Last updated: 2026-05-11
AI Agent Architecture Patterns
Agent architecture is about boundaries: tools, state, retrieval, memory, evals, and human review.
Definition
A useful agent architecture makes every capability explicit enough to test, observe, restrict, and replace.
Why it matters
Most agent projects fail from hidden state and unclear tool boundaries, not from lack of another framework.
Problems it solves
- Ambiguous autonomy boundaries
- Unobservable failures
- Unclear ownership between model, tools, and product code
Common misconceptions
- Multi-agent does not automatically mean better architecture.
- Memory and RAG should not be bolted on without evals.
- Human review is a system component, not an afterthought.
Minimal example
Start with one agent, one retrieval path, one tool group, one trace format, and one eval set before splitting roles.
Next step: Pick a pattern, implement the narrowest working loop, then add autonomy only where evals prove it helps.