Common AI Agent Design Patterns

AI agents follow several recurring architectural patterns. The simplest is the
single-loop agent: receive input, call an LLM, optionally invoke tools, and
return a response. This works well for straightforward tasks but struggles with
complex multi-step problems.

The router pattern directs each sub-task to the best-suited model or provider.
A cheap, fast model handles simple lookups while a more capable model tackles
reasoning-heavy work. This optimizes cost and latency across a session.

Multi-agent teams assign specialized roles to different agents. A lead agent
decomposes a goal into sub-tasks and delegates them to worker agents, each with
its own tools and system prompt. Frameworks like CrewAI and AutoGen formalize
this pattern with role definitions and communication protocols.

The RAG pattern augments LLM calls with retrieved context. Documents are chunked,
embedded, and stored in a vector index. At query time, relevant chunks are
retrieved and injected into the prompt, grounding the model's response in
source material and reducing hallucinations. Fallback chains add resilience
by switching providers when the primary one fails.
