Agent Architecture Patterns

Agent Architecture Patterns

Choosing the right architecture is the most important design decision when building an AI agent.

ReAct (Reasoning + Acting)

The most common pattern. The agent alternates between thinking and acting:

Thought: I need to find the current weather in Tokyo
Action: search("Tokyo weather today")
Observation: Tokyo is 22°C and sunny
Thought: I now have the answer
Action: respond("The weather in Tokyo is 22°C and sunny")

Plan-and-Execute

The agent creates a full plan before taking any actions:

1. Analyze the task
2. Break into subtasks
3. Execute each subtask sequentially
4. Verify results

Best for complex, multi-step tasks where order matters.

Reflexion and Self-Correction

The agent evaluates its own outputs and iterates:

• Generate initial response
• Critique the response (find errors, gaps)
• Revise based on critique
• Repeat until satisfactory

Tool-Augmented Agents

Agents that extend their capabilities through external tools:

• Web search, calculators, code execution
• Database queries, API calls
• File operations, image generation

Router/Orchestrator Patterns

A central agent routes tasks to specialized sub-agents:

User query → Router Agent → [Research Agent | Code Agent | Writing Agent]

Key Takeaways

• ReAct is the default starting pattern for most agents
• Plan-and-Execute works best for complex multi-step tasks
• Reflexion improves output quality through self-critique
• Choose based on task complexity, latency requirements, and reliability needs