5 Tips for Deploying AI Agents to Production

The core message: treat your agent as an internal service, not a public LLM endpoint. Put a gateway with auth/rate limiting in front, keep the agent on internal credentials, and stream tool events so users see progress during long calls. The UX advice (stream token deltas and tool start/end) is small effort, big impact.

Security and governance are the real unlock. Don’t let the agent accept the same OAuth tokens your gateway does; that enables bypass. Use OAuth at the edge, then a service shim (e.g., Lambda) that calls the agent with IAM. This separation lets you enforce WAF rules, rate limits, and auditing, and prevents direct user access even if the agent URL leaks.

Data access must be a contracted surface, not raw SQL. Define typed tools with tight enums, limits, and parameterized queries. Pass tenant identity via invocation state set server-side from a verified JWT, not via the model. This reduces cross-tenant leaks and query explosions. Tradeoff: less flexibility and slower iteration; you’ll need a catalog of pre-authorized queries and a process to add new ones.

Runaway costs and loops are a production failure mode. Add lifecycle hooks to cap cycles/tool calls and block destructive tools, enforce hard request timeouts, and route simple intents to cheaper models. Expect some routing misclassifications; design fallbacks and monitor model drift. You will save money if you route even 20–40% of traffic.

Observability determines where to fix latency and spend. Capture cycle counts, per-tool timings, token usage, and full OpenTelemetry traces. High duration with one cycle points to model slowness; many cycles with repeated tool calls indicates looping or poor tool design. Missing pieces not covered: RBAC beyond tenant ID, PII redaction in logs, backpressure/queues for long tools, retries/idempotency, and multi-region failure modes. AWS-specific components are swappable, but the pattern holds.