Claude AI Agent Tutorial: Build Your First Agent

What is a Claude AI Agent?

A Claude AI agent is an application where Claude plans a multi-step approach, calls tools to interact with external systems, observes the results, and continues working autonomously until the goal is complete. Unlike a chatbot that handles one message at a time, an agent pursues a goal across multiple reasoning steps — deciding what to do next based on what it just observed.

Every AI application you have built so far in this series follows the same pattern: user sends a message, Claude responds, done. This works perfectly for question answering, document analysis, classification, and most conversational use cases. But some tasks do not fit a single-turn pattern.

Imagine asking Claude to: research a company, find its tech stack, check if they are hiring for roles matching your skills, and draft a personalised outreach email. That is not one task — it is four sequential tasks, where each step depends on what the previous step found. No single API call can handle this. You need an agent.

An AI agent is a Claude application that plans a multi-step approach, takes actions autonomously using tools, observes the results, and continues working until the goal is complete. This post introduces the agent concept, explains the plan-act-observe loop, and walks you through building a functioning agent from scratch.

What Makes an Agent Different from a Chatbot?

A chatbot responds to each message independently. An agent pursues a goal.

The technical distinction is straightforward:

• A chatbot: Receives input → generates a single response → stops
• An agent: Receives a goal → plans → takes action (tool call) → observes result → updates plan → takes next action → repeats until goal is complete

Agents have three capabilities that chatbots typically lack:

• Tool use: The ability to call functions that interact with external systems
• Multi-step reasoning: The ability to plan a sequence of steps and execute them in order
• Self-direction: The ability to decide what to do next based on what just happened, without being told step by step

The Plan-Act-Observe Loop

Every agent — whether built on Claude or any other model — follows this fundamental loop:

1. Plan: Given the goal and current context, what is the next action to take?
2. Act: Execute the chosen action via a tool call
3. Observe: Incorporate the tool result into context
4. Evaluate: Is the goal complete? If yes, produce the final response. If no, return to step 1

In Claude's case, this loop runs automatically through repeated API calls. Claude sees the conversation history, the available tools, and all previous tool results, and decides what to do next on each iteration.

Your First Agent: A Research Assistant

Let us build a simple research agent that can search the web, extract information, and produce a structured summary. The agent can decide how many searches it needs and in what order.

Breaking Down the Agent Loop

The critical part of the code above is the while loop and the stop condition:

• response.stop_reason == "end_turn": Claude has decided it is finished and is producing a final text answer. Exit the loop.
• response.stop_reason == "tool_use": Claude wants to call one or more tools. Execute them and continue the loop.
• max_iterations guard: Always include a maximum iteration limit. Agents can get stuck in loops — a hard limit prevents runaway API costs and infinite loops from bugs in tool results.

When to Use Agents vs Simple API Calls

Not every task needs an agent. Use agents when:

• The task requires multiple steps where each step depends on the previous result
• The number of steps is not known in advance — the agent must decide based on what it finds
• The task involves interacting with multiple tools or data sources
• The task requires real-world actions, not just text generation

Use simple API calls when:

• The task is a single step (summarise this document, classify this text)
• The steps are fixed, known, and can be coded directly without Claude deciding them
• Latency is critical — every agent iteration adds API round-trip time

Summary

An AI agent is the combination of Claude's reasoning, tool use, and a loop that runs until the goal is complete. The key components:

• Clear goal: The user's task stated as a complete objective, not just a single question
• Well-defined tools: Tools with clear descriptions so Claude knows when and how to use them
• The agent loop: A while loop that calls the API, handles tool results, and continues until stop_reason == "end_turn"
• A maximum iteration limit: Safety against infinite loops and runaway costs
• A guiding system prompt: Tells the agent how to approach its tasks and what its scope is

Going Further with Claude Agents

Once you have the basic agent loop working, there are several natural next steps:

• Add real web search: Integrate the Claude web search tool so your research agent queries live data instead of mocked results.
• Connect tools via MCP: The Model Context Protocol lets Claude connect to any tool ecosystem through a standardised interface — databases, file systems, APIs.
• Build specialised agents: The build AI coding agent with Claude API tutorial walks through a production-ready coding agent with real tool integrations.

The Anthropic agents and tools documentation is the authoritative reference for tool definitions, the tool_choice parameter, and best practices for agent design. Anthropic's Building Effective Agents guide covers patterns and anti-patterns from real-world agent deployments.

In the next post, we go deeper into how this loop works under the hood — the full agentic reasoning cycle that enables Claude to self-correct, revise plans, and handle unexpected results: The Agentic Loop: How Claude Reasons, Acts, and Self-Corrects.

This post is part of the Anthropic AI Tutorial Series. Previous post: Knowledge Check: Claude Tools & Capabilities Quiz.

External references:

• Claude tool use documentation — Anthropic
• Claude API reference — Anthropic developer docs