Pattern Library
AI Governance Pattern Library
138 patterns across 16 domains. Filter by regulatory framework to find the control that satisfies a specific obligation.
A/B Model Evaluation
Upgrading the LLM powering a production AI application carries risk that cannot…
EAAPL-OBS006 · AI Cost Observability
AI inference costs have a fundamentally different cost structure than traditional compute:…
EAAPL-OBS004 · AI Incident Management
AI system failures are qualitatively different from traditional software failures.
EAAPL-OBS008 · AI Performance Benchmarking
AI system quality degrades silently between benchmarking events.
EAAPL-OBS001 · AI Telemetry Architecture
AI systems present unique observability challenges that traditional APM tooling does not…
EAAPL-OBS007 · Distributed AI Tracing
AI pipelines are not single API calls.
EAAPL-OBS003 · Hallucination Detection
Large language models fabricate plausible-sounding content with confidence.
LLM Evaluation Pipeline
Deploying an LLM update to production without a structured evaluation gate is…
EAAPL-OBS005 · Model Drift Detection
AI models degrade silently.
Prompt Drift Detection
LLM providers update their underlying models without breaking API contracts.
EAAPL-OBS002 · Prompt Monitoring
Prompts sent to large language models in production are the primary control…