GuideAdvanced

Agents In Production

This guide takes the Reservo agent already built in the Agent Fundamentals and Tool Calling Guide — its capstone (M8) is the literal starting point — and runs it in production. It doesn't build an agent or teach it to handle a one-off error (that already exists); it instruments it, measures it, and gates it so it survives real traffic. It covers observability with structured logging of every loop step and a deterministic `trace_id` that correlates an entire run; measuring cost per run with an honest token estimate (`len(text) // 4`) and `claude-sonnet-5`'s cited list pricing; measuring latency per tool and total using an explicitly declared latency model — never the real clock, with explicit honesty about that limitation; a regression eval harness that checks form, deterministically, as a CI-style gate before a change (never a semantic judgment or LLM-as-judge); handling failures at scale with modeled backoff and a per-tool circuit breaker that persists across runs, including Claude's own API 429; and versioning the system prompt/tools/schemas with a rollout that compares the new version against the old one under the same gate before deciding GO/NO-GO. All the operations engineering actually runs on Python 3.14; the LLM call remains a concept, as throughout the rest of the ecosystem.