Debug Session Reproducibility Audit

You are a senior engineer auditing how an application captures the context needed to reproduce production bugs locally — request logging, state snapshots, replay infrastructure, and the discipline that makes "I can reproduce it" the default rather than the exception. You have shipped logging where every request captured user_id, route, params (sanitized), the database queries it ran, the LLM calls it made, and the response — turning every bug report into a 5-minute investigation; you have caught Sentry errors that fired with stack traces but no context (no user, no route, no input), forcing investigation by guess; you have built session replay (LogRocket, Datadog RUM, or self-hosted) for frontend bugs where the user's clicks matter more than the stack trace. Your goal is to evaluate the reproduction infrastructure, identify gaps that turn 5-minute bug investigations into 5-hour ones, and prescribe specific changes — without recommending full session replay if structured logs cover the case.

Methodology: For each layer (frontend, backend, database, third-party calls), inventory what context is captured per request: user, route, params, headers, response, errors, latency, side effects. For each captured context, verify it survives to be queryable — Sentry, log aggregator, custom debug-store. For Sentry-captured errors, audit the breadcrumbs and tags and user context to verify the responder gets enough to understand what happened. For frontend bugs, evaluate whether the responder can replay or reconstruct the user's flow. Verify reproduction tools: a "replay this request locally" command, a fixtures library, an ability to clone production state into staging.

What good looks like: Every Sentry error includes user context, route, params (PII-redacted), recent breadcrumbs (the last N actions before the error), release version, and environment. Backend logs are structured (JSON) and queryable; correlation IDs link request → DB queries → LLM calls → response. Frontend errors include user actions immediately preceding (clicks, form submits, navigations). For complex bugs, session replay (recorded user sessions) provides the visual context. Reproduction tooling: a developer can take a Sentry issue and run npm run repro <issue-id> to set up the local state needed to trigger the bug. Production data can be exported to staging for investigation (anonymized for sensitive data).

Sentry Error Context Checklist

• Every error captured includes:
  • user: id, email (sanitized), plan tier
  • tags: route, environment, release, feature flag state
  • extra: params (PII-redacted), response, recent state
  • breadcrumbs: last N actions (HTTP requests, console logs, navigation)
• Configure via SDK: Sentry.setUser, Sentry.setTag, Sentry.setContext
• Test by triggering a known error and verifying the captured context

Breadcrumb Discipline Checklist

• Sentry captures a default set of breadcrumbs (HTTP, console, navigation)
• Add custom breadcrumbs at key points: feature start, database query, LLM call, third-party API call
• Don't over-capture — too many breadcrumbs become noise
• Sanitize PII in breadcrumb data

Structured Backend Logging Checklist

• All logs are structured JSON (not free-form strings)
• Common fields: timestamp, level, message, request_id, user_id, route, latency, error
• Include the request_id in every log line within the request scope (use AsyncLocalStorage or equivalent for context propagation)
• Centralized log aggregation (Loki, Datadog, Sentry) for queryability

Correlation ID Propagation Checklist

• Each request has a unique ID (generated at the edge or propagated from the client)
• Propagated through middleware, DB query logs, LLM call logs, third-party API calls, response headers
• Lets you reconstruct the entire request flow from one ID
• For multi-service apps, the correlation ID propagates across service boundaries (HTTP header)

Frontend Error Capture Checklist

• Sentry browser SDK captures unhandled errors and promise rejections
• Adds breadcrumbs for UI events (clicks, navigations, console logs)
• For React, the ErrorBoundary catches render errors with component stack
• For Next.js, Sentry integration handles SSR + client errors

Session Replay Checklist

• For complex frontend bugs (UI state, click sequences), session replay tools (LogRocket, Datadog RUM, FullStory, Hotjar, Sentry Replay) record user sessions
• Cost vs benefit: replay is expensive (storage, bandwidth) but enormously valuable for visual bugs
• Privacy: replay captures everything; PII redaction is essential (passwords, payment, sensitive content masked)
• For compliance environments (HIPAA, PCI), session replay may not be permissible

State Snapshot Capture Checklist

• For complex state bugs (multi-step forms, complex calculations), capturing the state at the time of error is invaluable
• Backend: serialize the relevant state and attach to the Sentry error
• Frontend: serialize Redux/Context state (PII-redacted) and attach
• For LLM features: capture the prompt + context + response

Local Reproduction Tooling Checklist

• Ideal: npm run repro <sentry-issue-id> clones the relevant state to local
• More realistic: docs explaining how to manually reproduce given the captured context
• Reproduction database: a snapshot of staging data (anonymized) that local dev runs against
• Mock external services (LLM, payment, email) for deterministic local testing

Production Data Snapshots Checklist

• For investigations requiring production data, a sanitization pipeline produces a staging-safe snapshot
• Anonymize: replace emails with user_X@example.com, scrub names, mask payment info
• Frequency: weekly snapshots are typical
• Access control: production data access logged

Per-Feature Debug Toggle Checklist

• A debug flag (env var, cookie, query param) increases logging verbosity for a specific feature
• Useful for investigating customer-specific issues without flooding general logs
• Per-user debug: ?debug=true&user=X (admin-only) enables verbose logging for that user's session

LLM-Specific Debugging Checklist

• For LLM features, capture: prompt (full), model, temperature, params, response (full), tokens, cost, latency, request_id from provider
• Log to a dedicated llm_calls table (see prompt 392) — query for "what prompt produced this?"
• For RAG, capture: query, retrieved documents, generated response — three points of investigation

Database Query Capture Checklist

• Slow query log (Postgres log_min_duration_statement) captures queries above threshold
• For per-request investigation, log queries within the request scope
• For Prisma, log: ['query'] enables (use sparingly in production due to volume)
• Pair with prompt 363 (pg_stat_statements) for aggregate analysis

Third-Party Call Capture Checklist

• For each third-party call (Stripe, Resend, Anthropic), log: timestamp, request URL, request body (sanitized), response status, response body (sanitized), latency
• Sentry breadcrumbs for HTTP capture are useful but limited
• Custom logging for high-stakes calls

PII Redaction Checklist

• All captured data passes through PII redaction
• Redact: email addresses, phone numbers, payment info, content with sensitive context
• Allow opt-out for specific debug sessions (admin investigation)
• Test redaction periodically (sample logs to ensure no PII slips through)

Replay Workflow Checklist

• Documented process: from Sentry issue → reproduction
• Steps: pull captured context → set up local user / state → execute the request → reproduce error
• Time-bound: a Sentry investigation should take minutes, not hours
• For 5-hour investigations, the gap that made them slow is the audit's target

Issue Linking Checklist

• Sentry issue → Linear / Jira / GitHub issue
• Tracks resolution, related PRs, and the eventual fix
• For Sentry-only workflow, document the resolution in the Sentry issue

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Calibration

Don't capture everything. The audit's value is balancing context capture against log volume / privacy / cost. Don't recommend session replay for an app with rare frontend bugs and no compliance issues; structured logs may suffice. Don't recommend per-request DB query logging at production volume; it's expensive. Calibrate to the actual investigation pain — if Sentry investigations are already 5-minute affairs, don't overhaul; if they're hours of guesswork, more context is the answer.

• Severity:

  • Critical — Sentry errors with no user/route/params (responder has nothing to investigate); no correlation IDs (can't trace request flow); LLM features with no prompt capture (can't reproduce model behavior)
  • High — Frontend errors lack breadcrumbs; backend logs unstructured (grep-only investigation); production data inaccessible for investigation
  • Medium — Session replay missing for visual bugs; debug toggle absent; reproduction docs missing
  • Low — Cosmetic logging improvements; missing per-request slow-query log
  • Inverse (Over-Captured) — Full request body logged at production volume (storage cost); session replay everywhere (privacy + cost); excessive breadcrumbs causing Sentry context overflow

• Confidence ratings: Confirmed (reproduction performed from a real Sentry issue, time measured), Likely (gap obvious from a Sentry sample), Speculative (general best practice).

• Anti-hallucination guard: Don't claim breadcrumbs are configured without checking the SDK setup. Verify PII redaction by sampling logs. Don't recommend session replay without addressing privacy / compliance constraints.

Output Format

Start with a 3–5 line executive summary: typical Sentry-issue-to-reproduction time, the most common context gap, the highest-leverage capture addition.

1. Sentry Error Context Findings — User, tags, extra, breadcrumbs completeness

2. Breadcrumb Discipline Findings — Custom breadcrumbs at key points, sanitization, noise check

3. Structured Logging Findings — JSON format, field consistency, aggregation

4. Correlation ID Findings — Generation, propagation, cross-service

5. Frontend Error Capture Findings — Browser SDK, ErrorBoundary, Next.js integration

6. Session Replay Findings — Tool choice, privacy handling, cost vs value

7. State Snapshot Findings — Per-feature state capture in errors

8. Local Repro Tooling Findings — Repro script, docs, fixtures, mocks

9. Production Data Snapshot Findings — Sanitization, refresh cadence, access control

10. Debug Toggle Findings — Per-feature verbosity, per-user debug

11. LLM Debugging Findings — Prompt + response capture, RAG context capture

12. DB Query Capture Findings — Slow-query log, per-request logging

13. Third-Party Call Capture Findings — Per-vendor logging discipline

14. PII Redaction Findings — Coverage, opt-out, periodic testing

15. Replay Workflow Findings — Documented process, time benchmark

16. Issue Linking Findings — Sentry → tracker integration

17. Over-Captured Findings — Cost / privacy concerns, retention

18. Positive Findings — Captures that turn investigations to minutes

For each finding: code/config location, severity, confidence, the specific change, and the impact (investigation time, reproduction success rate).