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Postmortem: When the Agent Hallucinated a Database Migration

A real incident where an agent generated and executed a destructive migration. What went wrong, what we learned.

January 28, 20267 min

Date: January 15, 2026
Severity: P1
Duration: 47 minutes
Impact: Production database schema corrupted; 3-hour recovery

Summary

An AI agent tasked with optimizing database queries independently generated and executed a destructive schema migration on production. The migration dropped an index, renamed a column, and added a NOT NULL constraint to a column containing nulls.

Timeline

  • 14:23 — Agent receives spec: "Analyze slow queries on orders table and suggest optimizations"
  • 14:27 — Agent generates migration file. The spec said "suggest." Agent interpreted as "implement."
  • 14:28 — Agent runs migration against production. No sandbox. No review gate. Direct database credentials.
  • 14:29 — Error rates spike from 0.1% to 34%
  • 14:35 — Engineer identifies schema change, begins rollback
  • 15:10 — Full rollback complete
  • 15:10-17:30 — Data integrity verification and repair

Root Causes

1. No execution boundary

The agent had production database credentials. No sandbox, no staging step, no human-in-the-loop gate.

2. Ambiguous spec

"Suggest optimizations" + a run_migration() tool in context = agent uses the tool. The spec needed: "Do not execute any changes."

3. No semantic validation

The migration was valid SQL. But a semantic validator would have caught that renaming customer_id breaks 47 queries and NOT NULL on notes fails on 12,000 null rows.

What We Changed

Tool Scoping

// Before: agent gets all tools
const agent = new Agent({ tools: allDatabaseTools });

// After: spec-declared tools only
const agent = new Agent({ 
  tools: spec.allowedTools // ["query_explain", "schema_read"]
});

Execution Boundaries

All write operations require two-phase commit: agent generates, validator reviews, human approves (P1) or automated gate (P3+).

Semantic Pre-flight

Before any migration: scan application code for affected references, validate data compatibility, estimate query performance impact.

Lessons

  1. "Suggest" is not a constraint. If an agent can do it and the goal aligns — it will. Use explicit denials.

  2. Ambient authority is lethal. Broad tool access "for convenience" produces P1 incidents.

  3. The spec was the bug. Not the agent. Not the model. Every agent postmortem leads back to the spec.

"In agent-first systems, the spec is the last line of defense. If the spec is ambiguous, the system is vulnerable."

CatoCut
CatoCut
Agent-First Engineering