AI Agent Setup Checklist: 15 Steps Before You Deploy
Published: February 23, 2026
Deploying an AI agent without proper preparation is like launching a rocket without checking the fuel gauge. This checklist covers every critical step to ensure your agent succeeds in production.
Pre-Deployment Checklist
What specific problem does your agent solve? Write it in one sentence. If you can't, your scope is too broad.
How will you measure success? Response accuracy, task completion rate, user satisfaction score—pick 2-3 and set targets.
API costs can spiral. Set daily/monthly limits. Build alerts for 50%, 75%, and 90% thresholds.
Every agent action needs an approve/reject mechanism. Store decisions with reasons—this prevents amnesic loops.
Never trust agent self-reporting. If your agent says "file created," verify the file exists and has content.
Security & Safety
Grant minimum necessary access. Read-only where possible. No delete permissions unless absolutely required.
Prevent runaway costs and API bans. Limit requests per minute, hour, and day.
Log every action with timestamp, input, output, and token count. You'll need this for debugging and optimization.
How do you stop the agent immediately? Test it. Document it. Make sure multiple people can trigger it.
Strip sensitive data before logging. Mask API keys, tokens, and personal information.
Operations & Monitoring
Set up notifications for: budget thresholds, error rate spikes, unusual activity patterns, failed health checks.
Document common failure scenarios and recovery steps. Someone else should be able to debug at 3 AM.
What happens with empty inputs? Malformed data? Network failures? Rate limit hits? Test each scenario.
If deployment fails, how do you revert? Test the rollback procedure before you need it.
Weekly review of logs, costs, and performance. Monthly deep-dive into agent decisions and feedback patterns.
Building the agent is 30% of the work. The other 70% is the immune system: verification, detection, memory, and controls. Don't skip it.
Common Deployment Mistakes
- Hallucinated success — Agent reports "done" but nothing happened. Verify outputs.
- Silent failures — Cron jobs die quietly. Build watchdog alerts.
- Context overflow — Long sessions lose early context. Save important data to files.
- Unbounded API calls — A bug can cost thousands in minutes. Set hard limits.
- No memory — Agent repeats mistakes forever. Store feedback with reasons.
Next Steps
Work through this checklist before any production deployment. It takes time upfront but saves days of debugging later.
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