Is agent observability the same as LLM observability?

LLM observability tracks single calls (prompt, response, tokens, cost). Agent observability spans the whole agent run — multiple LLM calls, tool calls, retries, branching logic, and final output — and adds eval scoring across runs.

Do I need a dedicated platform or can I roll my own?

For pilots: structured logs + a basic trace ID is enough. At 10+ agents in production you will want OpenTelemetry-compatible tracing, a vector-DB-backed search over logs, and a continuous eval pipeline. AgentWorks ships these natively.

How long should I retain agent traces?

For EU AI Act Article 12, high-risk systems must retain logs for at least 6 months (longer if other law applies). For incident response and eval comparability, 90 days hot + 12 months cold is a practical baseline.

Is agent observability the same as LLM observability?

LLM observability tracks single calls (prompt, response, tokens, cost). Agent observability spans the whole agent run — multiple LLM calls, tool calls, retries, branching logic, and final output — and adds eval scoring across runs.

Do I need a dedicated platform or can I roll my own?

For pilots: structured logs + a basic trace ID is enough. At 10+ agents in production you will want OpenTelemetry-compatible tracing, a vector-DB-backed search over logs, and a continuous eval pipeline. AgentWorks ships these natively.

How long should I retain agent traces?

For EU AI Act Article 12, high-risk systems must retain logs for at least 6 months (longer if other law applies). For incident response and eval comparability, 90 days hot + 12 months cold is a practical baseline.

Glossary

What is Agent observability?

Last updated: 2026-05-26

Definition

Agent observability is the practice of capturing what an AI agent did, why it did it, and how well it did it, in a form that engineers can search and reviewers can audit. It combines three pillars: logs (the steps), traces (the causal chain across LLM calls and tools), and evals (continuous scoring of output quality).

Why Agent observability matters

Agents are non-deterministic systems that call external tools. When they fail in production, "look at the logs" rarely reveals the cause without structured tracing. Observability is also an EU AI Act requirement: Article 12 mandates record-keeping for high-risk systems, and Article 14 requires reviewers to "correctly interpret output" — both impossible without proper agent tracing.

How Agent observability works

1Every LLM call gets a unique trace ID; child tool calls inherit and extend the trace so the full causal chain is reconstructable.
2Logs capture input/output/timing/cost per step; structured fields make them queryable (slug, agent_id, user_id, tool_name).
3Traces visualise the dependency tree of an agent run — which step called which, what data flowed where, where time and tokens were spent.
4Evals run continuously against logged outputs: rule-based checks (did the JSON parse? did the email pass spam check?) and LLM-as-judge for fuzzy quality (was the answer accurate?).
5Findings feed back into prompt iteration, tool choice, and human-review thresholds — closing the observability → improvement loop.

Examples

A customer-support agent's P95 latency suddenly spikes; the trace shows a single retrieval call ballooned from 200ms to 4s because the vector DB ran out of memory.
An LLM-as-judge eval flags 8% of an outbound-sales agent's drafts as "off-tone" — the team adjusts the prompt and the score drops to 1% within a week.
A compliance reviewer audits an agent decision and the trace reveals it used a stale document from cache instead of the current policy version.

References

FAQ

Agent observability — common questions

Is agent observability the same as LLM observability?: LLM observability tracks single calls (prompt, response, tokens, cost). Agent observability spans the whole agent run — multiple LLM calls, tool calls, retries, branching logic, and final output — and adds eval scoring across runs.
Do I need a dedicated platform or can I roll my own?: For pilots: structured logs + a basic trace ID is enough. At 10+ agents in production you will want OpenTelemetry-compatible tracing, a vector-DB-backed search over logs, and a continuous eval pipeline. AgentWorks ships these natively.
How long should I retain agent traces?: For EU AI Act Article 12, high-risk systems must retain logs for at least 6 months (longer if other law applies). For incident response and eval comparability, 90 days hot + 12 months cold is a practical baseline.