Evaluation, Observability & Reliability
Teach how AI systems are measured, debugged and operated safely in production.
Why This Matters
You cannot ship a probabilistic system on logs meant for request/response APIs. Evals, traces, and SLOs are how architecture becomes operable.
Architecture Position
Platform Layer: gateways, observability, evals, and infrastructure sit beside every runtime hop. See it on the architecture map.
Start Here
Core Concepts
Evaluation
- Offline evals
- Online evals
- Golden datasets
- LLM-as-judge
- Agent evals
- RAG evals
- Security evals
Observability
- LLM traces
- Agent traces
- Tool traces
- Distributed tracing
- Token usage
- Cost telemetry
Reliability
- Failure taxonomy
- SLOs
- Model fallbacks
- Circuit breakers
- Retries
- Graceful degradation
Operations
- Release gates
- AI CI/CD
- Incident response
- Debugging
- Production monitoring
Advanced
Advanced material
Deeper guides for this domain are on the way.
Coming SoonArchitecture Patterns
Applied patterns for this domain live in the pattern library.
Browse architecture patternsCase Studies
Enterprise walkthroughs that apply this topic in a full system.
Browse case studiesRelated Topics
AI Platforms & Infrastructure
Infrastructure needed when AI moves from an app into an enterprise-wide capability.
Open →Agents & Orchestration
What agents are, where they are appropriate, and how autonomous systems should be structured.
Open →AI Security & Trust
Threats created by probabilistic systems that retrieve data and take actions.
Open →Distributed Systems for AI
Classic production architecture connected to modern AI systems.
Open →Roadmaps
This topic appears in the Enterprise AI Architect path.
Follow the Enterprise AI Architect roadmap