Apr 14, 2026 · 4 min read

Last updated on Apr 19, 2026

What is an AI Agent? A Developer's Explanation

An AI agent is an LLM that can do things, not just say things. Instead of answering a question and waiting for your next message, an agent plans a sequence of actions, executes them using tools, observes the results, and decides what to do next — autonomously.

Chatbot vs agent

	Chatbot	Agent
Input	Your message	A goal
Output	A response	Completed task
Tools	None	Files, APIs, databases, terminal
Steps	1 (respond)	Many (plan → act → observe → repeat)
Autonomy	Waits for you	Acts independently
Example	”What’s a reverse proxy?"	"Set up Nginx as a reverse proxy for my app”

A chatbot tells you how to do something. An agent does it for you.

How agents work

Every AI agent follows the same loop:

1. PERCEIVE  → Read the current state (files, errors, tool output)
2. REASON    → Decide what to do next
3. ACT       → Execute an action (edit file, run command, call API)
4. OBSERVE   → Check the result
5. REPEAT    → Until the goal is achieved or max steps reached

In code:

async def agent_loop(goal, tools, max_steps=20):
    messages = [
        {"role": "system", "content": "You are a coding agent. Use tools to accomplish the goal."},
        {"role": "user", "content": goal}
    ]
    
    for step in range(max_steps):
        # REASON: LLM decides what to do
        response = await call_llm(messages, tools=tools)
        
        if response.tool_calls:
            # ACT: Execute the tool
            for call in response.tool_calls:
                result = execute_tool(call)
                # OBSERVE: Add result to context
                messages.append({"role": "tool", "content": result})
        else:
            # Done - agent returned a final response
            return response.content
    
    return "Max steps reached"

That’s it. Every AI agent — from Claude Code to Aider to Kimi CLI — is a variation of this loop.

The four components

1. The LLM (the brain)

The language model that reasons and decides. Bigger models make better decisions but cost more:

Model	Agent quality	Cost
Claude Opus	✅ Best	$15/$75 per M tokens
Claude Sonnet	✅ Great	$3/$15
Qwen 3.6 Plus	✅ Good	Free (preview)
DeepSeek R1	✅ Good reasoning	$0.55/$2.19
Small local models	⚠️ Limited	Free

2. Tools (the hands)

Tools let the agent interact with the world. Common tools:

File system — read, write, search files
Terminal — run commands, check output
Web search — find information online
APIs — call external services
MCP servers — standardized tool access

See our tool calling guide and MCP guide for implementation.

3. Memory (the notebook)

Agents need to remember what they’ve done. Without memory, they repeat actions or forget context. Four patterns:

Conversation history — replay past messages
Summarized memory — compress old context
Vector store — semantic search over past interactions
Structured state — JSON tracking progress

See our agent memory patterns guide for implementation.

4. Planning (the strategy)

Good agents plan before acting. Bad agents just start doing things.

Bad agent:  "Fix the bug" → immediately starts editing random files
Good agent: "Fix the bug" → reads error log → identifies cause → finds relevant file → makes targeted fix → runs tests

Planning quality is mostly determined by the LLM. Frontier models (Claude, GPT-5) plan better than small models.

Real-world AI agents

Agent	What it does	How it works
Claude Code	Writes and edits code	LLM + file tools + terminal
Aider	Pair programming in terminal	LLM + git + file editing
Cursor	AI-powered IDE	LLM + codebase indexing + editing
Devin	Autonomous software engineer	LLM + browser + terminal + planning

In our AI Startup Race, 7 agents run autonomously to build startups. Each uses the same loop: plan → code → deploy → check → iterate.

When to use agents

✅ Use agents for:

Multi-step coding tasks (refactoring, debugging, feature building)
Research that requires searching and synthesizing
Tasks that need iteration (try, fail, adjust, retry)

❌ Don’t use agents for:

Single-step tasks (summarize, classify, translate) — just use an API call
Deterministic workflows — use a fixed pipeline instead
High-stakes decisions — keep humans in the loop

See our when NOT to use agents guide for the full decision framework.

Getting started

The fastest way to experience AI agents:

Use one: Install Claude Code ($20/mo) or Aider (free + API)
Build one: Follow our multi-agent guide (50 lines of Python)
Watch one: Follow the AI Startup Race where 7 agents build startups autonomously

FAQ

What’s the difference between an AI agent and an AI assistant?

An AI assistant responds to your messages one at a time and waits for your next input. An AI agent takes a goal, plans multiple steps, executes actions using tools, and keeps working autonomously until the task is complete — or it gets stuck and asks for help.

Do I need expensive frontier models to build an agent?

Not necessarily — smaller models can handle simple agent loops with well-defined tools and clear goals. However, planning quality degrades significantly with smaller models. For complex multi-step tasks, frontier models like Claude Opus or GPT-5 make far fewer reasoning errors and recover better from unexpected situations.

Are AI agents safe to run autonomously?

It depends on the guardrails you set. Most production agents include confirmation steps for destructive actions, spending limits, and maximum step counts. Start with human-in-the-loop approval for critical actions and gradually increase autonomy as you build trust in the system’s behavior.