Agent Observability Standard - Trace
AI agents make autonomous decisions that impact business outcomes. Without observability, enterprises can't understand, trust, or control these decisions.
Transform agent black boxes into transparent, auditable systems through comprehensive tracing.
AOS Extends Industry Standards
We already have great observability standards, so AOS doesn't introduce a new one. Instead, it extends existing industry-proven standards: OpenTelemetry and OCSF to support AI agent-specific components.
Why Agent Observability Matters
Modern agents orchestrate complex workflows: reasoning chains, tool execution, knowledge retrieval, multi-agent collaboration. When things go wrong, or right, you need to understand exactly what happened.
The Problem: Agents operate autonomously with complex internal logic. Traditional monitoring can't capture reasoning processes or decision context.
The Solution: Comprehensive observability that traces every agent action from trigger to outcome, with full reasoning context.
Observability Goals
Transparency: Complete reconstruction of agent behavior. See not just what agents did, but why they made each decision.
Security: Detect anomalous behavior in real-time. Trace attack vectors across multi-agent systems. Enforce policies at decision points.
Performance: Identify bottlenecks in reasoning chains. Optimize tool usage patterns. Monitor resource consumption across agent workflows.
Trust: Verifiable audit trails for regulatory compliance. Explainable decisions for stakeholder confidence.
How It Works
AOS provides specification for detailed tracing of agent behavior. Traces are implemented via extensions of proven industry standards:
| Standard | AOS Spec | Status |
|---|---|---|
| OpenTelemetry | AOS with OpenTelemetry | Working draft |
| OCSF | AOS with OCSF | Working draft |