Agentic AI Observability and Auditability Architecture

Agentic AI systems can reason, plan, invoke tools, delegate tasks, access enterprise data, and perform actions across applications with limited human intervention. These characteristics create observability and auditability challenges that are not fully addressed by traditional application logging or model monitoring. This paper proposes an Agentic AI Observability and Auditability Architecture that captures agent identity, objectives, planning steps, prompts, model versions, retrieved context, tool calls, policy decisions, approvals, execution outcomes, exceptions, and human interventions. The architecture introduces event schemas, trace correlation, tamper-resistant audit records, risk-aware telemetry, privacy controls, behavioral baselines, anomaly detection, and forensic reconstruction. It also defines architectural layers for telemetry generation, collection, normalization, policy evaluation, storage, analytics, dashboards, alerting, and audit evidence. The proposed design enables enterprises to operate autonomous and multi-agent systems with stronger transparency, accountability, incident response, regulatory readiness, and operational control.