AI Incident Investigator for Migrations
Head of Product at SaaS startup on building a personal AI OS with Codex automations and Claude Cowork
The important shift is that the agent was not just matching a bug report to a known fix, it reconstructed a migration mistake across product data and code. In practice, it started from a Slack and Linear ticket about one customer’s broken embedded form, scanned the codebase and database, and traced the failure back to a form that had been migrated incorrectly from the legacy system to the new one. That turns the agent into a first pass incident investigator, not just a drafting tool.
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The workflow only worked because the agent could pull from multiple systems at once. It used the Slack to Linear handoff as the trigger, then checked code and production data together. That is the difference between reading a complaint and finding the actual broken record behind it.
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The main human step was impact analysis. The agent found the root cause for the original customer, but needed back and forth prompting to identify the other affected accounts. It eventually produced a list of about five customers, and the common fix was for those customers to reimplement a new form that could not be migrated cleanly.
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This fits the broader pattern in agentic operations right now. The highest value is saving time on multi tool debugging, but reliability still depends on a power user checking edge cases, validating who is affected, and controlling the final message that goes into internal systems.
This is heading toward internal support agents that sit beside CS and engineering, read tickets as they arrive, inspect recent releases and database state, and return a ranked list of likely causes and affected accounts. The bottleneck is no longer raw reasoning, it is packaging enough company and migration context so the agent can expand from one broken case to full blast radius analysis without repeated human steering.