What is Agent Observability?

Agent observability vs LLM observability

They are adjacent but they watch different layers. LLM observability traces the model: the prompt that went in, the completion that came out, token counts, and latency for a single call — invaluable in development and debugging, but scoped to one request to one model.

Agent observability watches the agent: the full multi-step execution — reasoning, the sequence of tool calls and their results, what was retrieved, which policies fired, the cost of the whole session, and whether the task actually got done. An agent is more than its model, so observing the model is not the same as observing the agent.

The distinction matters because agents fail in places a model trace never shows: the right model called the wrong tool, lost context three steps in, or took an action it was not authorised to take. Agent observability is the production layer that makes those failures visible — which is why it sits alongside agent evaluation, not under LLM observability.

Why traditional monitoring is not enough for agents

Traditional application monitoring tracks metrics like CPU usage, response times, error rates, and request throughput. These are necessary but insufficient for AI agents.

Agents make autonomous decisions. They choose which tools to call, what data to retrieve, how to process results, and what to return. A 200 OK response tells you the request succeeded — but not whether the agent accessed data it should not have, hallucinated a factual claim, or consumed ten times the expected token budget.

Agent observability fills this gap by capturing the full trace of an agent's execution — every reasoning step, every tool call, every retrieval, and every token spent.

The three pillars of agent observability

Agent observability builds on the traditional three pillars — logs, metrics, and traces — but extends them for agentic workloads.

Traces capture the end-to-end journey of a single agent execution: the user input, the model's reasoning steps, each tool call and its response, evaluation scores, and the final output. Traces are the most important observability primitive for agents because they reveal the causal chain of decisions.

Metrics track aggregate operational data: request counts, latency percentiles, token consumption, error rates, cost per agent, and policy violation rates. Metrics power dashboards and alerting.

Logs record individual events: a tool call was made, a policy was evaluated, a credential was rotated, an error occurred. Logs provide the detail needed for debugging and forensic investigation.

What agent-specific telemetry looks like

Beyond standard metrics and logs, agent observability captures data unique to agentic systems.

Tool call telemetry records which tools each agent invoked, with what parameters, and what results were returned. This is essential for understanding agent behavior and detecting misuse.

Token usage telemetry tracks how many tokens each agent consumes per request, per day, and per task — enabling cost attribution, budget enforcement, and anomaly detection.

Quality signals capture whether each step actually worked: did the agent select the right tool, pass valid arguments, stay grounded in retrieved context, and complete the task. Scored by evals on sampled live traffic, these signals turn raw traces into a measure of whether an agent is getting better or worse.

Reasoning traces capture the model's intermediate thinking steps, which help explain why an agent took a particular action — though they should not be treated as reliable audit records on their own.

Observe, evaluate, optimize: turning traces into better agents

Observability is not the goal — improvement is. Capturing traces is step one of a loop: observe what the agent did, evaluate whether it did it well, then optimize the prompt, model, tools, or routing and watch the next traces to confirm the change worked.

This is why observability and evaluation belong together. A trace tells you the agent called three tools and returned an answer; an eval scored on that trace tells you whether the answer was correct, grounded, and worth the cost. Run those evals on a sample of live traffic and every production session becomes evidence of whether the agent is improving or regressing.

The payoff compounds: failed and suspicious sessions surfaced in observability flow back into your eval set, so the test suite tracks production reality instead of drifting away from it.

Building an agent observability pipeline

An agent observability pipeline typically includes four stages.

Instrumentation adds telemetry collection to agent code — either through SDK integrations, middleware, or a proxy layer that intercepts agent interactions transparently.

Collection aggregates telemetry from all agents into a central system, normalising data across different frameworks and models.

Evaluation scores a sample of that telemetry — rule-based checks and LLM-as-a-judge evals that measure task success, groundedness, and quality, not just operational health.

Action connects results back to the team — alerting on-call when a rolling metric degrades, and feeding failed sessions back into the eval dataset so the next iteration is measured against real production behaviour.

The pipeline should be framework-agnostic, so that agents built on LangChain, CrewAI, or any other framework produce consistent telemetry.

Key metrics to track for AI agents

Every organisation monitoring AI agents should track a core set of metrics.

Reliability: task completion rate, error rate, timeout rate, and fallback frequency.

Performance: end-to-end latency, time-to-first-token, and tool call latency.

Cost: tokens consumed per agent, per task, and per user — mapped to dollar cost.

Quality: task success rate, tool-selection accuracy, groundedness, and hallucination rate — scored by evals on sampled traffic.

Drift: week-over-week change in success, cost, and latency, so a silent regression from a model update or a shifted tool API is caught early.

These metrics should be visible to engineering and product teams through shared dashboards, broken down by agent, route, and model.

How do you monitor AI agents in production?

Monitor agents by instrumenting traces and spans for every model call, tool invocation and retrieval, then running evals on a sample of live traffic. Track task success, tool-call accuracy, drift, latency and cost per request on dashboards broken down by agent, route and model, with alerts when a rolling metric degrades.

The reason monitoring matters more for agents than for ordinary software is that they degrade without any code change — a provider ships a model update, a tool's API shifts, or user behaviour drifts. Failed and suspicious sessions surfaced in monitoring should flow straight back into your eval dataset, so production reality keeps the test set honest.

For the evaluation methods that run on that sampled traffic, see the Agent Evals and LLM-as-a-Judge guides.

What are the best AI agent observability tools?

AI agent observability tools fall into three groups: open-source tracing platforms (Langfuse, Arize Phoenix), commercial suites that pair tracing with evals (LangSmith, Datadog LLM Observability, Helicone), and agent-quality layers that add per-agent evaluation, cost attribution, and improvement tracking on top of traces. The table below compares the main options.

Most teams already collect traces somewhere; the common gap is the agent-level view — whether a specific agent is getting better, worse, or more expensive over time, across sessions, sub-agents, and tool calls. Prefactor sits at that layer and works with the traces you already collect rather than asking you to switch stacks.

How is agent observability different from LLM observability?

LLM observability focuses on individual model calls — the prompts, tokens, latency and cost of single completions. Agent observability covers the multi-step reality on top: full trajectories, tool calls, sub-agents, retries and the actions an agent takes, so you can see not just what the model said but what the agent did across an entire session.

That difference is why multi-agent observability is its own challenge: when agents call other agents, you need trace context propagated across the whole chain to attribute a failure to the right step. A single-call view cannot tell you which sub-agent or tool broke a multi-step task.