AI agent monitoring
How's Atlan different from LangSmith or Langfuse for AI agent monitoring?
LangSmith, Langfuse, Arize Phoenix, and Braintrust focus on the LLM layer: prompts, traces, spans, latency, token cost, and evals. Atlan focuses on the governed data context layer: lineage where available, access-control enforcement through MCP, and trust signals on the assets your agent used. They're complementary.
Does Atlan replace LLM observability tools for AI agents?
No. Use an LLM observability tool for prompts, completions, traces, spans, and evals. Use Atlan for governed metadata context, access controls, and provenance review on the data side.
Does Atlan detect AI agent hallucinations?
Not directly. Hallucination scoring lives in your LLM observability tool. Atlan helps you investigate whether the agent's retrieved context was trustworthy and, where lineage exists, whether the cited fields trace back to an authoritative upstream source.
Can Atlan show me every column agents touched?
Only if your agent logs that information and lineage exists for those fields. Atlan can help you inspect lineage for the assets and columns you identify from the run. Coverage depends on the connected source and available lineage.
How do I detect when AI agent retrieved stale or low-quality data?
Review the trust, certification, ownership, and quality signals on the relevant assets in Atlan, then compare them with the retrieval step captured in your LLM observability tool.
Does this work for LangChain, LlamaIndex, and OpenAI assistants?
Yes. Atlan MCP is framework-agnostic. Your LLM observability tool handles framework-specific tracing, while Atlan provides the governed metadata and context layer underneath.