5 Questions Nobody Asks Before Deploying an Agent

The checklist I wish I had before my agent promised 200 customers a refund policy that didn't exist.

01. What happens when your agent doesn't know the answer?

LLMs are prediction engines. They predict the next token based on statistical patterns — they don't verify facts. They have no internal mechanism to say "I don't know." When faced with uncertainty, an LLM won't pause. Won't flag. Won't escalate. It will generate a confident, fluent response regardless of whether it's accurate.

My agent invented a refund policy because it had no instruction for "I don't have this information."

02. How does error compound across your orchestration chain?

If each step has a 95% accuracy rate: - 1 step: 95% success - 5 steps: 77% success - 10 steps: 60% success - 20 steps: 36% success

Real-world agents don't fail at 5%. Complex orchestration tasks fail at 15–20%. Failures aren't independent — they cascade.

03. What are you actually measuring in production?

You can have 99.9% uptime, sub-500ms latency, zero system errors — and still have an agent that's wrong 30% of the time. Uptime ≠ accuracy.

04. How do you verify before the agent acts?

Some actions are irreversible: sending emails, processing refunds, deleting records, making commitments to customers. Every action that can't be undone needs a gate before it.

05. What's your recovery plan when it fails?

Not if. When. Only 27% of organizations express trust in fully autonomous AI agents. The organizations that trust their agents aren't the ones whose agents don't fail — they're the ones who know exactly what to do when they do.

The best agents aren't the ones that never fail. They're the ones that fail safely, visibly, and recoverably.