What your agents are doing, why they did it, and what it cost. Traces, tool calls, token spend, and drift signals stitched into one view across every framework.
AI agent observability is the practice of collecting, correlating, and analyzing traces, token and cost metrics, and behavioral signals from autonomous AI agents so teams can see what agents are doing, why, and at what cost. It extends classic application observability past a single request-response pair to cover multi-step reasoning, tool calls, and decisions an agent makes without a human reviewing each one. For a broader look at where observability fits in the stack, see our explainer on the AI agent control plane.
Application performance monitoring was built for a request-response world: one call in, one response out, latency and error rate as the headline metrics. AI agents do not work that way. A single task can trigger a chain of model calls, tool calls, and decisions, each one a place the task can go wrong.
Three properties of agents make classic APM insufficient on its own: they run long, multi-step chains instead of single requests; they are non-deterministic, so the same input can produce a different path through the same task; and token spend is a first-class signal of its own, not just a side effect of latency.
Multi-step agent chains compound failure at every hop. Tracing one request tells you nothing about whether the chain as a whole succeeded.
An agent can call different tools or reach a different conclusion on a repeat run. Uptime and latency alone cannot tell you if it behaved correctly.
A reasoning loop can burn 10x the tokens for the same task and never trip a single error or latency alert.
The two overlap but answer different questions. LLM observability watches the model call. Agent observability watches the whole acting system.
| Dimension | LLM Observability | AI Agent Observability |
|---|---|---|
| Unit of work | One model call: prompt in, completion out | A multi-step task spanning several tools and decisions |
| Traces | A single request-response pair | The end-to-end chain across every tool call and hand-off |
| Cost | Tokens per call | Tokens and dollars per agent, per task, per team |
| Failure signal | Latency, error rate, hallucination score | Behavioral drift, tool-call failures, policy violations |
| Multi-agent chains | Not applicable | Delegation and hand-offs between cooperating agents |
| Governance tie-in | Rarely connected to policy | Feeds directly into policy enforcement and audit evidence |
Six signals, tracked together, show whether an agent did the right thing at a reasonable cost, not just whether it responded.
The full multi-step chain across model calls, tool calls, and hand-offs, not just one request.
Spend attributed to the agent, the task, and the team that owns it, not a single monthly invoice line.
Time at each hop in the chain, so a slow tool call does not hide behind an acceptable total response time.
How often tool calls fail and how often the agent retries, which classic uptime metrics miss entirely.
Whether the agent is still doing what it did last week: same tools, same data access, same output patterns.
Blocked actions, approvals, and rule evaluations, which turn observability data into governance evidence.
The instrumentation layer matters as much as the dashboard on top of it. OpenTelemetry is the vendor-neutral way to emit traces, metrics, and logs from agent code, and it is the approach we recommend regardless of which platform reads the data downstream.
The GenAI semantic conventions define standard span and attribute names for model calls, tool calls, and agent operations. Emitting OTLP from your agent code means the same trace can feed monitoring, cost attribution, and compliance evidence, and it means you are not locked into a single vendor's SDK to get any of it.
Read the OpenTelemetry setup guideA crowded field, most of it built for LLM calls, not agents. Here is what to look for before you pick one.
Ingests OTLP directly and supports the GenAI semantic conventions, instead of requiring a proprietary SDK.
Traces the full chain of tool calls and decisions across a task, not only individual model calls.
Breaks token spend down by agent, task, and team, not just a total across the whole account.
Feeds the same signals into policy enforcement and compliance evidence, so monitoring and control share one system of record.
MeshAI is built as the Agent Control Plane: the same OTel-native traces, cost, and drift signals that power observability also feed policy enforcement and audit evidence, so observability and governance are not two disconnected tools bolted together.
AI agent observability is the practice of collecting, correlating, and analyzing traces, token and cost metrics, and behavioral signals from autonomous AI agents so teams can see what agents are doing, why, and at what cost. It extends classic application observability to cover multi-step reasoning, tool calls, and decisions made without a human in the loop.
LLM observability watches individual model calls: prompt, completion, latency, and token count for a single request. AI agent observability watches the whole acting system across a task: every tool call, every decision, hand-offs in multi-agent chains, cost attribution per agent and per team, and behavioral drift over time. An agent can make a dozen LLM calls and a dozen tool calls to complete one task, and LLM observability only sees the individual calls, not the chain.
Six signals matter most: traces and spans across the full multi-step chain, token and cost per agent and per task, latency at each hop, error and retry rates for tool calls, behavioral drift from an agent's established baseline, and policy events such as blocked actions or approvals. Together these show not just whether an agent responded, but whether it did the right thing at a reasonable cost.
OpenTelemetry. The GenAI semantic conventions define standard span and attribute names for model calls, tool calls, and agent operations, so instrumentation stays vendor-neutral and portable across observability backends. Emitting OTLP traces and metrics from agent code means you are not locked into a single vendor's SDK, and the same data can feed monitoring, cost attribution, and compliance evidence.
Observability data becomes audit evidence. The EU AI Act requires record-keeping and human oversight for high-risk AI systems: a record of what an agent did, when, and why. The same traces, tool-call logs, and drift signals that engineering teams use to debug agents are the raw material for Article 12 record-keeping and Article 14 human oversight, provided they are captured consistently and retained long enough to be useful to an auditor.
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Governing what agents can do is the next step. Read our guide to AI agent governance.