AI Agent Design Patterns: How Production Agents Work

Every production AI agent is built from a handful of core patterns. Understanding these patterns lets you build better agents and use existing tools more effectively.

The Core Loop: ReAct

Reason → Act → Observe → Repeat. This is the foundation of every AI agent.

1. The agent receives a goal
2. It reasons about what to do next
3. It takes an action (tool call, code execution, API request)
4. It observes the result
5. It reasons about the next step based on the result
6. Repeat until the goal is met or it gets stuck

Claude Code uses this pattern - it thinks about your request, reads files, writes code, runs tests, and fixes errors in a continuous loop.

Pattern 1: Tool Use

Agents are only as capable as their tools. The tool use pattern gives an LLM access to functions it can call:

• File system tools - Read, write, search files
• Code execution - Run scripts, tests, builds
• API tools - HTTP requests, database queries
• Search tools - Web search, codebase search

The key design decision: which tools to expose and how to constrain them. Too many tools = confusion. Too few = inability.

Pattern 2: Planning + Execution

Split the agent into two phases:

Planning: Analyze the goal, break it into steps, define success criteria. Claude Code's plan mode is this pattern.

Execution: Follow the plan step by step, adapting when unexpected results appear.

This separation prevents the agent from diving into implementation before understanding the full scope.

Pattern 3: Multi-Agent Orchestration

Use multiple specialized agents instead of one general agent:

• Orchestrator - Breaks down the task and delegates
• Specialists - Each handles one domain (frontend, backend, testing)
• Reviewer - Validates output from specialists

Claude Code's subagent system implements this. The main agent dispatches parallel workers for research, implementation, and review.

Pattern 4: Memory and Context

Agents need memory beyond the conversation:

• Short-term - Current conversation context (the context window)
• Working memory - Files, notes, scratchpads during execution
• Long-term - Persistent knowledge across sessions (CLAUDE.md, project docs)

Effective agents combine all three. Claude Code reads CLAUDE.md for project-specific instructions, uses the file system as working memory, and has the full conversation as short-term context.

Pattern 5: Error Recovery

Production agents must handle failure gracefully:

• Retry with modification - If an approach fails, try a different strategy
• Graceful degradation - If the full task can't be completed, complete what's possible
• Human escalation - Ask for help when stuck rather than looping forever

Claude Code implements all three - it retries failing tests with different fixes, falls back to simpler approaches, and asks for clarification when genuinely stuck.

Applying These Patterns

You don't need to build agents from scratch. Understanding these patterns helps you:

1. Use existing tools better - Knowing Claude Code uses ReAct + Planning helps you write better prompts
2. Debug agent behavior - When an agent fails, identify which pattern broke down
3. Build custom agents - The Claude Code SDK lets you build agents using these patterns

For practical agent use cases, see AI Agent Use Cases.

Frequently Asked Questions

What's the most important AI agent design pattern?

Do I need to understand design patterns to use AI agents?

Can I build production AI agents with no ML background?