AI Agent Deployment Checklist

When to Use This Checklist

Use this checklist before deploying an autonomous LLM agent that calls tools and takes actions. Unlike a chatbot, an agent can write data, send messages, or trigger workflows, so a mistake has real-world consequences. This list covers the authorization, safety limits, and observability needed to deploy agents responsibly.

How to Use This Checklist

Start by defining goals and an explicit list of allowed and forbidden actions. Scope every tool to least privilege so the agent cannot exceed its mandate, and require human approval for irreversible steps. Add step, loop, and cost limits to stop runaway execution, and log the full reasoning trace and tool calls for audit. Red-team the agent for goal hijacking before launch, and confirm a kill switch can halt it instantly.

What Good Looks Like

A safe agent operates within clearly bounded permissions, requesting human approval for high-impact actions. Each tool is least-privilege and validates inputs against injection. Hard limits on steps, loops, and cost prevent runaway behavior. Full reasoning and tool-call traces support auditing. The agent passes a multi-step eval suite and a red-team pass, and operators can halt it instantly with a kill switch.

Common Pitfalls

The most dangerous pitfall is giving an agent broad credentials, so a single hijack causes wide damage. Missing step or cost limits lead to runaway loops and large bills. Skipping human approval on irreversible actions removes the last safety net. Indirect prompt injection via tool output is often overlooked. Finally, agents without reasoning traces are nearly impossible to debug after an incident.

Related Resources

Review AI agent design patterns, the Model Context Protocol, LLM guardrails, AI red-teaming, and least-privilege principles.