AI Code Review for Post-Incident Analysis

The post-incident gap

Every engineering team follows a similar pattern after an incident. The on-call engineer identifies the immediate cause, pushes a fix, and restores service. The team runs a post-mortem that documents the timeline, contributing factors, and action items. One of those action items is almost always a variation of: “audit the codebase for similar patterns.”

That action item rarely gets completed. The team is already behind on their sprint. The developer who understands the incident best is needed on other work. A grep for the exact code pattern finds a few matches, but the underlying issue – the class of bug, not the specific instance – requires reasoning about code semantics, not string matching. The audit item sits in the backlog, priority drops over time, and eventually it is closed as stale.

Three months later, the same class of bug causes another incident in a different part of the codebase. The post-mortem identifies a similar root cause. The same audit action item is created. The cycle repeats.

The fundamental problem is that manual codebase audits are expensive. A developer capable of understanding the incident well enough to audit for similar patterns needs hours or days to trace through the codebase. That time competes with feature work, and feature work almost always wins.

Systematic post-incident scanning with VibeRails

VibeRails turns the post-incident audit from a manual, time-constrained task into a systematic full-codebase scan. After an incident, run VibeRails on the repository with a focus on the category of issue that caused the outage. The AI reads every file and identifies not just the specific pattern that failed, but the broader class of weakness that allowed it.

For each incident type, VibeRails traces the root cause across files and identifies where similar logic exists elsewhere in the codebase:

• Trace the root cause across files – production incidents are rarely caused by a bug in a single file. The actual failure path involves data flowing through multiple services, middleware, and handlers. VibeRails follows these cross-file chains and maps the full contributing path, not just the line that threw the exception.
• Find similar patterns elsewhere – if a race condition in a payment handler caused an incident, VibeRails identifies other handlers that use the same concurrency pattern. If a missing null check in a user service caused a crash, the scan finds every other service that makes the same assumption about input data. The search is semantic, not syntactic – the AI understands the pattern, not just the text.
• Verify the fix does not introduce new issues – incident fixes are written under pressure. They often solve the immediate problem but create new edge cases, break an implicit contract with a downstream consumer, or add a special case that should have been handled generically. VibeRails evaluates the fix in context of the full codebase and flags potential side effects.
• Identify systemic weaknesses – the incident was the symptom; the systemic weakness is the cause. Missing input validation across an entire API layer, inconsistent error handling between services, absent retry logic on external calls, or a lack of circuit breakers in a distributed system. VibeRails identifies the architectural patterns that made the incident possible.

From post-mortem action items to evidence

Post-mortem action items are notoriously difficult to prioritise. Engineering leadership wants to invest in reliability, but competing against feature requests requires evidence: how many similar patterns exist, what is the potential blast radius, and how much effort is needed to remediate.

VibeRails provides that evidence. After a scan, the team has a structured inventory of every instance of the pattern that caused the incident, categorised by severity, with file paths and line numbers. This transforms the post-mortem action item from a vague “audit the codebase” into a specific list of 17 files that need the same class of fix, prioritised by risk.

The structured findings export as HTML for incident review meetings or CSV for import into Jira, Linear, or whatever project management tool the team uses. Each finding becomes a ticket with clear scope, estimated effort, and a direct link to the incident that motivated it. Sprint planning becomes straightforward: the team knows exactly what needs to change, where it is, and why it matters.

For teams that track incident metrics, VibeRails findings provide a leading indicator. Instead of measuring reliability reactively (mean time to recovery, incident frequency), the team can measure proactively: how many known vulnerability patterns exist in the codebase, and how quickly they are being remediated.

Common post-incident review patterns

Unhandled error propagation. An unhandled exception in a background worker crashes the process. The fix adds a try-catch. VibeRails scans every other background worker and finds 12 more with the same missing error handling. The team fixes all 12 in a single sprint instead of discovering them through 12 separate incidents.

Race conditions in concurrent operations. A double-submit on a payment form processes the charge twice. The fix adds idempotency to that endpoint. VibeRails identifies every other endpoint that modifies state without idempotency protection – eight more handlers that are vulnerable to the same class of issue.

Configuration drift. A production environment variable is missing, causing a service to fall back to a default that is wrong for production. The fix adds the variable. VibeRails scans every configuration reference and finds 15 other values that rely on defaults instead of explicit configuration, any of which could cause a similar incident.

Timeout and retry failures. An external API call hangs indefinitely because no timeout is configured. The fix adds a timeout to that call. VibeRails finds every other external call in the codebase and categorises them: calls with timeouts, calls without timeouts, calls with retry logic, and calls that fail silently on error. The result is a complete map of external dependency resilience.

Authentication bypass. A new API endpoint is deployed without authentication middleware. The fix adds the middleware. VibeRails reviews every route definition across the application and identifies any other endpoints that are missing authentication or authorisation checks, preventing the same class of security incident from recurring.

Desktop app, flexible pricing

VibeRails runs as a desktop app with a BYOK model. It orchestrates Claude Code or Codex CLI installations you already have. Your source code is read from disk locally and sent directly to the AI provider you configured – never to VibeRails servers. For teams handling sensitive incident data or operating under regulatory requirements, this means your code and your incident context stay under your control.

The post-incident review workflow is straightforward: point VibeRails at your repository, run the scan, triage findings against the incident root cause, and export the results as actionable tickets. The entire process takes hours, not the weeks that a manual audit would require.

Start with the free tier today. Run a scan on your codebase and see what VibeRails surfaces. If the findings help prevent the next incident, upgrade to Pro at $19/month per developer, or $299 for a lifetime licence.