AI agent observability
Why AI agents need both observability layers
Two questions matter when an agent answers a user:
- What did the agent do?
- What data context did the agent rely on?
Your LLM observability tool answers the first question. It captures prompts, completions, traces, spans, tool-call timing, and evals.
Atlan helps answer the second question. It gives your agent governed metadata through MCP, enforces existing access controls, and lets you inspect lineage and trust signals on the assets the agent used.
Neither layer is sufficient on its own. An LLM trace can tell you that an agent called a tool and returned an answer. It usually can't tell you whether the field behind that answer came from a trusted upstream source, whether the lineage is complete, or whether the agent ought to have been allowed to retrieve that metadata in the first place.
LLM observability vs data-side observability
| Question | LLM observability tools | Atlan |
|---|---|---|
| What did the agent say? | Prompt and completion capture | Out of scope |
| How long did each step take? | Trace and span timing | Not the primary system for this layer |
| Did a tool call succeed? | Tool-call status and payload, if instrumented | Access enforcement and Atlan-side handling for MCP calls |
| What data context did the agent retrieve? | Retrieval payload, if instrumented | Metadata context plus lineage and trust signals where available |
| Was the agent allowed to see that metadata? | Usually out of scope | Access-control enforcement on MCP |
| What is the provenance of a cited field? | Partial, depending on instrumentation | Lineage where available |
| Was the source data fresh and trusted? | Usually out of scope | Data quality and trust signals on the relevant assets |
| Who changed this metadata, and when? | Out of scope | Asset activity history for metadata changes |