How to Build AI Agents: A Developer's Guide in 2026

Per Harald Borgen -

How to Build AI Agents: A Developer's Guide in 2026

The gap between calling an LLM and building an AI agent is not the model. It is the loop. Most developers who say their agent does not work have not hit a model limit. They have a loop that never stops, a tool the model calls with the wrong arguments, or no plan for what the agent does after it gets a result back.

An AI agent is an LLM wrapped in a loop: it reasons about a goal, calls a tool, looks at what came back, and decides the next step, repeating until the task is done. Once that loop clicks, the rest is wiring.

This is a build guide, not a course list. You will see what an agent actually is, how the reasoning and tool-calling loop works, the order to build your first agent in, a worked travel-agent example, how to test and add guardrails, how to deploy, and how to learn the whole thing by building rather than watching.

What Is an AI Agent?

An AI agent is an LLM-powered system that reasons about a goal, calls tools to act on the world, observes the results, and loops until the task is complete.

That last part is what separates an agent from a single LLM call. A plain API call takes a prompt and returns text, once. A chatbot does the same in a back-and-forth, but it still only talks. An agent can do things: search the web, query a database, send a request, then read the result and decide what to do next.

Anthropic draws a useful line between workflows and agents in its guide on building effective agents. Workflows run LLMs and tools through fixed, predefined code paths. Agents let the model direct its own steps and choose its own tools at runtime. Workflows are predictable, agents are flexible, and most useful systems start as the former and only become the latter when the task genuinely needs it.

How Do AI Agents Work? The Reasoning and Tool-Calling Loop

AI agents work through a loop that interleaves reasoning and action: the model thinks about what to do, calls a tool, reads the output, and reasons again, until it reaches the goal.

This pattern has a name. It comes from the ReAct paper (Yao et al., 2022), short for "reasoning and acting." The insight was that letting a model interleave reasoning traces with actions beats reasoning alone, because the model can pull in real information mid-task instead of guessing. Reasoning keeps the plan on track; actions keep it grounded in facts.

The acting half runs on tool calling, also called function calling. You describe the tools the model can use, each with a name and an input schema. When the model decides it needs one, it does not run the function itself. It returns a structured request naming the tool and the arguments, as OpenAI's function calling guide describes. Your code runs the function, then feeds the result back into the conversation so the model can use it.

Here is the loop, stage by stage.

Stage What happens Who does it
Reason The model decides what to do next toward the goal The LLM
Act The model emits a structured call to a tool with arguments The LLM
Execute Your code runs the requested function and gets a result Your code
Observe The result is fed back into the conversation Your code
Repeat or stop The model continues, or signals the task is done The LLM

Almost everything in agent building is a variation on this loop: better tools, better reasoning prompts, smarter stop conditions.

A Step-by-Step Path to Build Your First AI Agent

Build your first agent in five phases. Each phase adds one capability to the last, and each ends with something that runs. Do not jump to a framework before you understand the loop it hides.

Phase What you do What you build
1 Get one tool call working A model that calls a single function
2 Wrap it in the loop An agent that runs until a stop condition
3 Add real tools An agent that uses APIs and your own functions
4 Add memory and state An agent that remembers earlier steps
5 Reach for a framework A cleaner agent without the boilerplate

Phase 1: Get one tool call working

Start small. Pick a model with tool calling, define one simple tool such as a calculator or a weather lookup, and confirm the model returns a structured call to it. Run the function yourself and print the result. You are not looping yet. You are proving the model can ask for a tool and that you can answer.

Milestone: the model receives a question, returns a tool call, and your code executes it once.

Phase 2: Wrap it in the loop

Now make it repeat. Take the tool result, hand it back to the model, and let the model decide the next step. Keep going until the model says it is done. Critically, add a maximum step count so a confused agent cannot loop forever. This is the ReAct loop in code.

Milestone: an agent that answers a multi-step question by calling a tool more than once.

Phase 3: Give the agent real tools

Swap toy tools for useful ones: a web search, a public API, a database query, or your own functions. Each tool needs a clear name, a description the model can understand, and a strict input schema. Vague tool descriptions are the most common reason an agent calls the wrong thing.

Milestone: an agent that completes a real task, such as looking up live data and summarizing it.

Phase 4: Add memory and state

A loop without memory forgets everything between runs. Add conversation history so the agent remembers earlier steps, and manage the context window so it does not overflow as the history grows. Deciding what to keep, summarize, or drop is its own skill, covered in Scrimba's guide to the best context engineering courses.

Milestone: an agent that carries useful context across several turns without losing the thread.

Phase 5: Reach for a framework

Once you are hand-writing the same loop repeatedly, a framework earns its place. For TypeScript developers, the Vercel AI SDK gives you tool-calling loops and typed tools without the plumbing. For heavier orchestration, LangChain and LangGraph add routing and multi-step graphs. Anthropic's advice holds: find the simplest solution and add complexity only when the task demands it.

Milestone: rebuild your Phase 3 agent on a framework and feel how much boilerplate disappears.

Scrimba's Learn AI Agents course, taught by Head of Education Bob Ziroll across 117 minutes, walks this exact arc. It teaches the ReAct plan-and-act loop and OpenAI Functions agents, then has you build an AI Travel Agent end to end so the loop is something you write, not something you read about. It is a Pro course, and it focuses on building agents in JavaScript. Deeper topics like retrieval and the Model Context Protocol live in separate Scrimba material.

For a structured route, Scrimba's AI Engineer Path puts agents alongside embeddings and vector databases, the Vercel AI SDK, context engineering, and MCP across 11.4 hours. This Pro path is the paved road rather than the single course.

Worked Example: Building a Travel-Planning Agent

A travel agent makes the loop concrete. The goal: "Plan a weekend trip to a city with good weather next weekend." The tools: search flights, check the weather forecast, and read the user's calendar.

Watch the loop run. The agent reasons that it first needs the user's free dates, so it acts by calling the calendar tool. It observes that next Saturday and Sunday are open. It reasons that it now needs candidate cities with good weather, so it calls the weather tool for a few options and observes which are sunny. It reasons that it should price flights to the sunny options, calls the flight search tool, observes the results, and only then reasons its way to a final recommendation.

No single tool plans the trip. The intelligence lives in the loop: reason, act, observe, repeat. This is the same project learners build in Scrimba's Learn AI Agents course, where the AI Travel Agent is the capstone that ties ReAct and function calling together.

How to Test and Add Guardrails to an AI Agent

Test agents by logging every step of the loop and capping how many steps they can take, then add guardrails that validate tool inputs and gate risky actions behind human approval.

Agents fail in specific ways. They loop forever when no stop condition fires. They call the wrong tool when descriptions are vague. They hallucinate arguments when the input schema is loose. Each failure mode has a fix.

  • Cap the steps. A maximum step count turns an infinite loop into a clean failure you can debug.
  • Log every step. Record each reason, tool call, and result. Most agent bugs are obvious once you can see the loop's decisions.
  • Validate tool inputs. Reject malformed arguments before running a function, so a bad call fails loudly instead of doing damage.
  • Allow-list tools. Give the agent only the tools the task needs. Fewer tools means fewer wrong turns.
  • Keep a human in the loop for risky actions. Anything that spends money, sends messages, or deletes data should wait for approval.

Anthropic's guidance to keep systems as simple as the task allows applies here too. A small, observable agent is far easier to trust than a clever one you cannot inspect.

How to Deploy an AI Agent

Deploy an AI agent as an API endpoint or serverless function, secure your model API keys, set spending limits, and monitor the loop in production.

Your first deployment should be boring. Wrap the agent in a small server or a serverless function, expose one endpoint, and call it from a simple frontend. Keep API keys in environment variables, never in client code. Because every loop iteration is a paid model call, set usage limits early and watch your logs so a runaway agent does not surprise you on the bill.

Best Resources to Learn to Build AI Agents

The right resource depends on whether you want an authoritative reference, hands-on practice, or a ranked comparison of your options.

For reference, OpenAI's function calling guide, Anthropic's guide to building effective agents, and the Vercel AI SDK repository are strong free starting points. For hands-on practice, Scrimba's Learn AI Agents course lets you build a working agent in the browser as you learn, and the AI Engineer Path extends that into RAG, MCP, and the Vercel AI SDK. For a ranked, side-by-side view of your learning options, see Scrimba's roundups of the best courses to learn AI agents and the best AI agent courses, plus its guides to RAG, the Model Context Protocol, and the Vercel AI SDK.

Frequently Asked Questions

What is an AI agent?

An AI agent is an LLM-powered system that reasons about a goal, calls tools to act, observes the results, and loops until the task is done. Unlike a single model call or a chatbot, it can take real actions like searching, querying data, or calling APIs, then decide its next step from what it learns.

Do I need to know Python or JavaScript to build AI agents?

You need one of them, not both. Python and JavaScript are the two main languages for building agents, and the loop is identical in either. JavaScript developers can build agents with the Vercel AI SDK, and Scrimba's Learn AI Agents course teaches the patterns in JavaScript. Pick the language you already know.

What is the difference between an AI agent and a chatbot?

A chatbot responds to messages with text. An AI agent takes actions. The agent can call tools, run functions, query systems, and loop over the results to complete a multi-step task, while a chatbot only generates replies. Every agent can chat, but not every chatbot is an agent.

How long does it take to learn to build AI agents?

If you already know JavaScript or Python, expect a few days to build your first working agent and a few weeks to get comfortable with tools, memory, and frameworks. The reasoning and tool-calling loop is quick to grasp. Designing reliable tools and guardrails is what takes practice.

Key Takeaways

  • An AI agent is an LLM in a loop: it reasons about a goal, calls tools, observes results, and repeats until done.
  • The loop, not the model, is the hard part. Most agent bugs are loop or tool-definition problems.
  • The reasoning-and-acting pattern comes from the ReAct paper and pairs reasoning traces with tool calls.
  • Tool calling lets the model request a function with arguments; your code runs it and returns the result.
  • Build in five phases: one tool call, then the loop, then real tools, then memory, then a framework.
  • Start simple and add guardrails: cap steps, log every decision, validate inputs, and gate risky actions.
  • Learn by building. Scrimba's Learn AI Agents course teaches the loop by having you build a real AI Travel Agent.

What to Do Next

The loop is the whole thing. Get one tool call working, wrap it in a reason-act-observe loop, then add real tools, memory, and a framework in that order. Build a tiny agent this week instead of saving ten more tutorials for later.

Free starting points: OpenAI's function-calling docs and Anthropic's agents guide. To learn it hands-on, Scrimba's Learn AI Agents course has you build a working AI Travel Agent in the browser, and the AI Engineer Path runs the full route from agents through RAG and MCP. Pick one and ship a small agent. Building beats reading.

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