12 Questions to Ask Before You Ship an AI Agent

Author: Patrick Joubert
Role: Co-founder & CEO
Date: 2025-06-24

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 your 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 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 may 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