How production AI agents should handle tool failures, retries, fallback behavior, logs, unsafe chains, and pre-launch failure testing.

A monitoring checklist for AI agent hallucinations covering evidence grounding, risky patterns, human review, regression tests, and answer constraints.

A product requirements checklist for AI agents covering users, allowed actions, knowledge sources, safety requirements, evaluation, and ownership.

A startup-focused AI agent security review covering authority, prompt injection, tool permissions, customer data, monitoring, and enterprise evidence.

A checklist for AI agent audit logs covering decision paths, tool events, policy decisions, correlation IDs, log protection, and post-launch review.

When should an AI agent hand off to a human? This checklist covers mandatory escalation, uncertainty signals, context preservation, loops, and metrics.

How to design an AI agent evaluation dataset that covers real workflows, risky cases, expected outcomes, metadata, and monthly refreshes.

A practical deployment runbook for AI agents covering scope, release packages, pre-launch checks, gradual rollout, monitoring, and rollback.

A practical AI agent red team checklist for testing prompt injection, tool misuse, data exposure, refusals, and regression coverage before launch.

Prepare customer support teams to explain, triage, and escalate AI agent issues after production launch.