The Dry-Run Pattern: How to Test AI Agents Without Breaking Production

Most AI agent builders test in production. Not because they want to — but because there's no obvious staging environment for agents. Unlike a web app, an agent's "production" is a mix of API calls, file writes, external messages, and scheduled loops. Hard to sandbox cleanly. So agents ship to prod. And prod is where the config drift gets discovered. Here's a pattern that fixes it. The Dry-Run Flag Before any consequential action — a write, a send, a delete, an API call with side effects — check a flag: DRY_RUN = os . getenv ( " DRY_RUN " , " false " ). lower () == " true " def send_tweet ( text ): if DRY_RUN : print ( f " [DRY RUN] Would post: { text [ : 50 ] } ... " ) return { " status " : " dry_run " } return twitter_client . post ( text ) Simple. But the discipline matters: every destructive or external action checks this flag. The Morning Audit Loop Once you have the flag, run a dry-run loop every morning before agents go live: # cron: 0 6 * * * DRY_RUN=true python3 agent_loop.py >