You've rolled out an AI agent or copilot on top of your data warehouse, and it's writing plausible SQL that hits the wrong table, or the right table with the wrong join, or a metric definition that doesn't match what your finance team means by "active users." You point it at your catalog, thinking that will fix things. It helps some, but not as much as you expected.
The agent isn't reading your catalog the way your analysts do. It never opens a browser tab and scans a page. It doesn't recognize a well-written description the way a person skimming for context does. It calls an API, receives a payload, and acts on its contents.
Most catalog evaluations focus on features built for people browsing a UI, which is why they miss what agents actually need. This is a checklist for the specific slice of catalog functionality agents actually use: governed programmatic access, lineage, semantics, quality signals, and memory. Use it to check a catalog before you let an agent touch production data, alongside whatever broader evaluation you run for human users.
Key Takeaways
A human analyst opens the catalog, searches, reads a description, clicks into lineage, and decides what to trust. An agent does none of that browsing. It calls a tool, gets structured metadata back, and has to decide what to do with it in the same turn, with no chance to come back later and check. If the tool call returns only a table name, the agent has to infer everything else from context it doesn't have, and that's where wrong joins and stale metrics come from.
This is why the interface matters as much as the content behind it. OpenAI's Kepler agent, described in a joint case study with OpenMetadata, was built to answer data questions using table metadata, human annotations, and lineage retrieved through structured calls rather than a search box. The design reflects how an agent actually consumes a metadata layer: as a set of typed, queryable objects rather than a page of prose.
If you're evaluating a catalog for agent use, compare what the API returns with what a person sees in the browser. Does the catalog expose the same core components of a traditional data catalog programmatically, at the same fidelity a person gets in the browser? If the API is a thin, stripped-down mirror of the web app, the agent will work with less information than your analysts do, and its answers will reflect that gap.
The mechanism that matters here is the Model Context Protocol (MCP), which lets an agent call a catalog's metadata and search functions directly as tools, rather than through a custom integration built for a single model. OpenMetadata's MCP server exposes catalog search, lineage, and glossary lookups as MCP tools. OpenMetadata documents that its MCP server uses the same role-based access control engine as the human UI. Check this before rollout rather than after. An agent with a service account that bypasses RBAC can read tables a given team was never supposed to see, and it will hand that data to whoever asks, with no separate approval step in between.
When you evaluate a catalog for agent readiness, check three things at this layer: does it have an MCP server or an equivalent tool-calling interface; does that interface run on the same permission engine as human access; and does it cover search, lineage, and glossary lookups, not just basic asset metadata. OpenMetadata's MCP implementation sits on top of 130+ connectors, so the answer for a given warehouse or BI tool is usually already built rather than something your team has to write.
An agent asked what changed in a metric needs to trace that metric backward through transformations to source tables, and it needs that trace at the column level, not the table level. Table-level lineage tells you dataset A feeds dataset B. Column-level lineage tells you which specific field in A produced the specific field in B that's wrong. For automated troubleshooting or root-cause questions, that level of detail determines whether the agent identifies the actual broken field or points to the wrong one.
Column-level lineage also functions as a safety check before an agent writes anything back. Before an update propagates, the lineage graph shows which downstream assets depend on the field being changed, allowing an agent or the human reviewing its proposed action to see the blast radius first.
Give a capable model a warehouse schema and a plain-English question, and it will often produce SQL that runs without error and returns the wrong answer, because column names and table structures don't carry business meaning. Spider 2.0, an enterprise text-to-SQL benchmark, found leading models like GPT-4o and Claude Sonnet dropping to around 10% execution accuracy on real enterprise schemas without semantic grounding. As described in Collate's write-up of Anthropic's internal testing, accuracy on enterprise data questions moved from roughly 21% to the high 70s and above once the model had grounded semantic context to work with rather than raw schema alone.
That gap is what a semantic layer closes. Glossary terms and metric definitions clarify the difference between how finance defines revenue and the raw column name for revenue. Without that layer, the agent is pattern-matching against column names. With it, the agent has an actual definition to check its work against. If you're scoring a catalog for agent readiness, check whether glossary terms are just documentation strings or linked, machine-readable objects that the agent's tool calls can retrieve and reason over.
A catalog that tells an agent a table exists but not whether last night's load succeeded is withholding half the information the agent needs to decide whether to trust the data. Freshness, test results, and severity signals attached at the asset level let an agent check, before it answers, whether the table it's about to query passed its most recent tests and when it last updated. This check is what stops an agent from confidently answering a question with data that's three days stale or failed a null check overnight.
The practical requirement: quality and freshness metadata needs to live at the same asset level and be retrievable through the same programmatic interface as everything else, not siloed in a separate observability tool the agent's tool calls never reach.
Every other feature on this list answers a question about a single moment. Memory determines whether what the agent learns in one moment carries over to the next. Without it, one analyst corrects the agent's understanding of a metric on Monday, and a different analyst gets the same wrong answer on Tuesday because the correction didn't persist. OpenAI's write-up of Kepler describes memory as one layer within its context model, alongside table metadata and human annotations, because institutional knowledge that isn't captured somewhere has to be relearned from scratch every session.
A governed home for memory means those corrections, past answers, and accumulated context live in a place with the same access controls and audit trail as the rest of the catalog, rather than in a chat log or a side tool nobody else can easily query. Check whether a catalog you're evaluating has any concept of persistent, queryable memory at all, or whether every agent session starts from zero.
Agents frequently need to find the right asset from a natural-language description of what a business user wants, not from a table name they already know. Semantic search built for meaning, not keywords, matches on what a term means rather than what it's spelled like, which matters when the user's question uses "customer churn" and the actual column is named cust_attrition_flag. A catalog without this layer forces the agent to guess at string matches, which fails most often on the naming that least resembles the query, and that's frequently the naming the business user actually used.
Before you sign off on a catalog for agent use, confirm it has:
This list is deliberately narrower than a full catalog feature comparison. It covers metadata foundational to agentic analytics, specifically: the slice of data that an agent's tool calls actually touch, distinct from the broader question of what AI needs from a data catalog, which also encompasses organizational readiness, data contracts, and governance processes. If you want that broader picture, the four pillars of AI-ready data cover it, and the shared metadata infrastructure agents need explains why a single context layer beats stitching these six features together from separate tools, where a gap like RBAC enforced in the catalog but not propagated to a separately integrated lineage tool an agent also calls can quietly undo the access control you thought you had. Run this checklist before greenlighting an agent against your current catalog, and revisit it whenever you add a new tool call or connector to the agent's setup. Metadata management connects discovery, lineage, quality, and governance into a single system, and an agent's answers will be only as reliable as the weakest link in that chain.
Humans browse a catalog through a UI: searching, reading descriptions, clicking through lineage graphs before deciding what to trust. Agents interact with the same underlying data through structured tool calls, typically over an API or MCP server, in a single turn with no chance to look around further. The features that matter to agents are those exposed programmatically at full fidelity, not the visual layer built for browsing.
Yes. Even a read-only agent needs lineage to answer questions about provenance, freshness, or why a number changed, since tracing a metric back to its source tables is how it explains or verifies an answer. Lineage also functions as a trust signal: an agent that can trace where a number came from gives a reviewer something concrete to check, unlike one that returns an answer with no supporting trail.
It can, through custom API integrations or exports, but that usually means building and maintaining a bespoke connection for each model or agent framework rather than reusing a standard interface. An MCP server gives agents a consistent way to call catalog functions like search, lineage, and glossary lookups, with access control enforced at the same layer humans use, which is harder to replicate reliably with one-off integrations.
A glossary defines terms and metrics: what "revenue" means and which columns support it. Memory captures the corrections, approvals, and exceptions that arise during actual use, such as an analyst telling the agent to exclude a specific table from a calculation due to a known data issue. A glossary supplies definitions. Memory stores the operational exceptions and corrections a team has already worked out, so an agent doesn't have to be told the same thing twice.
It depends on how the agent is used. An agent answering ad hoc questions against a table that refreshes nightly only needs freshness and test results current as of that refresh; daily signals are sufficient. An agent embedded in a pipeline or alerting workflow where staleness has immediate consequences needs those signals updated closer to real time. The requirement isn't a fixed cadence. Check that the signal exists at the asset level and is at least as current as the data the agent is querying.
