Your catalog works. Analysts find the right tables, owners are listed, and lineage answers "where did this come from" in a few clicks. Then someone points an AI agent at the same metadata and asks for last quarter's revenue. The agent finds a table, writes a query, and returns a confident number. Finance says it's wrong.
The catalog did its job, and the agent found the asset it was looking for. It could not know which revenue definition your company means, or that an analyst corrected this exact answer last week. That difference separates a data catalog from a context platform, and you resolve it by extending the catalog you already run.
A data catalog inventories, describes, and governs data assets for people. The key features of a data catalog fall into eight groups: discovery, metadata, lineage, quality, glossary, governance, collaboration, and APIs.
Each group solves a daily problem for a human reader. Search finds the table, and metadata says what is in it and who owns it. Lineage shows which dashboards break if you change a column, and quality tests tell you whether to trust today's load. The glossary gives a steward a place to write down what a term means, and governance policies decide who sees what.
None of that goes away when agents arrive. Agents need the same inventory, ownership, lineage, and policies. A context platform has to start from that foundation.
Collate is an Agentic Data Platform built on OpenMetadata, an Apache 2.0 project. Collate's product page reports 4,000+ enterprises using OpenMetadata (self-reported), with 130+ native connectors. Connectors feed the metadata graph that metadata, meaning and memory attach to.
Agents fail in three places: meaning , memory, and governed access.
Meaning. A glossary entry is a sentence written for a person who can ask a follow-up question. "Revenue" can mean net revenue (Finance), gross revenue (Sales), or attributed revenue (Marketing), and all three can share one column name across different tables. A person walks down the hall and asks, while an agent picks one and never says which. Retrieval over glossary text can surface the sentence, but it doesn't guarantee the model resolves which definition applies to this question, this team, and this table. The semantic context layer explainer walks through the revenue example.
Memory. Say an analyst corrects an agent's answer on Tuesday. On Thursday, a different analyst asks the same question and gets the same wrong answer because the correction lived in a chat window and never reached the metadata. Catalogs record what is true about assets. A catalog with no field for decisions, corrections, and the reasons behind them leaves a correction nowhere to go.
Governed agent access. People reach a catalog through a UI with a login. Agents reach it through APIs and tool calls, often on behalf of a user. If the agent's access path doesn't carry that user's permissions, you get either a blocked agent or a leaky one.
A context platform keeps the metadata graph you have and adds meaning and memory to it, with access control applied across all of it.
Meaning means an ontology and governed definitions. Revenue becomes a defined concept with an owner, an approved definition, and explicit links to the columns, metrics, and tables that implement each variant. When an agent asks about revenue, it resolves against that concept instead of the closest column name. Humans read the same definition, so a steward's edit changes the answer for people and agents alike..
Memory means decisions, corrections, and an audit trail stored on the graph. When someone tells an agent "remember this", the correction is stored on the graph with an audit record. The next person and the next agent reuse it. The audit trail records who decided what and when, which gives your risk team something to inspect.
Governed access means agents assume the roles and policies you define; OpenMetadata's MCP server uses the same authorization engine as its APIs.
Collate adds metadata , semantics, and memory on top of the OpenMetadata graph. Collate AI, AskCollate, AI Studio, and the MCP server and software development kit (SDK) sit on that governed context, so a chat question, a coding assistant, and a custom agent all read one interpretation.
Collate's enterprise context layer page covers the this in more depth. The metadata for agentic analytics article describes the progression from a card-catalog-style inventory to data intelligence to semantic intelligence. Each step builds on the one before it, so the catalog stays underneath.
A semantic layer executes metric logic for BI. It defines how to compute net revenue, applies joins and filters, and returns the same number to every dashboard. A catalog doesn't do that, and a context platform doesn't replace it.
The semantic layer computes metrics, and the catalog documents and governs assets. A context platform ties both to shared meaning, so the metric definition, the glossary term, and the ontology concept point at each other. That's why catalogs and semantic layers are complementary. If your team already runs a semantic layer, an agent should read its definitions rather than guess around them.
If you're choosing a catalog or extending the one you have, test it against how agents will use it. Check these seven areas:
Ask vendors for named references at your scale and for the same kind of workload. Our guide on what to look for in an enterprise data catalog overlaps with these areas.
Keep it. The inventory, ownership, lineage, and policies you've built are the foundation, and meaning and memory attach to the same graph. If you want the operational work handled for you, Collate runs OpenMetadata as a managed service and adds semantics, memory, and AI Studio. Core metadata stays portable back to OpenMetadata. Collate-exclusive features are not in the open-source project.
A data catalog inventories, describes, and governs data assets for people. A context platform keeps that catalog and adds meaning and memory to the same metadata graph, so agents can resolve meaning and reuse corrections as well.
No. The inventory, ownership, lineage, and policies you already maintain are the foundation, and semantics and memory attach to the same graph. You extend the catalog you run instead of starting over.
No. A semantic layer executes metric logic for BI, while a context platform ties metric definitions, glossary terms, and ontology concepts to shared meaning. The two work together, and an agent should read the semantic layer's definitions.
A glossary entry is a sentence written for a person who can ask a follow-up question. When "revenue" can mean three things that share one column name, an agent needs governed definitions it can resolve, plus a place where corrections persist.
Metadata is what data exists: schemas, tables, columns, dashboards, pipelines, lineage, and classifications. Meaning is what the data means: an ontology and governed definitions. Memory is what people and agents have learned: decisions, corrections, and an audit trail stored on the graph. Together they make up the context both read.
