AI Agent Design Patterns

ReAct (Reasoning + Acting): agents alternate between thinking and acting. Simple and effective for many tasks.

Plan-and-Execute: create a plan first, then execute steps. Better for complex, multi-step tasks.

Reflexion: agents evaluate their own performance and improve. Useful for tasks with clear success criteria.

Tools extend what agents can do: search the web, query databases, call APIs, modify files.

Design tools with clear interfaces and good error messages. The agent needs to understand what tools do.

Start with fewer, high-quality tools. Too many options can confuse the agent.

Short-term memory: the current context window. Limited but always available.

Long-term memory: external storage (vector databases, key-value stores) for information that persists.

Episodic memory: specific past interactions. Semantic memory: general knowledge and facts.

Multiple specialized agents can outperform a single general agent.

Patterns: supervisor (one agent coordinates others), peer-to-peer (agents collaborate directly), hierarchical (nested agent teams).

Key challenges: communication protocols, conflict resolution, and avoiding infinite loops.

Define success clearly before building. What does 'working correctly' mean for your agent?

Build evaluation datasets covering expected use cases and edge cases.

Test reliability: agents should handle failures gracefully and recover without human intervention.