AI Agents in Enterprise — Architectural Patterns for Production

A PoC agent in an afternoon? Sure. A production agent handling 1,000 req/h with graceful failures and audit? A different league.

Production Challenges¶

• Reliability: LLM APIs have outages, timeouts, rate limits
• Determinism: Same input, different output
• Cost control: Agent in a loop = unlimited calls
• Security: Agent with access to production
• Auditability: Why did the agent do that?

Patterns¶

Circuit breaker: Fallback to simpler logic. Human-in-the-loop: Confirmation for high-impact actions. Budget limiter: Max tokens/cost per request. Audit log: Log every call.

State Management¶

Externalize state to Redis/PostgreSQL. The agent can restart and continue. Saga pattern for multi-step workflows.

Testing¶

Unit tests for deterministic parts. Integration tests with mock LLM. E2E tests with real LLM on a golden dataset.

Production Agents Require Engineering Discipline¶

Build them like critical distributed systems — with retry, fallback, monitoring, and audit.