12 Questions to Ask Before You Ship an AI Agent

Shipping an AI agent into production is fundamentally different from deploying traditional software. The probabilistic nature of language models introduces failure modes that most engineering teams have never encountered. Before you push that agent live, work through these twelve questions with your team. If you cannot answer each one confidently, you are not ready.

Accuracy and Hallucinations

1. How do you detect when the agent fabricates information? Every AI agent will, at some point, generate text that sounds plausible but is factually wrong. You need automated detection mechanisms, not just manual review, to catch hallucinations before they reach customers.

2. What is your hallucination rate, and how do you measure it? If you cannot quantify your hallucination rate on representative test sets, you cannot claim your agent is production-ready. Establish baselines and track this metric continuously.

3. Which decisions require deterministic accuracy versus probabilistic responses? Not every output needs to be perfect. Identify the high-stakes decisions, such as pricing, eligibility, and compliance statements, that demand guaranteed correctness, and route them through a verified reasoning layer.

Guardrails and Compliance

4. What prevents the agent from making unauthorized promises or commitments? Language models are eager to please. Without explicit guardrails, your agent will offer discounts, make delivery guarantees, or agree to terms it has no authority to grant.

5. How do you enforce regulatory and policy constraints in real time? Compliance rules must be checked before the agent responds, not after. Post-hoc filtering is too slow and too unreliable for regulated industries.

6. Can your guardrails adapt to different regulatory jurisdictions? If you operate across regions, your agent must respect jurisdiction-specific rules. A static rule set will not suffice; you need dynamic, context-aware constraint enforcement.

Auditability and Trust

7. Can you explain why the agent made a specific decision? When a customer, manager, or regulator asks why the agent said what it said, you need a clear, traceable answer. If your reasoning is opaque, trust erodes quickly.

8. Do you maintain complete audit trails for every agent interaction? Every input, every reasoning step, and every output should be logged in a format that compliance and legal teams can review.

9. How do you handle edge cases where the agent is uncertain? The best agents know when they do not know. Define escalation paths for low-confidence scenarios so that uncertain decisions are routed to human reviewers.

Production Readiness

10. What is your rollback plan if the agent starts producing bad outputs? You need the ability to pull an agent offline, revert to a previous version, or switch to a human fallback within minutes, not hours.

11. How do you monitor agent performance in real time? Dashboards that show response quality, guardrail violations, escalation rates, and customer satisfaction scores should be live from day one.

12. Have you stress-tested the agent with adversarial inputs? Users will try to jailbreak your agent, feed it contradictory instructions, or push it outside its domain. Test for these scenarios before your customers discover them for you.

If these questions exposed gaps in your deployment plan, that is a good sign. It means you are taking production readiness seriously. Address each gap methodically, and you will ship an agent that your organization and your customers can trust.