From AI Agent Pilot to Production: A Practical Checklist

Most AI agent pilots look great in a demo and stall before production. The pilot worked because someone watched it. Production means the agent runs unattended, on real customers, with real money on the line, every day. The gap between those two states is mostly operational, not model-related.

Here's the checklist teams that actually ship use.

1. Define what "good" means before you start

Pick three metrics, write them down, and make them measurable.

• Quality metric: resolution rate, accuracy, customer satisfaction, deflection rate.
• Safety metric: false-action rate, escalation accuracy, hallucination rate on a fixed test set.
• Cost metric: cost per task, tokens per resolution, average latency.

If you can't measure it, you can't ship it. "It feels good" gets killed in week three of production.

2. Build an eval set before you build the agent

Take 100–300 real historical tasks—real tickets, real leads, real contracts. For each, write down the correct outcome. This is your eval set. Every model change, prompt change, and tool change runs against it. Without this you're flying blind, and the first time the model provider updates their default version you'll find out by reading angry customer emails.

3. Guardrails that match the blast radius

Match the constraint to what's at stake.

• Read-only actions (summarize, classify, suggest): light guardrails. Confidence threshold and a fallback message.
• Reversible actions (drafting an email, creating a draft ticket): require user approval before send.
• Irreversible actions (sending money, deleting data, public messages): require human approval, full audit trail, daily volume cap.

The cap matters. A bug in a sending agent without a cap is how you find out about it in the news.

4. Observability before launch, not after

Log every step: input, model used, tokens, tools called, decisions made, output, and final outcome. Use a tracing tool (LangSmith, Braintrust, Arize, or even structured logs in your existing stack). When something goes wrong in week two, you need to be able to answer "what did the agent see and why did it do that?" within five minutes. If you can't, you'll spend days fishing.

5. A staged rollout with real exit criteria

Don't flip the switch on 100% of traffic.

1. Shadow mode: agent runs in parallel with humans, outputs are logged but not delivered. Compare against human decisions.
2. 5% canary: agent handles 5% of real traffic. Watch quality and safety metrics daily.
3. 25%: more confidence, broader behavior coverage.
4. 100%: only after metrics hold steady for two weeks at the previous tier.

Each stage needs an exit criterion ("quality metric within X% of human baseline for 7 consecutive days") and a rollback plan that can fire in under 15 minutes.

6. Plan for the failure modes you haven't seen yet

The interesting failures in production aren't the ones you tested. They're:

• Model provider outages. Your fallback should be a queue or a degraded-mode response, not an error page.
• Context window overflow on edge-case long inputs.
• Prompt injection from data the agent retrieves.
• Silent quality regressions when the provider updates the default model snapshot.
• Cost spikes from runaway loops or a misconfigured tool that returns a 10MB response.

Each one has a known mitigation. Build the mitigation before you need it.

7. Document the handoff

Production agents have owners. Write down who is on call, what the runbook says, and how to roll back. If the people who built the pilot are the only ones who understand the agent, you don't have a production system—you have a pet project that customers depend on. That ends badly.

The teams that ship and keep shipping treat the agent like any other production service: evals, observability, on-call, gradual rollouts, and a clear owner. The model is the easy part.