takes 30 minutes to two hours using no-code platforms, depending on complexity and the number of integrations required.\u00a0More advanced agents with custom logic and multiple data sources may take a few days to build and test properly.<\/p>\n    <\/div>\n  <\/div>\n    <div class=\"accordion__item\">\n    <a class=\"accordion__button d-block\" data-toggle=\"collapse\" data-parent=\"#faq-frequently-asked-questions\" href=\"#q-frequently-asked-questions-4\" aria-expanded=\"false\">\n      <h3 class=\"accordion__question\">Can I build AI agents without technical skills?        \n          \n        \n      <\/h3>\n    <\/a>\n    <div id=\"q-frequently-asked-questions-4\" class=\"accordion__answer collapse collapse--md\" data-parent=\"#faq-frequently-asked-questions\">\n      <p>You can build functional AI agents without any coding experience using no-code platforms that offer drag-and-drop interfaces and pre-built templates.\u00a0Platforms like monday.com let you describe what your agent should do in plain language, then handle the technical work behind the scenes.<\/p>\n    <\/div>\n  <\/div>\n    <div class=\"accordion__item\">\n    <a class=\"accordion__button d-block\" data-toggle=\"collapse\" data-parent=\"#faq-frequently-asked-questions\" href=\"#q-frequently-asked-questions-5\" aria-expanded=\"false\">\n      <h3 class=\"accordion__question\">Do AI agents need maintenance after deployment?        \n          \n        \n      <\/h3>\n    <\/a>\n    <div id=\"q-frequently-asked-questions-5\" class=\"accordion__answer collapse collapse--md\" data-parent=\"#faq-frequently-asked-questions\">\n      <p>AI agents require periodic performance reviews and occasional updates to handle new scenarios or integrate with additional systems.\u00a0Plan weekly reviews during the first month and monthly reviews thereafter. Any changes to connected tools or data sources may require adjustments to the agent's configuration.<\/p>\n    <\/div>\n  <\/div>\n    <div class=\"accordion__item\">\n    <a class=\"accordion__button d-block\" data-toggle=\"collapse\" data-parent=\"#faq-frequently-asked-questions\" href=\"#q-frequently-asked-questions-6\" aria-expanded=\"false\">\n      <h3 class=\"accordion__question\">What happens if an AI agent makes a mistake?        \n          \n        \n      <\/h3>\n    <\/a>\n    <div id=\"q-frequently-asked-questions-6\" class=\"accordion__answer collapse collapse--md\" data-parent=\"#faq-frequently-asked-questions\">\n      <p>AI agents can be configured with safety measures like approval requirements for important actions and automatic rollback capabilities to undo problematic changes.\u00a0Well-designed guardrails, including input validation and scope constraints, reduce the likelihood of mistakes reaching users.<\/p>\n    <\/div>\n  <\/div>\n    <div class=\"accordion__item\">\n    <a class=\"accordion__button d-block\" data-toggle=\"collapse\" data-parent=\"#faq-frequently-asked-questions\" href=\"#q-frequently-asked-questions-7\" aria-expanded=\"false\">\n      <h3 class=\"accordion__question\">How do you add guardrails to an AI agent?        \n          \n        \n      <\/h3>\n    <\/a>\n    <div id=\"q-frequently-asked-questions-7\" class=\"accordion__answer collapse collapse--md\" data-parent=\"#faq-frequently-asked-questions\">\n      <p>Guardrails protect your agent from acting outside its boundaries. Start with input validation to filter what the agent receives, output filtering to check responses before users see them, scope constraints to limit tool access, and human-in-the-loop checkpoints for high-stakes actions. When you build an AI agent, begin with strict guardrails and loosen them as trust grows.<\/p>\n    <\/div>\n  <\/div>\n    <div class=\"accordion__item\">\n    <a class=\"accordion__button d-block\" data-toggle=\"collapse\" data-parent=\"#faq-frequently-asked-questions\" href=\"#q-frequently-asked-questions-8\" aria-expanded=\"false\">\n      <h3 class=\"accordion__question\">Which AI model should I use to build an agent?        \n          \n        \n      <\/h3>\n    <\/a>\n    <div id=\"q-frequently-asked-questions-8\" class=\"accordion__answer collapse collapse--md\" data-parent=\"#faq-frequently-asked-questions\">\n      <p>Choose based on your use case and technical comfort. Hosted models like GPT-4o, Claude, and Gemini are easiest for beginners because they require no infrastructure setup. Open-source models like Llama and Mistral offer greater control but require technical expertise to deploy. Within any AI agent framework, consider cost per token, context window size, speed, and compliance requirements when selecting a model.<\/p>\n    <\/div>\n  <\/div>\n  {\n    \"@context\": \"https:\\\/\\\/schema.org\",\n    \"@type\": \"FAQPage\",\n    \"mainEntity\": [\n        {\n            \"@type\": \"Question\",\n            \"name\": \"How much does it cost to build an AI agent?\",\n            \"acceptedAnswer\": {\n                \"@type\": \"Answer\",\n                \"text\": \"<p>The cost ranges from free for basic no-code platforms to $50- $ 200 per month for advanced features. Most beginners start with free tiers before upgrading based on usage. If you're learning how to build an AI agent for the first time, look for platforms with free plans so you can experiment without financial commitment.\\n\"\n            }\n        },\n        {\n            \"@type\": \"Question\",\n            \"name\": \"What's the difference between AI agents and chatbots?\",\n            \"acceptedAnswer\": {\n                \"@type\": \"Answer\",\n                \"text\": \"<p>AI agents can take actions and make decisions autonomously across multiple systems, while chatbots primarily respond to questions within a single conversation interface.\\u00a0An agent might read an email, look up a customer record, update a database, and send a follow-up. A chatbot answers what you ask it, one question at a time.\\n\"\n            }\n        },\n        {\n            \"@type\": \"Question\",\n            \"name\": \"How long does it take to create your first AI agent?\",\n            \"acceptedAnswer\": {\n                \"@type\": \"Answer\",\n                \"text\": \"<p>Creating your first AI agent typically takes 30 minutes to two hours using no-code platforms, depending on complexity and the number of integrations required.\\u00a0More advanced agents with custom logic and multiple data sources may take a few days to build and test properly.\\n\"\n            }\n        },\n        {\n            \"@type\": \"Question\",\n            \"name\": \"Can I build AI agents without technical skills?\",\n            \"acceptedAnswer\": {\n                \"@type\": \"Answer\",\n                \"text\": \"<p>You can build functional AI agents without any coding experience using no-code platforms that offer drag-and-drop interfaces and pre-built templates.\\u00a0Platforms like monday.com let you describe what your agent should do in plain language, then handle the technical work behind the scenes.\\n\"\n            }\n        },\n        {\n            \"@type\": \"Question\",\n            \"name\": \"Do AI agents need maintenance after deployment?\",\n            \"acceptedAnswer\": {\n                \"@type\": \"Answer\",\n                \"text\": \"<p>AI agents require periodic performance reviews and occasional updates to handle new scenarios or integrate with additional systems.\\u00a0Plan weekly reviews during the first month and monthly reviews thereafter. Any changes to connected tools or data sources may require adjustments to the agent's configuration.\\n\"\n            }\n        },\n        {\n            \"@type\": \"Question\",\n            \"name\": \"What happens if an AI agent makes a mistake?\",\n            \"acceptedAnswer\": {\n                \"@type\": \"Answer\",\n                \"text\": \"<p>AI agents can be configured with safety measures like approval requirements for important actions and automatic rollback capabilities to undo problematic changes.\\u00a0Well-designed guardrails, including input validation and scope constraints, reduce the likelihood of mistakes reaching users.\\n\"\n            }\n        },\n        {\n            \"@type\": \"Question\",\n            \"name\": \"How do you add guardrails to an AI agent?\",\n            \"acceptedAnswer\": {\n                \"@type\": \"Answer\",\n                \"text\": \"<p>Guardrails protect your agent from acting outside its boundaries. Start with input validation to filter what the agent receives, output filtering to check responses before users see them, scope constraints to limit tool access, and human-in-the-loop checkpoints for high-stakes actions. When you build an AI agent, begin with strict guardrails and loosen them as trust grows.\\n\"\n            }\n        },\n        {\n            \"@type\": \"Question\",\n            \"name\": \"Which AI model should I use to build an agent?\",\n            \"acceptedAnswer\": {\n                \"@type\": \"Answer\",\n                \"text\": \"<p>Choose based on your use case and technical comfort. Hosted models like GPT-4o, Claude, and Gemini are easiest for beginners because they require no infrastructure setup. Open-source models like Llama and Mistral offer greater control but require technical expertise to deploy. Within any AI agent framework, consider cost per token, context window size, speed, and compliance requirements when selecting a model.\\n\"\n            }\n        }\n    ]\n}<\/div>\n\n\n<\/div>","protected":false},"excerpt":{"rendered":"","protected":false},"author":262,"featured_media":287483,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"pages\/cornerstone-primary.php","format":"standard","meta":{"_acf_changed":false,"_yoast_wpseo_title":"","_yoast_wpseo_metadesc":"","monday_item_id":11238274062,"monday_board_id":0,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[14080],"tags":[],"class_list":["post-287481","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-agents"],"acf":{"sections":[{"acf_fc_layout":"content_1","blocks":[{"main_heading":"Key takeaways","content_block":[{"acf_fc_layout":"text","content":"<ul>\n<li>\n<p><strong>Start with a single use case:<\/strong> beginners get the best results by automating one clear, repetitive task before expanding to more complex agent workflows<\/p>\n<\/li>\n<li>\n<p><strong>No-code lowers the barrier:<\/strong> modern platforms let anyone build an AI agent using plain language and visual tools, without technical skills<\/p>\n<\/li>\n<li>\n<p><strong>Custom agents beat generic tools:<\/strong> building your own agents gives you tighter control over data access, behavior, and integrations with existing systems<\/p>\n<\/li>\n<li>\n<p><strong>Guardrails and safety matter:<\/strong> input validation, output filtering, and human-in-the-loop checkpoints keep your agents reliable and trustworthy from day one<\/p>\n<\/li>\n<li>\n<p><strong>Team-based agents scale better:<\/strong> the monday AI Work Platform makes it easier to create multiple focused agents that work together across everyday workflows<\/p>\n<\/li>\n<\/ul>\n<a class=\"cta-button blue-button\" aria-label=\"Get started with monday.com\" href=\"https:\/\/auth.monday.com\/users\/sign_up_new\" target=\"_blank\">Get started with monday.com<\/a>\n"}]},{"main_heading":"","content_block":[{"acf_fc_layout":"image","image_type":"normal","image":351102,"image_link":null}]},{"main_heading":"What are AI agents and how do they work?","content_block":[{"acf_fc_layout":"text","content":"<p>AI agents are software programs that can complete tasks and make decisions without constant oversight, and the market is projected to <a href=\"https:\/\/www.grandviewresearch.com\/industry-analysis\/ai-agents-market-report\" target=\"_blank\">reach USD 50.31 billion<\/a> by 2030. They use artificial intelligence to understand your needs, take action, and learn from the results.<\/p>\n<p>Think of these agents as smart assistants that don&#8217;t just wait for commands. Instead, they actively work toward <a href=\"https:\/\/monday.com\/blog\/project-management\/goal-setting-template\/\" target=\"_blank\">goals you set<\/a>, make choices based on context, and handle complex workflows from start to finish.<\/p>\n<p>Every AI agent follows a core operating cycle. First, the agent perceives inputs from its environment, like an incoming email, a database update, or a user message. Then it reasons about the best course of action by weighing options against its goals and instructions. Next, it acts by executing a task, sending a response, or updating a system. Finally, it learns from the outcome and uses that feedback to improve future decisions.<\/p>\n<p>To understand how agents operate autonomously, it&#8217;s important to know their core components. These work together to process information, make decisions, and take action on your behalf:<\/p>\n<ul>\n<li>\n<p><strong>Language understanding:<\/strong> AI agents process natural language, so you can communicate with them as you would with a colleague<\/p>\n<\/li>\n<li>\n<p><strong>Memory systems:<\/strong> they remember past interactions and use that context to make smarter decisions<\/p>\n<\/li>\n<li>\n<p><strong>Integration capabilities:<\/strong> agents connect to your existing tools, such as calendars, email, and databases, to take real action<\/p>\n<\/li>\n<li>\n<p><strong>Decision logic:<\/strong> they evaluate options and choose the best path forward based on your goals<\/p>\n<\/li>\n<\/ul>\n<p>For\u00a0more context, imagine you build an AI agent to manage customer inquiries. It reads incoming messages, understands the customer&#8217;s need, checks your knowledge base for answers, and either responds directly or routes the inquiry to the right team member,\u00a0all without you lifting a finger.<\/p>\n"}]},{"main_heading":"","content_block":[{"acf_fc_layout":"image","image_type":"normal","image":351110,"image_link":null}]},{"main_heading":"Why you need to build your own AI agents","content_block":[{"acf_fc_layout":"text","content":"<p>Generic AI platforms work well for common processes. However, your business has unique processes, specific requirements, and proprietary information that off-the-shelf solutions can&#8217;t handle properly.<\/p>\n<p>Building custom AI agents gives you control over exactly how they work. You decide what data they access, how they make decisions, and which systems they connect to.<\/p>\n<p>The business case for custom AI agents comes down to several key advantages:<\/p>\n<ul>\n<li>\n<p><strong>Perfect fit for your workflows:<\/strong> design agents that match your exact processes instead of changing how you work<\/p>\n<\/li>\n<li>\n<p><strong>Complete data control:<\/strong> keep sensitive information within your systems rather than sending it to third-party services<\/p>\n<\/li>\n<li>\n<p><strong>Seamless integration:<\/strong> connect directly to your existing tools without compatibility headaches<\/p>\n<\/li>\n<li>\n<p><strong>Long-term cost savings:<\/strong> eliminate monthly subscriptions for multiple <a href=\"https:\/\/monday.com\/blog\/project-management\/top-ai-platforms\/\" target=\"_blank\">AI platforms<\/a><\/p>\n<\/li>\n<li>\n<p><strong>Competitive edge:<\/strong> create capabilities your competitors can&#8217;t buy or copy<\/p>\n<\/li>\n<\/ul>\n<p>Consider a <a href=\"https:\/\/monday.com\/blog\/project-management\/real-estate-ai\/\" target=\"_blank\">real estate<\/a> team that needs to qualify leads, schedule property viewings, and follow up with prospects. A generic chatbot might handle basic questions, but it can&#8217;t manage the entire workflow.<\/p>\n<p>A custom agent, however, can access your property database, check agent calendars, and send personalized follow-ups based on viewing history. It can even track which approaches convert most effectively, providing valuable data for your team.\u00a0When you build an AI agent around your own processes, you capture institutional knowledge that generic tools simply can&#8217;t replicate.<\/p>\n<p>With the monday AI Work Platform, this customization becomes even easier. You describe what you need\u00a0in plain language, and the platform creates specialized agents that work together as a team, each handling specific tasks while sharing information seamlessly.<\/p>\n<a class=\"cta-button blue-button\" aria-label=\"Get started with monday.com\" href=\"https:\/\/auth.monday.com\/users\/sign_up_new\" target=\"_blank\">Get started with monday.com<\/a>\n"}]},{"main_heading":"","content_block":[{"acf_fc_layout":"image","image_type":"normal","image":351118,"image_link":null}]},{"main_heading":"Building AI agents without coding experience","content_block":[{"acf_fc_layout":"text","content":"<p>You don&#8217;t need to be a programmer to create AI agents. Modern no-code platforms let you build powerful agents using visual interfaces and plain language instructions, with Gartner predicting that <a href=\"https:\/\/www.gartner.com\/en\/articles\/the-rise-of-business-technologists\" target=\"_blank\">70% of new enterprise applications will use no-code or low-code technologies<\/a> by 2026.<\/p>\n<p>No-code development works by abstracting away the technical complexity. Instead of writing code, you describe what you want your AI agent builder to do, connect it to your tools, and test it through user-friendly interfaces.<\/p>\n<p>The key features that make no-code AI development accessible include:<\/p>\n<ul>\n<li>\n<p><strong>Visual workflow designers:<\/strong> drag and drop logic blocks to create agent behaviors<\/p>\n<\/li>\n<li>\n<p><strong>Pre-built templates:<\/strong> start with <a href=\"https:\/\/monday.com\/blog\/project-management\/project-management-framework\/\" target=\"_blank\">proven frameworks<\/a> for common scenarios<\/p>\n<\/li>\n<li>\n<p><strong>Natural language setup:<\/strong> configure agents by describing goals in plain English<\/p>\n<\/li>\n<li>\n<p><strong>One-click integrations:<\/strong> connect to popular business tools without technical knowledge<\/p>\n<\/li>\n<\/ul>\n<p>Here&#8217;s how no-code compares to traditional development approaches:<\/p>\n<table style=\"min-width: 75px;\">\n<colgroup>\n<col style=\"min-width: 25px;\"\/>\n<col style=\"min-width: 25px;\"\/>\n<col style=\"min-width: 25px;\"\/><\/colgroup>\n<tbody>\n<tr>\n<th colspan=\"1\" rowspan=\"1\">\n<p>Aspect<\/p>\n<\/th>\n<th colspan=\"1\" rowspan=\"1\">\n<p>Traditional coding<\/p>\n<\/th>\n<th colspan=\"1\" rowspan=\"1\">\n<p>No-code platforms<\/p>\n<\/th>\n<\/tr>\n<tr>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Time to build<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Weeks to months<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Hours to days<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Skills needed<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Programming expertise<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Basic computer skills<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Maintenance<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Requires developer support<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Self-service updates<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Flexibility<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Complete control<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>High within platform capabilities<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>The real advantage? You can start small and iterate quickly. Build a simple agent today, test it with real work, and expand its capabilities as you learn what works best.\u00a0Many teams start with a single-purpose agent that handles a single task, such as sorting incoming requests or summarizing meeting notes, and then add complexity once they see results.<\/p>\n"}]},{"main_heading":"Essential tools and platforms for creating AI agents","content_block":[{"acf_fc_layout":"text","content":"<p>Choosing the right platform shapes your entire AI agent experience. The best choice depends on your technical skills, specific needs, and growth plans.<\/p>\n<p>Different platforms serve different purposes. Some prioritize simplicity; others prioritize power. Understanding these differences helps you pick the right starting point.<\/p>\n<h3>No-code AI agent builders<\/h3>\n<p>Platforms designed for non-technical users prioritize ease of use and quick results. They&#8217;re ideal for getting started without a steep learning curve.<\/p>\n<p>What makes a great no-code platform? Look for these essential features:<\/p>\n<ul>\n<li>\n<p><strong>Intuitive interface:<\/strong> visual builders that feel familiar, like creating a presentation<\/p>\n<\/li>\n<li>\n<p><strong>Rich template library:<\/strong> pre-built agents for common business scenarios you can customize<\/p>\n<\/li>\n<li>\n<p><strong>Easy integrations:<\/strong> connect to tools you already use with simple authentication<\/p>\n<\/li>\n<li>\n<p><strong>Testing environment:<\/strong> safe spaces to experiment before going live<\/p>\n<\/li>\n<\/ul>\n<p><a href=\"https:\/\/monday.com\/blog\/ai-agents\/best-ai-agent-platform\/\" target=\"_blank\">AI agent platforms<\/a> like the monday AI Work Platform are great for beginners because they treat AI agents as team members rather than isolated tools. You can create multiple specialized agents, one for scheduling, another for research, and a third for <a href=\"https:\/\/monday.com\/blog\/crm-and-sales\/customer-relationship\/\" target=\"_blank\">building customer relationships<\/a>, that coordinate naturally.<\/p>\n<h3>Choosing the right AI model for your agent<\/h3>\n<p>The AI model is the &#8220;brain&#8221; behind your agent. It determines how well the agent understands instructions, processes context, and generates responses. Choosing the right model early saves you time and money as your agent scales.<\/p>\n<p>You have two broad categories to consider. Hosted models like GPT-4o, Claude, and Gemini are managed by their providers. You access them through an API, and they handle all the infrastructure. Open-source models like Llama and Mistral give you more control but require technical setup to run and maintain.<\/p>\n<p>Several factors should guide your decision within any AI agent framework:<\/p>\n<ul>\n<li>\n<p><strong>Cost per token:<\/strong> hosted models charge based on usage. Open-source models have infrastructure costs instead<\/p>\n<\/li>\n<li>\n<p><strong>Context window size:<\/strong> larger context windows let agents process more information at once, which matters for document-heavy tasks<\/p>\n<\/li>\n<li>\n<p><strong>Speed:<\/strong> faster models improve the user experience, especially for customer-facing agents<\/p>\n<\/li>\n<li>\n<p><strong>Multimodal capabilities:<\/strong> some models handle text, images, and audio, which expand what your agent can do<\/p>\n<\/li>\n<li>\n<p><strong>Compliance requirements:<\/strong> regulated industries may need models that keep data within specific regions<\/p>\n<\/li>\n<\/ul>\n<p>If you&#8217;re a beginner, start with a hosted model through a no-code platform. This approach removes infrastructure complexity so you can focus on designing the agent&#8217;s behavior. You can explore open-source options later if you need more control or want to reduce per-query costs at scale.<\/p>\n<p>One practical tip: test your agent with the cheapest suitable model first. If performance meets your needs, you&#8217;ve saved money. If it falls short, move up to a more capable model with clear evidence of where the cheaper one failed. This incremental approach prevents overspending on model capabilities you don&#8217;t actually need.<\/p>\n<h3>Development environments for AI agents<\/h3>\n<p>Technical teams might prefer platforms with more customization options. These require programming knowledge but offer greater control over agent behavior.<\/p>\n<p>Popular\u00a0AI agent frameworks include LangChain, CrewAI, and AutoGen. These tools support multiple programming languages and advanced features\u00a0like multi-agent orchestration, custom memory management, and fine-grained control over model behavior. They&#8217;re most effective when you have specific requirements that no-code platforms can&#8217;t meet.<\/p>\n<p>When evaluating a development environment, consider language support (Python dominates the AI agent ecosystem), community size (larger communities mean more tutorials and troubleshooting help), and how well the framework handles the specific agent pattern you need, whether that&#8217;s a simple tool-calling agent, a retrieval-augmented generation (RAG) pipeline, or a full multi-agent system.<\/p>\n<h3>Testing and deployment tools<\/h3>\n<p>Every AI agent needs proper testing before handling real work. Testing tools help you verify agent behavior, catch edge cases, and ensure reliable performance.<\/p>\n<p>Good testing covers normal operations, unusual scenarios, and integration points. Deployment tools then help you roll out agents gradually while monitoring their performance.<\/p>\n<a class=\"cta-button blue-button\" aria-label=\"Get started with monday.com\" href=\"https:\/\/auth.monday.com\/users\/sign_up_new\" target=\"_blank\">Get started with monday.com<\/a>\n"}]},{"main_heading":"","content_block":[{"acf_fc_layout":"image","image_type":"normal","image":351126,"image_link":null}]},{"main_heading":"Five steps to create your first AI agent","content_block":[{"acf_fc_layout":"text","content":"<p>Understanding how to build an AI agent becomes straightforward when you follow a proven process. Each step below builds on the previous one, creating a clear path from idea to working agent.<\/p>\n<h3>Step 1. Define your agent&#8217;s purpose<\/h3>\n<p>Start with a specific problem you want to solve. Vague goals lead to disappointing results. Clear, focused purposes create agents that deliver real value.\u00a0Knowing how to create an AI agent starts with defining exactly what it should do and for whom.<\/p>\n<p>Choose your first project based on these criteria:<\/p>\n<ul>\n<li>\n<p><strong>Single, clear objective:<\/strong> pick one task rather than trying to solve everything<\/p>\n<\/li>\n<li>\n<p><strong>Frequent occurrence:<\/strong> target activities you do multiple times per week<\/p>\n<\/li>\n<li>\n<p><strong>Measurable success:<\/strong> define exactly what &#8220;working well&#8221; looks like<\/p>\n<\/li>\n<li>\n<p><strong>Existing process:<\/strong> start with tasks you already do manually<\/p>\n<\/li>\n<\/ul>\n<p>Once you&#8217;ve picked your use case, answer these scoping questions before building anything:<\/p>\n<ul>\n<li>\n<p>Who is the agent serving? (a specific team, department, or customer segment)<\/p>\n<\/li>\n<li>\n<p>What inputs does it need? (emails, form submissions, database records)<\/p>\n<\/li>\n<li>\n<p>What data sources will it access? (knowledge bases, CRM, spreadsheets)<\/p>\n<\/li>\n<li>\n<p>What tools must it connect to? (calendars, email, project boards, chat platforms)<\/p>\n<\/li>\n<li>\n<p>What does success look like? (response time under two minutes, 90% accuracy, 50% fewer manual tasks)<\/p>\n<\/li>\n<\/ul>\n<p>Instead of &#8220;help with sales,&#8221; try &#8220;qualify <a href=\"https:\/\/monday.com\/blog\/marketing\/inbound-marketing-strategy\/\" target=\"_blank\">inbound leads<\/a> from our website contact form and schedule demos with interested prospects.&#8221; That level of specificity gives you a clear target to build toward and a concrete way to measure whether the agent is working.<\/p>\n<h3>Step 2. Select the right platform<\/h3>\n<p>Your platform choice affects everything from development speed to long-term capabilities. Match the platform to your immediate needs while considering future growth.<\/p>\n<p>Base your selection on these factors:<\/p>\n<ul>\n<li>\n<p><strong>Personal use:<\/strong> choose platforms with strong individual productivity features.<\/p>\n<\/li>\n<li>\n<p><strong>Team coordination:<\/strong> look for collaboration, permissions, and sharing capabilities.<\/p>\n<\/li>\n<li>\n<p><strong>Customer-facing needs:<\/strong> prioritize security, reliability, and compliance features.<\/p>\n<\/li>\n<li>\n<p><strong>Data processing:<\/strong> ensure robust integration and data handling capabilities.<\/p>\n<\/li>\n<\/ul>\n<h3>Step 3. Design your agent workflow<\/h3>\n<p>Map out how your agent will handle different scenarios. <a href=\"https:\/\/monday.com\/blog\/productivity\/workflow\/\" target=\"_blank\">Good workflow design<\/a> prevents confusion and ensures smooth operation.<\/p>\n<p>Document these key elements:<\/p>\n<ul>\n<li>\n<p><strong>Triggers:<\/strong> what starts your agent working<\/p>\n<\/li>\n<li>\n<p><strong>Decisions:<\/strong> where your agent chooses between different actions<\/p>\n<\/li>\n<li>\n<p><strong>Actions:<\/strong> specific tasks your agent performs<\/p>\n<\/li>\n<li>\n<p><strong>Handoffs:<\/strong> when to involve people<\/p>\n<\/li>\n<\/ul>\n<p>Start simple. Map the happy path first, then add branches for exceptions and errors.<\/p>\n<h3>Step 4. Test and train your AI agent<\/h3>\n<p>Testing separates agents that work in theory from those that work in practice. Thorough testing reveals issues before they impact real work.<\/p>\n<p>Your testing approach should cover:<\/p>\n<ul>\n<li>\n<p><strong>Basic functionality:<\/strong> does the agent do what it&#8217;s supposed to?<\/p>\n<\/li>\n<li>\n<p><strong>Edge cases:<\/strong> how does it handle unusual situations?<\/p>\n<\/li>\n<li>\n<p><strong>Performance:<\/strong> is it fast and reliable enough?<\/p>\n<\/li>\n<li>\n<p><strong>Integration points:<\/strong> do connections to other systems work properly?<\/p>\n<\/li>\n<\/ul>\n<p>Document what works and what doesn&#8217;t. Use these insights to refine your agent&#8217;s behavior.<\/p>\n<h3>Step 5. Deploy and monitor your AI agent<\/h3>\n<p>Launch your agent gradually to minimize risk and gather feedback. Smart deployment builds confidence while maintaining stability.<\/p>\n<p>Follow these deployment practices:<\/p>\n<ul>\n<li>\n<p><strong>Start small:<\/strong> begin with a limited scope or user group<\/p>\n<\/li>\n<li>\n<p><strong>Monitor closely:<\/strong> track performance and watch for issues<\/p>\n<\/li>\n<li>\n<p><strong>Communicate clearly:<\/strong> tell users what the agent does and how to use it<\/p>\n<\/li>\n<li>\n<p><strong>Plan for problems:<\/strong> know how to quickly disable or modify the agent<\/p>\n<\/li>\n<\/ul>\n<p>Once your agent is live, production monitoring becomes essential. Log every decision the agent makes so you can audit behavior when something goes wrong. Track success rates for key tasks, like how often the agent correctly routes tickets or qualifies leads. Set up alerts for failures, such as API timeouts, unexpected outputs, or tasks the agent can&#8217;t complete. And version-control your agent&#8217;s configuration so you can roll back to a known-good state if a change introduces problems.<\/p>\n<p>Real-world feedback will reveal gaps you can&#8217;t predict during testing. Treat deployment as the beginning of an ongoing improvement cycle, not the finish line. Set up a simple dashboard or weekly summary that shows task completion rates, error frequencies, and any tasks the agent escalated to people. This data drives your next round of improvements.<\/p>\n"}]},{"main_heading":"","content_block":[{"acf_fc_layout":"image","image_type":"normal","image":351134,"image_link":null}]},{"main_heading":"How to add guardrails and safety to your AI agents","content_block":[{"acf_fc_layout":"text","content":"<p>An agent that works correctly 95% of the time can still cause serious problems in the other 5%. Guardrails are the boundaries and checks that keep your agent operating safely, preventing hallucinated answers, data leaks, and unintended actions before they reach users or downstream systems.<\/p>\n<p>When you build an AI agent, plan for five types of guardrails:<\/p>\n<ul>\n<li>\n<p><strong>Input validation:<\/strong> filter and sanitize what the agent receives. Reject inputs that fall outside the agent&#8217;s scope, contain malicious prompts, or include sensitive data it shouldn&#8217;t process. This is your first line of defense against misuse<\/p>\n<\/li>\n<li>\n<p><strong>Output filtering:<\/strong> check agent responses before they reach users. Flag or block outputs that contain confidential information, off-topic content, or language that doesn&#8217;t match your brand standards<\/p>\n<\/li>\n<li>\n<p><strong>Scope constraints:<\/strong> limit what tools and data the agent can access. An agent that only answers support questions shouldn&#8217;t be able to modify billing records. Enforce the principle of least privilege<\/p>\n<\/li>\n<li>\n<p><strong>Human-in-the-loop checkpoints:<\/strong> require approval for high-stakes actions. Sending a refund, publishing content, or escalating a customer issue should involve a person until you&#8217;ve built enough trust in the agent&#8217;s judgment<\/p>\n<\/li>\n<li>\n<p><strong>Rate limiting and cost controls:<\/strong> prevent runaway API usage. Set daily or hourly caps on the number of actions or API calls the agent can make, so a single misconfigured trigger doesn&#8217;t generate unexpected costs<\/p>\n<\/li>\n<\/ul>\n<p>Start with strict guardrails and loosen them gradually as you build confidence. It&#8217;s much easier to relax a rule that&#8217;s working well than to recover from an agent that acted outside its boundaries.<\/p>\n<p>When designing guardrails, document each rule clearly so anyone on your team can understand why it exists. A guardrail like &#8220;require manager approval for refunds over $100&#8221; is specific and auditable. A guardrail like &#8220;be careful with money stuff&#8221; isn&#8217;t actionable. Treat guardrails as living rules that evolve with your agent&#8217;s responsibilities.<\/p>\n<p>The monday AI Work Platform includes built-in guardrails with full transparency into agent actions, granular permissions, and human-in-the-loop controls. These are ready to use without custom development.<\/p>\n"}]},{"main_heading":"How to evaluate and improve your AI agents","content_block":[{"acf_fc_layout":"text","content":"<p>Deploying an agent is just the starting point. Ongoing evaluation tells you whether the agent is actually achieving its purpose or quietly underperforming.<\/p>\n<p>Track these key metrics to understand how your agent is doing:<\/p>\n<ul>\n<li>\n<p><strong>Task completion rate:<\/strong> what percentage of assigned tasks does the agent finish successfully?<\/p>\n<\/li>\n<li>\n<p><strong>Accuracy:<\/strong> how often does it produce the correct result?<\/p>\n<\/li>\n<li>\n<p><strong>Response time:<\/strong> how quickly does it complete each task?<\/p>\n<\/li>\n<li>\n<p><strong>User satisfaction:<\/strong> are the people who interact with the agent&#8217;s output happy with the quality?<\/p>\n<\/li>\n<li>\n<p><strong>Cost per task:<\/strong> how much does each completed task cost in API calls, compute, and credits?<\/p>\n<\/li>\n<\/ul>\n<p>Use a simple improvement loop to get better results over time. Collect feedback from users and logs. Identify failure patterns, like specific question types the agent handles poorly. Adjust prompts, logic, or data sources to address those patterns. Re-test the agent with the same scenarios to confirm the fix works. Then redeploy and continue monitoring.<\/p>\n<p>A\/B testing is especially useful for agents that interact with customers. Run two versions of the same agent with slight differences in prompts or decision logic, then compare their performance on the metrics above. This approach removes guesswork from optimization. For example, you might test whether a customer support agent performs better when it asks a clarifying question upfront versus when it attempts to answer immediately.<\/p>\n<p>For cadence, review agent performance weekly during the first month after deployment. Once the agent is stable, shift to monthly reviews. Any time you change the agent&#8217;s scope, data sources, or connected tools, return to weekly reviews until things settle.<\/p>\n<p>Keep a simple log of every change you make and the metric impact it had. Over time, this log becomes a playbook for optimizing future agents. Teams that document their improvement process build better agents faster because they stop repeating the same mistakes.<\/p>\n"}]},{"main_heading":"AI agent tutorials for common examples","content_block":[{"acf_fc_layout":"text","content":"<p>AI agents become most valuable when applied to real, everyday workflows. The examples below show how simple, task-focused agents can be used in common business scenarios, starting with marketing, where repetition and data-heavy processes make automation especially effective.<\/p>\n<h3>Building AI agents for marketing tasks<\/h3>\n<p>Marketing teams benefit from AI agents because much of their work follows predictable patterns. Agents handle routine tasks while people focus on strategy and creativity.\u00a0A well-configured marketing agent can run repetitive campaigns around the clock without oversight.<\/p>\n<p>Common marketing applications include:<\/p>\n<ul>\n<li>\n<p><strong>Content distribution:<\/strong> post to multiple channels at optimal times<\/p>\n<\/li>\n<li>\n<p><strong>Lead scoring:<\/strong> evaluate prospects based on behavior and demographics<\/p>\n<\/li>\n<li>\n<p><strong>Performance tracking:<\/strong> <a href=\"https:\/\/monday.com\/blog\/project-management\/best-campaign-management-software-head-of-marketing-tech-cm\/\" target=\"_blank\">monitor campaigns<\/a> and generate reports<\/p>\n<\/li>\n<li>\n<p><strong>Message personalization:<\/strong> customize content based on user data<\/p>\n<\/li>\n<\/ul>\n<h3>Creating customer service agents<\/h3>\n<p><a href=\"https:\/\/monday.com\/blog\/ai-agents\/ai-agent-for-customer-service\/\" target=\"_blank\">Customer service agents<\/a> excel at handling routine inquiries while ensuring complex issues reach the right people. They improve response times and consistency, and they&#8217;re one of the most common starting points for teams learning to build AI agents.<\/p>\n<p>Effective <a href=\"https:\/\/monday.com\/blog\/service\/service-agent\/\" target=\"_blank\">service agents<\/a> handle:<\/p>\n<ul>\n<li>\n<p><strong>FAQ responses:<\/strong> answer common questions instantly<\/p>\n<\/li>\n<li>\n<p><strong>Ticket routing:<\/strong> direct inquiries to appropriate team members<\/p>\n<\/li>\n<li>\n<p><strong>Follow-up sequences:<\/strong> check if issues were resolved<\/p>\n<\/li>\n<li>\n<p><strong>Escalation management:<\/strong> recognize when someone needs help<\/p>\n<\/li>\n<\/ul>\n<h3>Personal productivity AI agents<\/h3>\n<p>Personal productivity agents handle the small tasks that interrupt your flow. They work best for repetitive activities that don&#8217;t require creative thinking.<\/p>\n<p>The monday AI Work Platform lets you build specialized agents for different aspects of your work:<\/p>\n<ul>\n<li>\n<p><strong>Calendar management:<\/strong> schedule meetings and protect focus time<\/p>\n<\/li>\n<li>\n<p><strong>Email processing:<\/strong> sort messages and draft routine responses<\/p>\n<\/li>\n<li>\n<p><strong>Task tracking:<\/strong> manage to-do lists and send reminders<\/p>\n<\/li>\n<li>\n<p><strong>Research assistance:<\/strong> gather information and create summaries<\/p>\n<\/li>\n<\/ul>\n<h3>Team collaboration agents<\/h3>\n<p>Collaboration agents reduce the overhead of coordinating work across multiple people. They&#8217;re especially valuable for distributed teams\u00a0where time zone differences and communication gaps slow handoffs.<\/p>\n<p>Key collaboration applications:<\/p>\n<ul>\n<li>\n<p><strong>Meeting coordination:<\/strong> find times that work for everyone<\/p>\n<\/li>\n<li>\n<p><strong>Status updates:<\/strong> automatically gather progress reports<\/p>\n<\/li>\n<li>\n<p><strong>Document management:<\/strong> organize and share resources<\/p>\n<\/li>\n<li>\n<p><strong>Communication routing:<\/strong> direct messages to the right people<\/p>\n<\/li>\n<\/ul>\n"}]},{"main_heading":"","content_block":[{"acf_fc_layout":"image","image_type":"normal","image":351142,"image_link":null}]},{"main_heading":"How to build an AI agent team","content_block":[{"acf_fc_layout":"text","content":"<p>Single agents handle individual tasks well. But when you&#8217;re learning how to build AI agents for complex workflows, you&#8217;ll often need multiple agents working together, each contributing specialized capabilities.<\/p>\n<h3>Connecting multiple AI agents<\/h3>\n<p>Agent teams need clear communication and coordination. Well-designed teams share information smoothly while maintaining distinct responsibilities.<\/p>\n<p>Effective coordination patterns include:<\/p>\n<ul>\n<li>\n<p><strong>Sequential processing:<\/strong> one agent completes work and passes it to the next<\/p>\n<\/li>\n<li>\n<p><strong>Parallel execution:<\/strong> multiple agents work on different parts simultaneously<\/p>\n<\/li>\n<li>\n<p><strong>Hierarchical structure:<\/strong> a coordinator agent manages specialized workers<\/p>\n<\/li>\n<li>\n<p><strong>Event-driven collaboration:<\/strong> agents respond to specific triggers<\/p>\n<\/li>\n<\/ul>\n<h3>Orchestrating agent workflows<\/h3>\n<p>Before diving into multi-agent orchestration, consider whether you actually need it. A single agent equipped with multiple tools is simpler to debug, has lower latency, and handles most beginner use cases well. Multi-agent systems shine when tasks are genuinely independent, require different specialized models, or involve workflows that are too complex for a single agent&#8217;s context window. If your first instinct is to build three agents, ask whether one agent with three tools would work just as well.<\/p>\n<p>When you need multiple agents, managing them requires systems that coordinate, prevent conflicts, and monitor overall performance.<\/p>\n<p>Key orchestration considerations:<\/p>\n<ul>\n<li>\n<p><strong>Communication standards:<\/strong> how agents share information<\/p>\n<\/li>\n<li>\n<p><strong>Conflict resolution:<\/strong> what happens when agents disagree<\/p>\n<\/li>\n<li>\n<p><strong>Performance tracking:<\/strong> monitoring the entire team&#8217;s effectiveness.<\/p>\n<\/li>\n<li>\n<p><strong>Resource allocation:<\/strong> ensuring agents don&#8217;t overwhelm systems<\/p>\n<\/li>\n<\/ul>\n<h3>Scaling from one agent to many<\/h3>\n<p>Growing your agent team works best when you add capabilities gradually. Start with two agents that have clearly different roles.<\/p>\n<p>Follow this scaling approach:<\/p>\n<ul>\n<li>\n<p><strong>Identify handoff points:<\/strong> where does work move between different types of tasks?<\/p>\n<\/li>\n<li>\n<p><strong>Design for clarity:<\/strong> give each agent distinct responsibilities<\/p>\n<\/li>\n<li>\n<p><strong>Plan information flow:<\/strong> map how data moves between agents<\/p>\n<\/li>\n<li>\n<p><strong>Monitor everything:<\/strong> track team performance, not just individual agents<\/p>\n<\/li>\n<\/ul>\n"}]},{"main_heading":"","content_block":[{"acf_fc_layout":"image","image_type":"normal","image":351150,"image_link":null}]},{"main_heading":"Build smarter workflows with monday AI Work Platform","content_block":[{"acf_fc_layout":"text","content":"<p>The monday AI Work Platform brings <a href=\"https:\/\/monday.com\/blog\/ai-agents\/ai-agents-for-business\/\" target=\"_blank\">business AI agents<\/a> into a unified environment where they become practical teammates you can design, deploy, and adjust without technical overhead. Instead of configuring abstract tools, you build focused agents that slot directly into how work actually gets done.<\/p>\n<p><img decoding=\"async\" alt=\"monday AI Work Platform homepage showing AI agent capabilities\" src=\"https:\/\/cdn.airops.com\/rails\/active_storage\/blobs\/proxy\/eyJfcmFpbHMiOnsiZGF0YSI6NDEyNjkxNTYyLCJwdXIiOiJibG9iX2lkIn19--6ef1f9cc1e0a779785c7ad927162162468eb291d\/monday-homepage-screenshot.png\"\/>The platform gives you several AI capabilities that work together as a connected AI agent builder:<\/p>\n<ul>\n<li>\n<p><strong>monday agents:<\/strong> build custom agents using a simple three-step process. Describe what you need, connect the knowledge and tools your agent requires, then test and refine it. You can also start with pre-built agents for common tasks like ticket assignment, lead scoring, meeting summarization, risk analysis, sentiment detection, vendor research, customer support, and process automation.\u00a0Every agent includes built-in guardrails, full transparency into actions, and granular permissions<\/p>\n<\/li>\n<li>\n<p><strong>monday vibe:<\/strong> an AI-powered no-code builder that turns natural language prompts into fully functional custom apps. Describe the tool you need, and vibe builds it for you, no coding required. It&#8217;s open to all monday.com users across all tiers<\/p>\n<\/li>\n<li>\n<p><strong>monday sidekick:<\/strong> a context-aware AI assistant embedded directly in your workspace. Sidekick understands your organizational data, workflows, and history, so it can generate content, analyze data, suggest next steps, and execute work within the platform<\/p>\n<\/li>\n<li>\n<p><strong>monday MCP:<\/strong> connect your monday.com workspace to external AI tools like Claude, ChatGPT, Copilot, and Gemini. Your data stays secure while you extend your agents&#8217; reach across your full tool stack<\/p>\n<\/li>\n<\/ul>\n<p>In practice, this might look like one agent preparing <a href=\"https:\/\/monday.com\/blog\/work-management\/meeting-agenda\/\" target=\"_blank\">meeting agendas<\/a> and follow-ups, another scoring leads based on fit and engagement signals, and a third triaging customer support tickets. Each agent has a clear role, and together they reduce the day-to-day friction that slows teams down.<\/p>\n<p>Because everything runs on a shared data layer, agents have full context across departments. A sales agent can see project timelines, a support agent can check delivery status, and an operations agent can factor in resource availability. This cross-departmental visibility is what separates a coordinated AI workforce from a collection of disconnected bots.<\/p>\n<p>The result is a capable digital workforce that handles repetitive work reliably, so you can stay focused on decisions, strategy, and progress.<\/p>\n<a class=\"cta-button blue-button\" aria-label=\"Get started with monday.com\" href=\"https:\/\/auth.monday.com\/users\/sign_up_new\" target=\"_blank\">Get started with monday.com<\/a>\n"}]},{"main_heading":"","content_block":[{"acf_fc_layout":"text","content":"<div class=\"accordion faq\" id=\"faq-frequently-asked-questions\">\n  <h2 class=\"accordion__heading section-title text-left\">Frequently asked questions<\/h2>\n    <div class=\"accordion__item\">\n    <a class=\"accordion__button d-block\" data-toggle=\"collapse\" data-parent=\"#faq-frequently-asked-questions\" href=\"#q-frequently-asked-questions-1\"\n      aria-expanded=\"false\">\n      <h3 class=\"accordion__question\">How much does it cost to build an AI agent?        <svg class=\"angle-arrow angle-arrow--down\" width=\"32\" height=\"32\" viewBox=\"0 0 32 32\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n          <path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M16.5303 20.8839C16.2374 21.1768 15.7626 21.1768 15.4697 20.8839L7.82318 13.2374C7.53029 12.9445 7.53029 12.4697 7.82318 12.1768L8.17674 11.8232C8.46963 11.5303 8.9445 11.5303 9.2374 11.8232L16 18.5858L22.7626 11.8232C23.0555 11.5303 23.5303 11.5303 23.8232 11.8232L24.1768 12.1768C24.4697 12.4697 24.4697 12.9445 24.1768 13.2374L16.5303 20.8839Z\" fill=\"black\"\/>\n        <\/svg>\n      <\/h3>\n    <\/a>\n    <div id=\"q-frequently-asked-questions-1\" class=\"accordion__answer collapse collapse--md\" data-parent=\"#faq-frequently-asked-questions\">\n      <p>The cost ranges from free for basic no-code platforms to $50- $ 200 per month for advanced features. Most beginners start with free tiers before upgrading based on usage. If you're learning how to build an AI agent for the first time, look for platforms with free plans so you can experiment without financial commitment.<\/p>\n    <\/div>\n  <\/div>\n    <div class=\"accordion__item\">\n    <a class=\"accordion__button d-block\" data-toggle=\"collapse\" data-parent=\"#faq-frequently-asked-questions\" href=\"#q-frequently-asked-questions-2\"\n      aria-expanded=\"false\">\n      <h3 class=\"accordion__question\">What's the difference between AI agents and chatbots?        <svg class=\"angle-arrow angle-arrow--down\" width=\"32\" height=\"32\" viewBox=\"0 0 32 32\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n          <path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M16.5303 20.8839C16.2374 21.1768 15.7626 21.1768 15.4697 20.8839L7.82318 13.2374C7.53029 12.9445 7.53029 12.4697 7.82318 12.1768L8.17674 11.8232C8.46963 11.5303 8.9445 11.5303 9.2374 11.8232L16 18.5858L22.7626 11.8232C23.0555 11.5303 23.5303 11.5303 23.8232 11.8232L24.1768 12.1768C24.4697 12.4697 24.4697 12.9445 24.1768 13.2374L16.5303 20.8839Z\" fill=\"black\"\/>\n        <\/svg>\n      <\/h3>\n    <\/a>\n    <div id=\"q-frequently-asked-questions-2\" class=\"accordion__answer collapse collapse--md\" data-parent=\"#faq-frequently-asked-questions\">\n      <p>AI agents can take actions and make decisions autonomously across multiple systems, while chatbots primarily respond to questions within a single conversation interface.\u00a0An agent might read an email, look up a customer record, update a database, and send a follow-up. A chatbot answers what you ask it, one question at a time.<\/p>\n    <\/div>\n  <\/div>\n    <div class=\"accordion__item\">\n    <a class=\"accordion__button d-block\" data-toggle=\"collapse\" data-parent=\"#faq-frequently-asked-questions\" href=\"#q-frequently-asked-questions-3\"\n      aria-expanded=\"false\">\n      <h3 class=\"accordion__question\">How long does it take to create your first AI agent?        <svg class=\"angle-arrow angle-arrow--down\" width=\"32\" height=\"32\" viewBox=\"0 0 32 32\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n          <path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M16.5303 20.8839C16.2374 21.1768 15.7626 21.1768 15.4697 20.8839L7.82318 13.2374C7.53029 12.9445 7.53029 12.4697 7.82318 12.1768L8.17674 11.8232C8.46963 11.5303 8.9445 11.5303 9.2374 11.8232L16 18.5858L22.7626 11.8232C23.0555 11.5303 23.5303 11.5303 23.8232 11.8232L24.1768 12.1768C24.4697 12.4697 24.4697 12.9445 24.1768 13.2374L16.5303 20.8839Z\" fill=\"black\"\/>\n        <\/svg>\n      <\/h3>\n    <\/a>\n    <div id=\"q-frequently-asked-questions-3\" class=\"accordion__answer collapse collapse--md\" data-parent=\"#faq-frequently-asked-questions\">\n      <p>Creating your first AI agent typically takes 30 minutes to two hours using no-code platforms, depending on complexity and the number of integrations required.\u00a0More advanced agents with custom logic and multiple data sources may take a few days to build and test properly.<\/p>\n    <\/div>\n  <\/div>\n    <div class=\"accordion__item\">\n    <a class=\"accordion__button d-block\" data-toggle=\"collapse\" data-parent=\"#faq-frequently-asked-questions\" href=\"#q-frequently-asked-questions-4\"\n      aria-expanded=\"false\">\n      <h3 class=\"accordion__question\">Can I build AI agents without technical skills?        <svg class=\"angle-arrow angle-arrow--down\" width=\"32\" height=\"32\" viewBox=\"0 0 32 32\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n          <path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M16.5303 20.8839C16.2374 21.1768 15.7626 21.1768 15.4697 20.8839L7.82318 13.2374C7.53029 12.9445 7.53029 12.4697 7.82318 12.1768L8.17674 11.8232C8.46963 11.5303 8.9445 11.5303 9.2374 11.8232L16 18.5858L22.7626 11.8232C23.0555 11.5303 23.5303 11.5303 23.8232 11.8232L24.1768 12.1768C24.4697 12.4697 24.4697 12.9445 24.1768 13.2374L16.5303 20.8839Z\" fill=\"black\"\/>\n        <\/svg>\n      <\/h3>\n    <\/a>\n    <div id=\"q-frequently-asked-questions-4\" class=\"accordion__answer collapse collapse--md\" data-parent=\"#faq-frequently-asked-questions\">\n      <p>You can build functional AI agents without any coding experience using no-code platforms that offer drag-and-drop interfaces and pre-built templates.\u00a0Platforms like monday.com let you describe what your agent should do in plain language, then handle the technical work behind the scenes.<\/p>\n    <\/div>\n  <\/div>\n    <div class=\"accordion__item\">\n    <a class=\"accordion__button d-block\" data-toggle=\"collapse\" data-parent=\"#faq-frequently-asked-questions\" href=\"#q-frequently-asked-questions-5\"\n      aria-expanded=\"false\">\n      <h3 class=\"accordion__question\">Do AI agents need maintenance after deployment?        <svg class=\"angle-arrow angle-arrow--down\" width=\"32\" height=\"32\" viewBox=\"0 0 32 32\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n          <path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M16.5303 20.8839C16.2374 21.1768 15.7626 21.1768 15.4697 20.8839L7.82318 13.2374C7.53029 12.9445 7.53029 12.4697 7.82318 12.1768L8.17674 11.8232C8.46963 11.5303 8.9445 11.5303 9.2374 11.8232L16 18.5858L22.7626 11.8232C23.0555 11.5303 23.5303 11.5303 23.8232 11.8232L24.1768 12.1768C24.4697 12.4697 24.4697 12.9445 24.1768 13.2374L16.5303 20.8839Z\" fill=\"black\"\/>\n        <\/svg>\n      <\/h3>\n    <\/a>\n    <div id=\"q-frequently-asked-questions-5\" class=\"accordion__answer collapse collapse--md\" data-parent=\"#faq-frequently-asked-questions\">\n      <p>AI agents require periodic performance reviews and occasional updates to handle new scenarios or integrate with additional systems.\u00a0Plan weekly reviews during the first month and monthly reviews thereafter. Any changes to connected tools or data sources may require adjustments to the agent's configuration.<\/p>\n    <\/div>\n  <\/div>\n    <div class=\"accordion__item\">\n    <a class=\"accordion__button d-block\" data-toggle=\"collapse\" data-parent=\"#faq-frequently-asked-questions\" href=\"#q-frequently-asked-questions-6\"\n      aria-expanded=\"false\">\n      <h3 class=\"accordion__question\">What happens if an AI agent makes a mistake?        <svg class=\"angle-arrow angle-arrow--down\" width=\"32\" height=\"32\" viewBox=\"0 0 32 32\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n          <path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M16.5303 20.8839C16.2374 21.1768 15.7626 21.1768 15.4697 20.8839L7.82318 13.2374C7.53029 12.9445 7.53029 12.4697 7.82318 12.1768L8.17674 11.8232C8.46963 11.5303 8.9445 11.5303 9.2374 11.8232L16 18.5858L22.7626 11.8232C23.0555 11.5303 23.5303 11.5303 23.8232 11.8232L24.1768 12.1768C24.4697 12.4697 24.4697 12.9445 24.1768 13.2374L16.5303 20.8839Z\" fill=\"black\"\/>\n        <\/svg>\n      <\/h3>\n    <\/a>\n    <div id=\"q-frequently-asked-questions-6\" class=\"accordion__answer collapse collapse--md\" data-parent=\"#faq-frequently-asked-questions\">\n      <p>AI agents can be configured with safety measures like approval requirements for important actions and automatic rollback capabilities to undo problematic changes.\u00a0Well-designed guardrails, including input validation and scope constraints, reduce the likelihood of mistakes reaching users.<\/p>\n    <\/div>\n  <\/div>\n    <div class=\"accordion__item\">\n    <a class=\"accordion__button d-block\" data-toggle=\"collapse\" data-parent=\"#faq-frequently-asked-questions\" href=\"#q-frequently-asked-questions-7\"\n      aria-expanded=\"false\">\n      <h3 class=\"accordion__question\">How do you add guardrails to an AI agent?        <svg class=\"angle-arrow angle-arrow--down\" width=\"32\" height=\"32\" viewBox=\"0 0 32 32\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n          <path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M16.5303 20.8839C16.2374 21.1768 15.7626 21.1768 15.4697 20.8839L7.82318 13.2374C7.53029 12.9445 7.53029 12.4697 7.82318 12.1768L8.17674 11.8232C8.46963 11.5303 8.9445 11.5303 9.2374 11.8232L16 18.5858L22.7626 11.8232C23.0555 11.5303 23.5303 11.5303 23.8232 11.8232L24.1768 12.1768C24.4697 12.4697 24.4697 12.9445 24.1768 13.2374L16.5303 20.8839Z\" fill=\"black\"\/>\n        <\/svg>\n      <\/h3>\n    <\/a>\n    <div id=\"q-frequently-asked-questions-7\" class=\"accordion__answer collapse collapse--md\" data-parent=\"#faq-frequently-asked-questions\">\n      <p>Guardrails protect your agent from acting outside its boundaries. Start with input validation to filter what the agent receives, output filtering to check responses before users see them, scope constraints to limit tool access, and human-in-the-loop checkpoints for high-stakes actions. When you build an AI agent, begin with strict guardrails and loosen them as trust grows.<\/p>\n    <\/div>\n  <\/div>\n    <div class=\"accordion__item\">\n    <a class=\"accordion__button d-block\" data-toggle=\"collapse\" data-parent=\"#faq-frequently-asked-questions\" href=\"#q-frequently-asked-questions-8\"\n      aria-expanded=\"false\">\n      <h3 class=\"accordion__question\">Which AI model should I use to build an agent?        <svg class=\"angle-arrow angle-arrow--down\" width=\"32\" height=\"32\" viewBox=\"0 0 32 32\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n          <path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M16.5303 20.8839C16.2374 21.1768 15.7626 21.1768 15.4697 20.8839L7.82318 13.2374C7.53029 12.9445 7.53029 12.4697 7.82318 12.1768L8.17674 11.8232C8.46963 11.5303 8.9445 11.5303 9.2374 11.8232L16 18.5858L22.7626 11.8232C23.0555 11.5303 23.5303 11.5303 23.8232 11.8232L24.1768 12.1768C24.4697 12.4697 24.4697 12.9445 24.1768 13.2374L16.5303 20.8839Z\" fill=\"black\"\/>\n        <\/svg>\n      <\/h3>\n    <\/a>\n    <div id=\"q-frequently-asked-questions-8\" class=\"accordion__answer collapse collapse--md\" data-parent=\"#faq-frequently-asked-questions\">\n      <p>Choose based on your use case and technical comfort. Hosted models like GPT-4o, Claude, and Gemini are easiest for beginners because they require no infrastructure setup. Open-source models like Llama and Mistral offer greater control but require technical expertise to deploy. Within any AI agent framework, consider cost per token, context window size, speed, and compliance requirements when selecting a model.<\/p>\n    <\/div>\n  <\/div>\n  <script type='application\/ld+json'>{\n    \"@context\": \"https:\\\/\\\/schema.org\",\n    \"@type\": \"FAQPage\",\n    \"mainEntity\": [\n        {\n            \"@type\": \"Question\",\n            \"name\": \"How much does it cost to build an AI agent?\",\n            \"acceptedAnswer\": {\n                \"@type\": \"Answer\",\n                \"text\": \"<p>The cost ranges from free for basic no-code platforms to $50- $ 200 per month for advanced features. Most beginners start with free tiers before upgrading based on usage. If you're learning how to build an AI agent for the first time, look for platforms with free plans so you can experiment without financial commitment.<\\\/p>\\n\"\n            }\n        },\n        {\n            \"@type\": \"Question\",\n            \"name\": \"What's the difference between AI agents and chatbots?\",\n            \"acceptedAnswer\": {\n                \"@type\": \"Answer\",\n                \"text\": \"<p>AI agents can take actions and make decisions autonomously across multiple systems, while chatbots primarily respond to questions within a single conversation interface.\\u00a0An agent might read an email, look up a customer record, update a database, and send a follow-up. A chatbot answers what you ask it, one question at a time.<\\\/p>\\n\"\n            }\n        },\n        {\n            \"@type\": \"Question\",\n            \"name\": \"How long does it take to create your first AI agent?\",\n            \"acceptedAnswer\": {\n                \"@type\": \"Answer\",\n                \"text\": \"<p>Creating your first AI agent typically takes 30 minutes to two hours using no-code platforms, depending on complexity and the number of integrations required.\\u00a0More advanced agents with custom logic and multiple data sources may take a few days to build and test properly.<\\\/p>\\n\"\n            }\n        },\n        {\n            \"@type\": \"Question\",\n            \"name\": \"Can I build AI agents without technical skills?\",\n            \"acceptedAnswer\": {\n                \"@type\": \"Answer\",\n                \"text\": \"<p>You can build functional AI agents without any coding experience using no-code platforms that offer drag-and-drop interfaces and pre-built templates.\\u00a0Platforms like monday.com let you describe what your agent should do in plain language, then handle the technical work behind the scenes.<\\\/p>\\n\"\n            }\n        },\n        {\n            \"@type\": \"Question\",\n            \"name\": \"Do AI agents need maintenance after deployment?\",\n            \"acceptedAnswer\": {\n                \"@type\": \"Answer\",\n                \"text\": \"<p>AI agents require periodic performance reviews and occasional updates to handle new scenarios or integrate with additional systems.\\u00a0Plan weekly reviews during the first month and monthly reviews thereafter. Any changes to connected tools or data sources may require adjustments to the agent's configuration.<\\\/p>\\n\"\n            }\n        },\n        {\n            \"@type\": \"Question\",\n            \"name\": \"What happens if an AI agent makes a mistake?\",\n            \"acceptedAnswer\": {\n                \"@type\": \"Answer\",\n                \"text\": \"<p>AI agents can be configured with safety measures like approval requirements for important actions and automatic rollback capabilities to undo problematic changes.\\u00a0Well-designed guardrails, including input validation and scope constraints, reduce the likelihood of mistakes reaching users.<\\\/p>\\n\"\n            }\n        },\n        {\n            \"@type\": \"Question\",\n            \"name\": \"How do you add guardrails to an AI agent?\",\n            \"acceptedAnswer\": {\n                \"@type\": \"Answer\",\n                \"text\": \"<p>Guardrails protect your agent from acting outside its boundaries. Start with input validation to filter what the agent receives, output filtering to check responses before users see them, scope constraints to limit tool access, and human-in-the-loop checkpoints for high-stakes actions. When you build an AI agent, begin with strict guardrails and loosen them as trust grows.<\\\/p>\\n\"\n            }\n        },\n        {\n            \"@type\": \"Question\",\n            \"name\": \"Which AI model should I use to build an agent?\",\n            \"acceptedAnswer\": {\n                \"@type\": \"Answer\",\n                \"text\": \"<p>Choose based on your use case and technical comfort. Hosted models like GPT-4o, Claude, and Gemini are easiest for beginners because they require no infrastructure setup. Open-source models like Llama and Mistral offer greater control but require technical expertise to deploy. Within any AI agent framework, consider cost per token, context window size, speed, and compliance requirements when selecting a model.<\\\/p>\\n\"\n            }\n        }\n    ]\n}<\/script><\/div>\n\n"}]}]}],"faqs":[{"faq_title":"Frequently asked questions","faq_shortcode":"frequently-asked-questions","faq":[{"question":"How much does it cost to build an AI agent?","answer":"<p>The cost ranges from free for basic no-code platforms to $50- $ 200 per month for advanced features. Most beginners start with free tiers before upgrading based on usage. If you're learning how to build an AI agent for the first time, look for platforms with free plans so you can experiment without financial commitment.<\/p>\n"},{"question":"What's the difference between AI agents and chatbots?","answer":"<p>AI agents can take actions and make decisions autonomously across multiple systems, while chatbots primarily respond to questions within a single conversation interface.\u00a0An agent might read an email, look up a customer record, update a database, and send a follow-up. A chatbot answers what you ask it, one question at a time.<\/p>\n"},{"question":"How long does it take to create your first AI agent?","answer":"<p>Creating your first AI agent typically takes 30 minutes to two hours using no-code platforms, depending on complexity and the number of integrations required.\u00a0More advanced agents with custom logic and multiple data sources may take a few days to build and test properly.<\/p>\n"},{"question":"Can I build AI agents without technical skills?","answer":"<p>You can build functional AI agents without any coding experience using no-code platforms that offer drag-and-drop interfaces and pre-built templates.\u00a0Platforms like monday.com let you describe what your agent should do in plain language, then handle the technical work behind the scenes.<\/p>\n"},{"question":"Do AI agents need maintenance after deployment?","answer":"<p>AI agents require periodic performance reviews and occasional updates to handle new scenarios or integrate with additional systems.\u00a0Plan weekly reviews during the first month and monthly reviews thereafter. Any changes to connected tools or data sources may require adjustments to the agent's configuration.<\/p>\n"},{"question":"What happens if an AI agent makes a mistake?","answer":"<p>AI agents can be configured with safety measures like approval requirements for important actions and automatic rollback capabilities to undo problematic changes.\u00a0Well-designed guardrails, including input validation and scope constraints, reduce the likelihood of mistakes reaching users.<\/p>\n"},{"question":"How do you add guardrails to an AI agent?","answer":"<p>Guardrails protect your agent from acting outside its boundaries. Start with input validation to filter what the agent receives, output filtering to check responses before users see them, scope constraints to limit tool access, and human-in-the-loop checkpoints for high-stakes actions. When you build an AI agent, begin with strict guardrails and loosen them as trust grows.<\/p>\n"},{"question":"Which AI model should I use to build an agent?","answer":"<p>Choose based on your use case and technical comfort. Hosted models like GPT-4o, Claude, and Gemini are easiest for beginners because they require no infrastructure setup. Open-source models like Llama and Mistral offer greater control but require technical expertise to deploy. Within any AI agent framework, consider cost per token, context window size, speed, and compliance requirements when selecting a model.<\/p>\n"}]}],"show_sidebar_sticky_banner":false,"parse_from_google_doc":false,"lobby_image":false,"post_thumbnail_title":"","hide_post_info":false,"hide_bottom_cta":false,"hide_from_blog":false,"landing_page_layout":false,"hide_time_to_read":false,"sidebar_color_banner":"","custom_tags":false,"cornerstone_hero_cta_override":{"label":"","url":""},"menu_cta_override":{"label":"","url":""},"show_contact_sales_button":"default","override_contact_sales_label":"","override_contact_sales_url":"","cluster":"","display_dates":"default","featured_image_link":"","activate_cta_banner":false,"banner_url":"","main_text_banner":"","sub_title_banner":"","sub_title_banner_second":"","banner_button_text":"","below_banner_line":"","custom_header_banner":false,"use_customized_cta":false,"custom_schema_code":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.6 (Yoast SEO v27.5) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>How to build an AI agent for beginners in 2026 | monday.com Blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/monday.com\/blog\/ai-agents\/how-to-build-ai-agents-for-beginners\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How to build an AI agent for beginners in 2026\" \/>\n<meta property=\"og:url\" content=\"https:\/\/monday.com\/blog\/ai-agents\/how-to-build-ai-agents-for-beginners\/\" \/>\n<meta property=\"og:site_name\" content=\"monday.com Blog\" \/>\n<meta property=\"article:published_time\" content=\"2026-01-22T15:24:14+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-07-05T14:57:09+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/01\/scene-1-19.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1344\" \/>\n\t<meta property=\"og:image:height\" content=\"768\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Ben Kazinik\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Ben Kazinik\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/monday.com\\\/blog\\\/ai-agents\\\/how-to-build-ai-agents-for-beginners\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/monday.com\\\/blog\\\/ai-agents\\\/how-to-build-ai-agents-for-beginners\\\/\"},\"author\":{\"name\":\"Ben Kazinik\",\"@id\":\"https:\\\/\\\/monday.com\\\/blog\\\/#\\\/schema\\\/person\\\/2495ad2a5eb69fd196f8af95f5459b08\"},\"headline\":\"How to build an AI agent for beginners in 2026\",\"datePublished\":\"2026-01-22T15:24:14+00:00\",\"dateModified\":\"2026-07-05T14:57:09+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/monday.com\\\/blog\\\/ai-agents\\\/how-to-build-ai-agents-for-beginners\\\/\"},\"wordCount\":9,\"publisher\":{\"@id\":\"https:\\\/\\\/monday.com\\\/blog\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/monday.com\\\/blog\\\/ai-agents\\\/how-to-build-ai-agents-for-beginners\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/monday.com\\\/blog\\\/wp-content\\\/uploads\\\/2026\\\/01\\\/scene-1-19.png\",\"articleSection\":[\"AI Agents\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/monday.com\\\/blog\\\/ai-agents\\\/how-to-build-ai-agents-for-beginners\\\/\",\"url\":\"https:\\\/\\\/monday.com\\\/blog\\\/ai-agents\\\/how-to-build-ai-agents-for-beginners\\\/\",\"name\":\"How to build an AI agent for beginners in 2026 | monday.com Blog\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/monday.com\\\/blog\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/monday.com\\\/blog\\\/ai-agents\\\/how-to-build-ai-agents-for-beginners\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/monday.com\\\/blog\\\/ai-agents\\\/how-to-build-ai-agents-for-beginners\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/monday.com\\\/blog\\\/wp-content\\\/uploads\\\/2026\\\/01\\\/scene-1-19.png\",\"datePublished\":\"2026-01-22T15:24:14+00:00\",\"dateModified\":\"2026-07-05T14:57:09+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/monday.com\\\/blog\\\/ai-agents\\\/how-to-build-ai-agents-for-beginners\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/monday.com\\\/blog\\\/ai-agents\\\/how-to-build-ai-agents-for-beginners\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/monday.com\\\/blog\\\/ai-agents\\\/how-to-build-ai-agents-for-beginners\\\/#primaryimage\",\"url\":\"https:\\\/\\\/monday.com\\\/blog\\\/wp-content\\\/uploads\\\/2026\\\/01\\\/scene-1-19.png\",\"contentUrl\":\"https:\\\/\\\/monday.com\\\/blog\\\/wp-content\\\/uploads\\\/2026\\\/01\\\/scene-1-19.png\",\"width\":1344,\"height\":768,\"caption\":\"How to build an AI agent for beginners in 2026\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/monday.com\\\/blog\\\/ai-agents\\\/how-to-build-ai-agents-for-beginners\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/monday.com\\\/blog\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"AI Agents\",\"item\":\"https:\\\/\\\/monday.com\\\/blog\\\/ai-agents\\\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"How to build an AI agent for beginners in 2026\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/monday.com\\\/blog\\\/#website\",\"url\":\"https:\\\/\\\/monday.com\\\/blog\\\/\",\"name\":\"monday.com Blog\",\"description\":\"\",\"publisher\":{\"@id\":\"https:\\\/\\\/monday.com\\\/blog\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/monday.com\\\/blog\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/monday.com\\\/blog\\\/#organization\",\"name\":\"monday.com Blog\",\"url\":\"https:\\\/\\\/monday.com\\\/blog\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/monday.com\\\/blog\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/res.cloudinary.com\\\/monday-blogs\\\/fl_lossy,f_auto,q_auto\\\/wp-blog\\\/2020\\\/12\\\/monday.com-logo-1.png\",\"contentUrl\":\"https:\\\/\\\/res.cloudinary.com\\\/monday-blogs\\\/fl_lossy,f_auto,q_auto\\\/wp-blog\\\/2020\\\/12\\\/monday.com-logo-1.png\",\"width\":200,\"height\":200,\"caption\":\"monday.com Blog\"},\"image\":{\"@id\":\"https:\\\/\\\/monday.com\\\/blog\\\/#\\\/schema\\\/logo\\\/image\\\/\"}},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/monday.com\\\/blog\\\/#\\\/schema\\\/person\\\/2495ad2a5eb69fd196f8af95f5459b08\",\"name\":\"Ben Kazinik\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/monday.com\\\/blog\\\/wp-content\\\/uploads\\\/2026\\\/04\\\/ben-kazinik-150x150.webp\",\"url\":\"https:\\\/\\\/monday.com\\\/blog\\\/wp-content\\\/uploads\\\/2026\\\/04\\\/ben-kazinik-150x150.webp\",\"contentUrl\":\"https:\\\/\\\/monday.com\\\/blog\\\/wp-content\\\/uploads\\\/2026\\\/04\\\/ben-kazinik-150x150.webp\",\"caption\":\"Ben Kazinik\"},\"description\":\"Ben is a Senior SEO Manager leading the SEO and content strategy of the blog. He is passionate about B2B SaaS strategy, branding, community building, project management, and the future of AI.\",\"sameAs\":[\"https:\\\/\\\/www.linkedin.com\\\/in\\\/ben-kazinik\\\/\"],\"url\":\"https:\\\/\\\/monday.com\\\/blog\\\/author\\\/ben-kazinik\\\/\"}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"How to build an AI agent for beginners in 2026 | monday.com Blog","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/monday.com\/blog\/ai-agents\/how-to-build-ai-agents-for-beginners\/","og_locale":"en_US","og_type":"article","og_title":"How to build an AI agent for beginners in 2026","og_url":"https:\/\/monday.com\/blog\/ai-agents\/how-to-build-ai-agents-for-beginners\/","og_site_name":"monday.com Blog","article_published_time":"2026-01-22T15:24:14+00:00","article_modified_time":"2026-07-05T14:57:09+00:00","og_image":[{"width":1344,"height":768,"url":"https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/01\/scene-1-19.png","type":"image\/png"}],"author":"Ben Kazinik","twitter_card":"summary_large_image","twitter_misc":{"Written by":"Ben Kazinik","Est. reading time":"1 minute"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/monday.com\/blog\/ai-agents\/how-to-build-ai-agents-for-beginners\/#article","isPartOf":{"@id":"https:\/\/monday.com\/blog\/ai-agents\/how-to-build-ai-agents-for-beginners\/"},"author":{"name":"Ben Kazinik","@id":"https:\/\/monday.com\/blog\/#\/schema\/person\/2495ad2a5eb69fd196f8af95f5459b08"},"headline":"How to build an AI agent for beginners in 2026","datePublished":"2026-01-22T15:24:14+00:00","dateModified":"2026-07-05T14:57:09+00:00","mainEntityOfPage":{"@id":"https:\/\/monday.com\/blog\/ai-agents\/how-to-build-ai-agents-for-beginners\/"},"wordCount":9,"publisher":{"@id":"https:\/\/monday.com\/blog\/#organization"},"image":{"@id":"https:\/\/monday.com\/blog\/ai-agents\/how-to-build-ai-agents-for-beginners\/#primaryimage"},"thumbnailUrl":"https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/01\/scene-1-19.png","articleSection":["AI Agents"],"inLanguage":"en-US"},{"@type":"WebPage","@id":"https:\/\/monday.com\/blog\/ai-agents\/how-to-build-ai-agents-for-beginners\/","url":"https:\/\/monday.com\/blog\/ai-agents\/how-to-build-ai-agents-for-beginners\/","name":"How to build an AI agent for beginners in 2026 | monday.com Blog","isPartOf":{"@id":"https:\/\/monday.com\/blog\/#website"},"primaryImageOfPage":{"@id":"https:\/\/monday.com\/blog\/ai-agents\/how-to-build-ai-agents-for-beginners\/#primaryimage"},"image":{"@id":"https:\/\/monday.com\/blog\/ai-agents\/how-to-build-ai-agents-for-beginners\/#primaryimage"},"thumbnailUrl":"https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/01\/scene-1-19.png","datePublished":"2026-01-22T15:24:14+00:00","dateModified":"2026-07-05T14:57:09+00:00","breadcrumb":{"@id":"https:\/\/monday.com\/blog\/ai-agents\/how-to-build-ai-agents-for-beginners\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/monday.com\/blog\/ai-agents\/how-to-build-ai-agents-for-beginners\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/monday.com\/blog\/ai-agents\/how-to-build-ai-agents-for-beginners\/#primaryimage","url":"https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/01\/scene-1-19.png","contentUrl":"https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/01\/scene-1-19.png","width":1344,"height":768,"caption":"How to build an AI agent for beginners in 2026"},{"@type":"BreadcrumbList","@id":"https:\/\/monday.com\/blog\/ai-agents\/how-to-build-ai-agents-for-beginners\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/monday.com\/blog\/"},{"@type":"ListItem","position":2,"name":"AI Agents","item":"https:\/\/monday.com\/blog\/ai-agents\/"},{"@type":"ListItem","position":3,"name":"How to build an AI agent for beginners in 2026"}]},{"@type":"WebSite","@id":"https:\/\/monday.com\/blog\/#website","url":"https:\/\/monday.com\/blog\/","name":"monday.com Blog","description":"","publisher":{"@id":"https:\/\/monday.com\/blog\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/monday.com\/blog\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/monday.com\/blog\/#organization","name":"monday.com Blog","url":"https:\/\/monday.com\/blog\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/monday.com\/blog\/#\/schema\/logo\/image\/","url":"https:\/\/res.cloudinary.com\/monday-blogs\/fl_lossy,f_auto,q_auto\/wp-blog\/2020\/12\/monday.com-logo-1.png","contentUrl":"https:\/\/res.cloudinary.com\/monday-blogs\/fl_lossy,f_auto,q_auto\/wp-blog\/2020\/12\/monday.com-logo-1.png","width":200,"height":200,"caption":"monday.com Blog"},"image":{"@id":"https:\/\/monday.com\/blog\/#\/schema\/logo\/image\/"}},{"@type":"Person","@id":"https:\/\/monday.com\/blog\/#\/schema\/person\/2495ad2a5eb69fd196f8af95f5459b08","name":"Ben Kazinik","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/04\/ben-kazinik-150x150.webp","url":"https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/04\/ben-kazinik-150x150.webp","contentUrl":"https:\/\/monday.com\/blog\/wp-content\/uploads\/2026\/04\/ben-kazinik-150x150.webp","caption":"Ben Kazinik"},"description":"Ben is a Senior SEO Manager leading the SEO and content strategy of the blog. He is passionate about B2B SaaS strategy, branding, community building, project management, and the future of AI.","sameAs":["https:\/\/www.linkedin.com\/in\/ben-kazinik\/"],"url":"https:\/\/monday.com\/blog\/author\/ben-kazinik\/"}]}},"auth_debug":{"user_exists":false,"user_id":0,"user_login":null,"roles":[],"authenticated":false,"get_current_user_id":0},"_links":{"self":[{"href":"https:\/\/monday.com\/blog\/wp-json\/wp\/v2\/posts\/287481","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/monday.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/monday.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/monday.com\/blog\/wp-json\/wp\/v2\/users\/262"}],"replies":[{"embeddable":true,"href":"https:\/\/monday.com\/blog\/wp-json\/wp\/v2\/comments?post=287481"}],"version-history":[{"count":9,"href":"https:\/\/monday.com\/blog\/wp-json\/wp\/v2\/posts\/287481\/revisions"}],"predecessor-version":[{"id":351158,"href":"https:\/\/monday.com\/blog\/wp-json\/wp\/v2\/posts\/287481\/revisions\/351158"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/monday.com\/blog\/wp-json\/wp\/v2\/media\/287483"}],"wp:attachment":[{"href":"https:\/\/monday.com\/blog\/wp-json\/wp\/v2\/media?parent=287481"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/monday.com\/blog\/wp-json\/wp\/v2\/categories?post=287481"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/monday.com\/blog\/wp-json\/wp\/v2\/tags?post=287481"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}