AI Agent Pre-Production Checklist: Prevent the Wrong Action Before It Happens

Teams usually say an AI agent is "ready" when they mean it worked on a happy path. Production readiness is a harder standard: can you explain what it is allowed to do, how it is tested, how it is stopped, and who owns the next bad decision?

What "Pre-Production Checklist: Prevent the Wrong Action Before It Happens" actually means

A real pre-production checklist for agents should verify not just quality, but boundaries, authority, evidence paths, rollback mechanisms, and incident ownership before production autonomy is granted.

If you are asking this question, the pain is usually immediate: the system reaches production before its trust controls reach production too. Teams about to launch agent workflows are not looking for a category lecture in that moment. They need a way to stop the behavior, narrow the blast radius, and create enough evidence to decide whether the agent should keep acting at all.

What to do in the next hour

1. Define the exact scope, risky actions, and forbidden actions of the agent.
2. Verify which workflows are autonomous, approval-gated, draft-only, or human-only.
3. Test adversarial prompts, ambiguous requests, and wrong-tool scenarios.
4. Validate kill switch, containment mode, and rollback ownership.
5. Record the minimal evidence needed to restore autonomy after an incident.

The order matters. Teams get into trouble when they jump straight to prompt edits, add a bigger system prompt, and then tell themselves the issue is handled. That can quiet one visible symptom while leaving the original permission, workflow, or evidence gap untouched.

What not to do when an agent is doing the wrong thing

• Do not launch with generic "we will monitor it" language.
• Do not equate demo success with production readiness.
• Do not leave high-risk tool access live because you plan to revisit it later.

Most "rogue AI" incidents are not dramatic jailbreak movie scenes. They are dull operational failures: a tool should not have been callable, an approval path was missing, context was stale, or nobody could tell whether the agent was still inside its intended scope. Those failures are fixable, but only if you treat them like control problems instead of personality problems.

The red flags that mean you are already late

• The team cannot answer which actions are never autonomous.
• No pre-launch test covers ambiguous or malicious inputs.
• Containment mode has never been rehearsed.
• Ownership after failure is vague or politically fuzzy.

A useful rule of thumb is this: if the only explanation you can give leadership is "the prompt probably drifted," you do not yet have a real operating model. You have a hypothesis. Mature teams replace hypotheses with enforceable boundaries, clear approvals, and a review trail.

Feature launch checklist vs autonomy readiness checklist

Feature launch checklists focus on shipping. Autonomy readiness checklists focus on whether the system deserves authority. Agent teams need both, but they should never confuse them.

This distinction matters because teams under pressure often buy more observability before they define a stop condition. Observability is useful, but it does not prevent a bad action by itself. A useful control changes what the agent is allowed to do, under which conditions, with what proof, and who gets pulled in when the answer is "not yet."

How Armalo helps you stop the wrong action without pretending the problem is solved

• Pacts force the scope and authority model into a concrete artifact before launch.
• Evaluations test whether the agent stays inside that artifact under stress.
• Trust surfaces help teams launch with explicit maturity levels instead of vague confidence.
• Auditability and incident-readiness planning make post-launch control tighter from day one.

That combination is the painkiller. Not "AI governance" in the abstract. A concrete way to define what the agent is allowed to do, independently evaluate whether it stayed inside those boundaries, publish a defensible trust surface, and attach real operational consequence when it does not.

Tiny proof

const launchReady = scopeDefined && riskyToolsGated && evalsPassed && containmentTested;

if (!launchReady) throw new Error('Agent not ready for production autonomy.');

Frequently asked questions

What is the most overlooked pre-production check?

Whether the team has a real degraded mode or kill path. Many launches assume they will figure that out later, which is exactly when they wish they had not.

Should every agent ship with the same readiness bar?

No. The readiness bar should depend on consequence level. A research copilot and a payment agent should not share the same autonomy standard.

Key takeaways

• Production readiness for agents means authority readiness.
• The launch checklist should include containment and evidence, not just quality.
• Different consequence levels deserve different bars.

Next step: Read the docs, explore the trust surfaces, or email dev@armalo.ai if you need help turning a live incident into an operating control.