Adam BlansettSenior Full-Stack & AI Engineer
Interactive Architectural Self-Audit

Production-Readiness Grader

Evaluate your web application, microservices, or AI pipelines across 8 core production disciplines. Answer each question candidly to receive an instant reliability score, risk tier, and targeted remediation recommendations.

Assessment Progress0 of 8 criteria evaluated

Evaluation Criteria Questions

01. Observability & Error Telemetry

Centralized Error Tracking & Sentry Release Tagging

Is centralized exception tracking (e.g., Sentry, Datadog) active in production with release versioning, environment isolation, and source maps?

02. Automated Testing & Quality Gates

Automated Test Gates in CI/CD

Does an automated test suite (unit, integration, or contract) execute on every pull request and strictly block merging when tests fail?

03. Security & Secret Governance

Zero Hardcoded Secrets & Vault / KMS Storage

Are all production credentials, database passwords, and third-party API keys stored in encrypted secret managers rather than committed code or plaintext files?

04. Data Persistence & Migrations

Idempotent Migrations & Connection Pooling

Are database schema changes automated with idempotent migrations, connection pooling configured (PgBouncer/RDS Proxy), and automated point-in-time backups verified?

05. Resilience & Timeout Policies

Bounded HTTP Timeouts & Queue Dead-Lettering

Do all downstream HTTP and RPC calls specify explicit connection/read timeouts, and do background worker queues have dead-letter routing for poison-pill messages?

06. Deployment & Rollback Safety

Immutable Builds & Fast Rollback Capability

Can your team roll back a failed deployment to the prior immutable build within 2 minutes without requiring a new code commit or recompilation?

07. API Contract & Schema Safety

Strict Runtime Type & Schema Validation

Are inbound API request bodies, webhooks, and environment variables parsed through strict runtime validation schemas (Zod, JSON Schema, Envalid) before processing?

08. AI & LLM Production Guardrails

Structured Outputs & Token Budget Limits

If utilizing LLMs, are outputs parsed with deterministic JSON schemas, prompt inputs bounded, and fallback handlers implemented for model rate limits or outages?