AI Observability in Practice

Governance, Compliance and Audit Readiness 

Once AI runs in production, observability picks up a second job. Auditors and internal risk teams want proof of what a model was given, and which policies were checked against what it returned. In practice, that means reconstructing the exact prompt a customer submitted on March 14, months after the fact. Sampled tracing captures one request in 100, so for the other 99 there is nothing to show. 

The AI Observability in Practice: Governance, Compliance and Audit Readiness guide is for teams who have the monitoring working and are figuring out what governance asks for next. It covers the five records an audit trail has to capture, why agent handoffs and RAG retrieval defeat conventional tracing, how to log guardrail activity including the near misses, and what full-fidelity retention changes when a regulator asks about a single transaction. 

What you'll get answers to: 

• What an audit trail must capture across the AI execution path, from system prompt and model response to tool call, agent handoff, and downstream action
• Why sampled traces and fragmented component logs fail to preserve the request-level causal chain across prompts, retrieval, agents, tools, and services
• How to turn agent handoffs and MCP activity into queryable audit evidence
• How to evaluate SOC 2 processing integrity for AI systems with probabilistic model outputs, agentic workflows, and downstream tool execution