Agent Observability — Trace Every AI Agent Action
Reconstruct full agent sessions — tool calls, reasoning steps, handoffs and escalations — with OTel-native distributed tracing.
99.9%
Uptime SLA
< 5ms
Trace overhead
SOC 2
Certified
OTel
Native
Agent observability
Agent observability is the practice of tracing every action an AI agent takes — tool calls, API requests, reasoning steps, handoffs, and escalations — so teams can debug failures, attribute cost per agent, and prove compliance in regulated environments.
Why agents need dedicated observability
Instrument once, observe everything
One OTel SDK, one OTLP exporter. No proprietary agents or middleware sitting in the critical request path.
Eval scores on production traffic
Faithfulness, answer relevancy, and hallucination rates measured on live requests — not just curated test sets.
Compliance evidence built in
Structured audit logs formatted for HIPAA, EU AI Act, DORA, and MAS FEAT. No manual export, no post-processing.
Why chatbot tracing falls short
LLM chatbot observability (prompt in, completion out) does not cover agents — agents make tool calls, spawn sub-agents, loop conditionally, and can run for minutes. A single user session may involve dozens of LLM calls, hundreds of API invocations, and multiple agent handoffs. Without distributed tracing, you see a failure but not why.
OTel spans map to agent structure
OTel span hierarchy maps naturally to agent architecture: root span = user session, child spans = agent steps, leaf spans = tool calls and LLM completions. Every agent action gets a span ID, parent ID, duration, token cost, and outcome. Session replay becomes possible.
Session-level audit for regulated teams
For regulated deployments — clinical AI agents under HIPAA, trading or fraud agents under DORA, and EU AI Act Annex III systems — session replay is not a convenience feature, it is a compliance requirement. Perimattic AI Suite persists the full span tree with PHI/PII redaction applied at capture time: input payloads, tool call arguments and results, model version and parameters, intermediate reasoning steps, and per-agent token cost. A compliance officer or auditor can reconstruct any session weeks or months after it ran, down to the exact tool call that produced a disputed output.
What agent observability captures
Everything your team needs to instrument, evaluate, and audit AI systems in production — with evidence that satisfies your compliance requirements.
Tool Call Traces
Every external API call, database lookup, and code execution an agent makes, with latency and result.
Multi-Agent Handoffs
When one agent delegates to another, trace the handoff span, transferred context, and outcome.
Per-Agent Cost Attribution
Token spend broken down by agent role — orchestrator, researcher, writer, executor.
Reasoning Step Visibility
Intermediate reasoning steps (chain-of-thought, ReAct loops) captured as spans.
Deadlock Detection
Identify agent loops, stalled handoffs, and coordination failures in real-time.
Common questions, answered
Answers to the most common questions about this regulation, what it requires, and how AI observability helps you meet it.
What is agent observability?
Agent observability is distributed tracing applied to AI agents. Where standard LLM monitoring tracks prompt-response pairs, agent observability traces the full execution graph — tool calls, sub-agent delegation, state transitions, and handoffs — using OTel spans with gen_ai.* semantic conventions.
How is agent observability different from LLM observability?
LLM observability covers single model calls: prompt, completion, latency, cost. Agent observability covers orchestration: which tools were called, in what order, by which agent, with what outcome. Multi-agent systems need both layers.
Which agent frameworks does Perimattic AI Suite support?
Any framework that emits OTel spans — LangChain, LangGraph, CrewAI, AutoGen, AWS Bedrock Agents, and custom agent loops. If your framework does not emit OTel natively, you can annotate spans manually using the standard OTel SDK.
How do I get started with agent observability?
Install the OTel SDK, configure the OTLP exporter to point at Perimattic, and annotate agent spans with gen_ai.* attributes. For LangChain, a callback handler handles annotation automatically. Full setup takes under 30 minutes for a single-agent system.
Can agent traces satisfy compliance requirements?
Yes. Agent traces are immutable, structured, and timestamped — they serve as audit evidence for HIPAA, EU AI Act (post-market monitoring), DORA (ICT incident documentation), and MAS FEAT (accountability trails).
Related pages
Dig deeper into the topics that matter most for your AI observability stack and compliance posture.
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