Overview
Building reliable AI agents in production means narrowing scope, constraining what the agent can do, and instrumenting every step, not making the model smarter. A demo that works once on a happy path fails on the long tail of real traffic; reliability comes from engineering around the model’s nondeterminism. This page is the production checklist behind the architecture patterns in agent-architecture-patterns. For the operational layer see llmops-best-practices and llm-observability.
Scope the task as narrowly as it will go
Reliability falls as autonomy rises. Give the agent the smallest job that solves the problem: a bounded task with clear success criteria beats an open-ended assistant. Prefer a workflow over an agent when the steps are knowable; see agentic-workflow-patterns.
Constrain the tool surface
Every tool the agent can call is a way it can fail or be exploited. Expose the minimum set, validate every argument against a schema, make destructive actions require confirmation, and sandbox filesystem and network access. Treat retrieved text and tool output as untrusted input; see prompt-injection-defense and tool-use-and-function-calling.
Bound every loop
Give each agent loop a hard ceiling on steps, tool calls, wall-clock time, and tokens. A stuck agent should halt and escalate, not spin. Force tool calls and final answers through a schema so the controller can parse state and detect when the agent is looping. See structured-output and agent-loop.
Evaluate continuously against a golden set
You cannot ship what you cannot measure. Build a golden set of representative tasks with known-good outcomes, run it on every prompt or model change, and gate releases on a threshold. Use an LLM-as-judge for open-ended outputs, but calibrate the judge against human labels. See evaluation.
Observe every step in production
Log each step with a trace ID: the prompt, the tool calls and their results, token counts, latency, and the stop reason. Without per-step traces, a production failure is unreproducible. Alert on loop-cap hits, tool-error rates, and cost spikes. See llm-observability.
Degrade gracefully and control cost
Plan for the model being slow, wrong, or down. Add timeouts, retries with backoff, and a fallback ladder from strong model to cheap model to a deterministic default. Cap spend per request and per user, and cache stable prefixes. See cost-control.
Verification before launch
- Run the golden set; confirm the pass rate clears the release threshold.
- Inject adversarial and malformed inputs; confirm the agent refuses or escalates rather than misbehaving.
- Force a tool to fail; confirm the loop recovers or halts cleanly.
- Confirm traces, cost caps, and alerts fire in a staging run.