How to Code AI Agents: From Concept to Production

Building an AI agent is simpler than you think. The core loop is under 100 lines of code. Everything else is tools, memory, and error handling.

The Core Agent Loop

Every AI agent follows the same pattern:

1. Send the goal + available tools to the LLM
2. LLM decides: respond to user OR call a tool
3. If tool call → execute the tool, add result to conversation
4. Go back to step 1
5. If response → return the final answer

That's it. Everything else - design patterns, frameworks, multi-agent systems - is built on top of this loop.

Step 1: Define Your Tools

Tools are functions the agent can call. Each tool has:

• Name - What the agent calls it ("read_file", "search_web")
• Description - What it does (the agent reads this to decide when to use it)
• Parameters - What inputs it accepts (schema definition)
• Implementation - The actual function that executes

Good tool design is the most important part of agent building. Tools should be:

• Focused - One tool, one job
• Well-described - The LLM decides based on the description
• Safe - Validate inputs, handle errors, limit scope

Step 2: Build the Loop

The loop alternates between LLM reasoning and tool execution:

1. Call the AI API with the conversation + tool definitions
2. Check if the response contains a tool call
3. If yes: execute the tool, append the result, loop back
4. If no: the agent is done, return the response

Use the Anthropic or OpenAI API's tool_use feature - it handles the tool calling format natively.

Step 3: Add Memory

Agents need context beyond the current conversation:

Conversation history - Keep the full message chain. This is your short-term memory.

File-based working memory - Let the agent read and write files. This extends capacity beyond the context window.

Persistent memory - Store knowledge across sessions. This is how Claude Code's CLAUDE.md works - persistent project instructions.

Step 4: Handle Errors

Production agents must handle:

• Tool execution failures - API timeouts, file not found, permission denied
• LLM errors - Rate limits, context overflow, content filters
• Infinite loops - Agent stuck trying the same failed approach
• Ambiguity - Agent unsure what to do next

Implement a max iteration limit, error retry with backoff, and a fallback that asks the user for help.

Step 5: Add Planning

For complex tasks, add a planning step before execution:

1. Agent receives goal
2. Agent creates a plan (list of steps)
3. User reviews plan (optional but recommended)
4. Agent executes plan step by step
5. Agent adapts if something unexpected happens

This is how Claude Code's plan mode works. Planning before execution dramatically improves results for multi-step tasks.

Using Existing Frameworks

You don't have to build everything from scratch:

• Claude Code SDK - Build agents with Claude's tool use. Production-ready.
• LangGraph - Complex stateful workflows. Good for branching logic.
• CrewAI - Multi-agent teams. Good for role-based agents.

See Agentic AI Frameworks for the full comparison.

My Recommendation

1. Build a simple agent first - One tool, one goal, basic loop. Understand the mechanics.
2. Use Claude Code daily - Learn agent patterns by using the best agent.
3. Graduate to the SDK - Build custom agents for your specific needs.
4. Add complexity only when needed - Multi-agent, planning, memory - add these as requirements demand.

For the conceptual foundation, see What Is Agentic AI.

Frequently Asked Questions

What programming language should I use for AI agents?

How complex is building a production AI agent?

Can I use AI to help me build an AI agent?