Audit, Observability & Lineage for Enterprise AI Agents

Short summary

Enterprise AI agent systems need specialized observability beyond traditional APM to handle non-deterministic reasoning loops, tool calls, and sub-agent delegations. The article proposes an architecture using OpenTelemetry traces with hierarchical spans, tail sampling for cost control, and immutable cryptographically-signed lineage graphs stored in lakehouse tables. This satisfies compliance requirements from SOC 2, FedRAMP, and the EU AI Act while enabling full execution reconstruction.

• •Standard APM tools cannot reconstruct non-deterministic agent reasoning and tool-call chains
• •Proposed stack uses OpenTelemetry hierarchical traces with tail sampling for cost-controlled retention
• •Immutable, cryptographically-signed lineage graphs satisfy SOC 2, FedRAMP, and EU AI Act audit requirements

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