An AI data catalog earns that name by whether an AI agent gets a correct, trusted answer at runtime, not by how many suggestion features sit behind its search bar. Atlan frames that shift as three eras: manual curation, automated suggestions a person still has to accept, and now autonomous context creation, where Context Agents draft business definitions, classifications, and lineage from evidence and a human steward certifies the result before it ships.
What changed is where the work happens. A catalog that only accepts or rejects AI suggestions still puts the authoring burden on a person; an autonomous one creates the first draft itself, from real usage rather than a guess at what a column name means, and asks a human to certify it rather than write it. That also changes what “working” looks like: a coverage percentage describes effort, not whether an agent actually found what it needed, which is also why the practical capabilities an AI-ready catalog supports for AI use cases look different than a year ago.
- Three eras, not two. Manual curation gave way to automated suggestions; the current shift is autonomous context creation.
- Evidence over guesswork. Context Agents draft from query history, lineage, and existing business logic, not from a column name alone.
- Answers, not coverage. A catalog’s job is judged by whether an agent’s answer was right, not by how many assets have a description.
- A person still signs off. AI drafts the context; a steward certifies it before anything ships to production.
Below, we cover: what an AI data catalog actually is, how it differs from a traditional catalog, what an autonomous catalog contains, how you tell if one is working, whether AI-generated metadata can be trusted without review, and how Atlan approaches the problem.
| Attribute | Details |
|---|---|
| What it is | An AI data catalog that creates and delivers context autonomously, not just faster documentation |
| Key shift | Manual curation, then automated suggestions, now autonomous context creation |
| Success metric | Whether an agent gets a correct answer at runtime, not how many assets are documented |
| Core components | Context Agents, a vectorized context layer, MCP-based delivery, and a learning loop |
| Best for | Teams running AI agents against enterprise data who need governed, certified answers |
| Trust model | AI drafts from evidence; a human steward reviews and certifies before anything ships |
What is an AI data catalog?
An AI data catalog is judged by whether an agent gets a correct, trusted answer at runtime, not by its feature list. That is a different bar than the one the category has used since 2023, when “AI data catalog” came to mean a catalog that could suggest a description or a tag for someone to review.
That suggestion-and-accept model was a real improvement over a blank field, and it is still what most vendors sell today. But it is the middle of a three-part shift, not the destination: a catalog that only offers suggestions still depends on a person to approve every piece of context an agent might need, which does not scale to lineage across thousands of tables or years of query history.
The corrected definition starts from evidence. It creates context, descriptions, glossary terms, classifications, lineage, by reading how data is actually queried and used, then routes the draft to a person for certification rather than free-generating a plausible answer from a column name alone, and detects when context goes stale and repairs the gap on its own schedule, the same distinction behind whether the data catalog is finally dead or just doing a different job now.
A person tolerates a slow, browsable search result. An AI agent does not: a wrong or missing answer at query time is a runtime failure, not an inconvenience, and it shows up downstream as a wrong number on a dashboard. According to IBM, a data catalog exists to help people find, understand, and trust the data they use. An autonomous one extends that same job to agents, at the speed agents actually operate.
The catalogs that matter over the next few years will not be the ones with the most suggestion features. They will be the ones that create context an agent can act on without a person approving every single field one at a time, then have a steward certify the result before it ships.
How is an AI data catalog different from a traditional data catalog?
The market has moved through three distinct eras of “AI data catalog,” and most catalogs sold today still operate in the middle one, whatever their marketing calls them.
In the manual era, humans curated everything by hand. Every description, every glossary term, every lineage map was typed by a person, and the documentation backlog never closed because new tables arrived faster than anyone could write about them.
The automated era is where most of the category lives right now, and the useful test is not how good the AI-generated draft is, it is what happens to it next. A catalog in this era generates a description, a tag, or a business term, and a person has to approve, edit, or reject each one before it counts as documented. Snowflake Horizon Catalog describes itself as an agentic catalog and uses Cortex to auto-generate descriptions and semantic views, a different approach from Atlan’s own context layer.
According to Databricks’ own documentation, Unity Catalog can auto-generate table and column descriptions too, a capability that shipped in preview in 2023 and reached general availability in 2024. Both generate real drafts from real signal; what still places them in this middle era, for now, is a workflow built around per-item approval rather than certifying an already-assembled body of context. For what Context Agents can and can’t do yet, or how the wider market compares on AI readiness, see data catalog tools.
The autonomous era is what this shift is actually about. Context Agents create context rather than suggest it, drafting from query history, lineage, and existing business logic. Consumption moves from a search box to a conversational interface an agent calls directly, through MCP and semantic search. Learning moves from closed, where what a user could not find was never recorded, to self-improving, where usage traces flow back and gaps get repaired.
| The shift | Manual | Automated (today’s common pitch) | Autonomous (now) |
|---|---|---|---|
| Metadata supply | Humans curate everything; the documentation backlog never closes | Auto-tagging and suggested descriptions a person accepts or rejects | Context Agents create descriptions, glossary terms, and classifications from evidence |
| Metadata consumption | Context lives in the catalog; the real work happens everywhere else | Context gets embedded in daily tools, personalized by role | AI becomes a consumer too, through MCP, semantic search, and conversational retrieval |
| Flexibility and learning | Closed; what a user could not find was never recorded | Extensible; open APIs, webhooks, a connector marketplace | Self-improving; usage traces flow back and gaps get repaired |
Other vendors are naming this same shift. A mid-market vendor has already published a similar three-tier framing built around the word “agentic.” That is worth taking seriously rather than dismissing: when a category’s newest word gets used by every vendor within a year, the word stops doing any differentiating work on its own. What still separates catalogs within that newest tier is not the label; it is whether context gets created from evidence and certified by a person, or generated and left for someone to eventually check.
What does an autonomous AI data catalog actually contain?
An autonomous catalog is four connected components, not a single feature: Context Agents that create context, a lakehouse that stores and serves it, MCP and conversational AI that deliver it, and a learning loop that closes the gap between what the catalog knows and what an agent actually asked.
Context Agents are the creation layer: AI teammates that draft descriptions, glossary terms, domain and sensitivity classification, and quality scores, grounded in evidence, how a table is actually queried, where it sits in the lineage graph, what business logic already exists for it, rather than a suggest-and-accept widget bolted onto search. A person still reviews and certifies the result; the agent drafts, it does not decide.
The Context Lakehouse is the engine, extending traditional metadata management for AI into something an agent can query directly. It stores and vectorizes every piece of context in open tables, in the customer’s own cloud, making four retrieval modes possible: vector and semantic search, hybrid search, and graph traversal. That answers why keyword search alone is not enough: a keyword match finds a table with the right name, but it cannot answer “where’s the table someone built for the Q3 conference-spend analysis, I don’t remember the name,” or trace which dashboards break if a column changes. Those need a context graph an agent can traverse, distinct from a knowledge graph in ways that matter more than they sound.
MCP and conversational AI handle delivery. One MCP connection serves the same certified context to every agent, the same interface layer any AI agent harness needs to reach enterprise data, while conversational search serves people asking the same kind of question in plain language, which is also how MCP delivers business context at agent speed, not wiki-reading speed.
The learning loop closes the system. It traces real usage, notices when a real question comes back incomplete or wrong, flags the gap, and routes a fix back to the Context Agents. The result lands in the Enterprise Data Graph already vectorized and traced to its source, so the next agent asking the same question gets a grounded answer, the same mechanism that turns an agent’s memory of past queries into something the catalog learns from.
| Stage | Component | What it does |
|---|---|---|
| Creation | Context Agents | Draft descriptions, glossary terms, domain and sensitivity classification, and quality scores from evidence |
| Engine | Context Lakehouse | Stores and vectorizes every piece of context, in open tables, in the customer’s own cloud |
| Consumption | MCP and conversational AI | Serve the same certified context to agents through one MCP connection and to people through conversational search |
| Learning | The learning loop | Traces real usage, flags context gaps, and routes fixes back to Context Agents |
None of these four pieces does much alone. A Context Agent drafting good descriptions into a catalog nobody’s agents can query is a documentation project, not a context layer. Building all four together is what makes a catalog get better the more it is used, instead of slowly going stale, and it is also what makes the next question, how do you know it is actually working, answerable at all: without a learning loop tracing real usage, there is no record of which answers landed and which did not.
How do you know an AI data catalog is actually working?
Traditional catalogs were measured by coverage, assets documented, terms defined; an autonomous catalog has to be measured by answers.
The old scoreboard is easy to report and does not tell you what you actually want to know. A coverage percentage, a glossary term count, a monthly active user number, all of these are inputs. None of them says whether an agent, or a person, actually got the right answer when they asked a real question. A catalog can show high coverage and still send an agent a stale or wrong number if the gap it is missing happens to be the metric someone asked about that week.
The new scoreboard asks a more direct question: did an agent find what it was looking for, and if not, why not. A missing definition, a stale one, and a genuine gap in what the catalog knows are three different problems with three different fixes, and a coverage number cannot tell them apart.
A practical starting point, not a full benchmark: pull ten real questions your team actually asks in a given week, run them against your current catalog, and score each one answered, wrong, or unknown. It won’t replace a rigorous evaluation, but it’s a more honest maturity signal than any coverage percentage. It measures what an agent needs, not what’s easiest to put on a dashboard.
| Traditional catalogs measured by coverage | Autonomous catalogs measured by answers |
|---|---|
| Assets documented | Did an agent find what it needed |
| Glossary terms defined | If not, was it a missing definition, a stale one, or a genuine gap |
| Active users | How often the same question required a manual follow-up |
This reframe is what “agentic” should actually mean day to day, not a word on a product page. A vendor can call a catalog agentic without changing how success gets measured underneath it. The catalogs worth trusting are the ones willing to be judged by answers instead of coverage, because that is a harder number to make look good and a more honest one to report.
Can you trust AI-generated metadata without human review?
Not on its own, and any AI data catalog worth evaluating has to answer the one question skeptical, evaluation-stage buyers actually care about.
Start with the risk directly. A reader who prefers an AI-written description over a human-written one is judging fluency, not accuracy. A confident, well-formatted sentence reads as more trustworthy than a rough one, whether or not the underlying fact is correct. That is exactly the failure mode a catalog built on suggestion-and-accept can hide: a plausible-sounding description nobody actually checked against the data.
The certification model treats that risk directly instead of hoping it goes away. A Context Agent drafts from evidence, query history, lineage, an existing glossary entry, not by free-generating a plausible answer from a column name alone, the same evidence discipline behind a well-built data contract for AI. A human steward then reviews and certifies the draft rather than writing it from scratch: judgment work, deciding whether a draft is right and who should own it, replaces documentation work, typing out a description nobody has time to write. The learning loop’s gap detection helps here too: a description flagged as stale or incomplete by real usage is a clearer certification trigger than waiting for someone to notice it looks wrong.
This is also where most AI data catalog content stops short. Vendor pages describe what their AI features do; few of them treat verification as a first-class topic. Decube (2026) comes closest, framing its coverage explicitly around a catalog’s limits and the questions a buyer should ask before trusting one. That honesty is worth crediting even where the details differ: a catalog that cannot explain how a human stays in the loop has not actually answered whether its AI-generated metadata can be trusted, no matter how it is marketed.
Evidence-based drafting plus a certifying human is what separates autonomous context creation from free generation, and it is the difference worth checking for in any catalog making an agentic claim, including how data governance itself has to adapt around it.
How Atlan approaches the autonomous AI data catalog
Suggestion-and-accept still puts the authoring burden on a person, even when the suggestion itself is good. A steward reviewing a thousand AI-generated tag suggestions one at a time is doing the same job as a steward writing them from scratch, just faster. That model scales suggestions. It does not scale context creation, because a person remains the bottleneck for every single field, and the number of tables, columns, and business terms an enterprise actually has keeps growing faster than any review queue can clear.
Atlan’s Context Agents create context from evidence: lineage, query history, and existing BI semantics, and write it into the Enterprise Data Graph, the connected layer that unifies technical metadata, business definitions, and usage patterns in one place, and the first real step in how to prepare enterprise data for AI agents. Delivery runs through Atlan’s MCP server for agents and conversational search for people, so both query the same certified answer instead of two different versions of the truth. Governance does not disappear in this model; it moves earlier. Stewards certify context before it reaches an agent, rather than auditing what an agent already said after the fact, which is also why agents need an enterprise context layer at all rather than a search index alone.
Mastercard made this exact shift, from writing metadata descriptions by hand to reviewing and certifying AI-drafted ones, in a matter of weeks rather than the years a fully manual rollout would take. The shift is not about writing less. It is about spending a steward’s time on the judgment calls that actually need a person, and letting evidence do the drafting everywhere else, a shift the enterprise context layer depends on to work at all. Judged by whether the certified answer holds up when an agent actually asks, not by how many descriptions got written, that is the shift working as intended.
Real stories from real customers: Context certified, not just generated
Enterprises adopting autonomous, human-certified context creation describe the shift in their own words. The practical capabilities behind it, beyond the certification model, are covered in more depth in data catalog for AI.
"AI initiatives require more context than ever. Atlan's metadata lakehouse is configurable, intuitive, and able to scale to hundreds of millions of assets. As we're doing this, we're making life easier for data scientists and speeding up innovation."
— Andrew Reiskind, Chief Data Officer, Mastercard
"Atlan captures Workday's shared language to be leveraged by AI via its MCP server. As part of Atlan's AI labs, we're co-building the semantic layer that AI needs."
— Joe DosSantos, VP, Enterprise Data and Analytics, Workday
Mastercard and Workday are two named accounts, not the whole picture. According to Atlan (2026), the platform has generated 1.7M+ AI-drafted descriptions across its enterprise customer base, with 87% rated on par with or better than human writing and 90% of drafts accepted after steward review. That aggregate figure is what evidence-based drafting plus certification looks like across the base, not evidence from these two stories alone.
What makes a catalog agentic is the mechanism, not the word
The word “agentic” is becoming commodity vocabulary across the data catalog category. A mid-market vendor can publish the same three-tier framing used here, and by next year several more will. That is not a reason to avoid the framing; three eras is simply what happened. It is a reason to be specific about what actually separates catalogs within the newest tier.
The differentiator was never the label. Whether a vendor calls it a context catalog or an agentic one, what matters is whether it creates context from evidence instead of a plausible guess, certifies that context with a person before it ships, and delivers it somewhere an agent can actually call it, through MCP, semantic search, or a query it runs directly, rather than a page a person has to open.
A catalog that does all three is agentic in practice. One that only uses the word is describing the automated era in newer language, and the difference shows up the first time someone checks. The same test applies to how any semantic layer, context graph, or enterprise context layer built for AI agents gets evaluated: evidence in, a person certifying, an agent able to call it.
FAQs about AI data catalogs
1. What is an AI data catalog?
An AI data catalog creates and maintains business context, descriptions, classifications, lineage, and glossary terms, using artificial intelligence rather than relying only on manual documentation. The current generation creates that context autonomously from evidence like query history and lineage, then routes the draft to a human steward for certification before an agent or a person queries it.
2. How is an AI data catalog different from a traditional data catalog?
A traditional catalog depends on people to write every description, tag, and lineage note by hand, so documentation never keeps pace with new data. An AI data catalog automates that work in stages: first by suggesting descriptions for a person to approve, and now by creating context autonomously from evidence, with a person certifying the result instead of writing it from scratch.
3. What are the benefits of an AI-powered data catalog?
The main benefits are faster data discovery, consistent documentation across teams, and metadata that stays current instead of going stale between manual updates. For AI initiatives specifically, an AI-powered catalog gives agents a governed source of business context, definitions, ownership, and lineage, so they answer questions correctly instead of guessing from a table name.
4. What are the best AI data catalog tools?
The right tool depends on your data estate, your AI use cases, and whether you need autonomous context creation or AI-assisted suggestions. A detailed comparison of data catalog tools, evaluated specifically for AI readiness, is a more reliable way to choose than a short ranked list here.
5. Can AI fully automate data cataloging?
No. AI can create most of the first draft, descriptions, classifications, lineage relationships, from evidence like query history and existing business logic. It cannot decide who owns a dataset, resolve a genuine disagreement about what a business term means, or take accountability for a certification. Those remain human judgment calls, even in an autonomous catalog.
6. Does an AI data catalog replace data governance?
No, it changes when governance happens. Instead of auditing metadata after the fact, stewards review and certify AI-drafted context before it reaches an agent or a person. Policies, ownership, and access rules still need a governance program to define them; the AI data catalog gives that program a faster, more current record to enforce them against.
7. What is an agentic data catalog, and how is it different from an AI-powered one?
“AI-powered” commonly describes a catalog that suggests descriptions or tags for a person to accept. “Agentic” should describe a catalog where Context Agents create that context autonomously from evidence and a human certifies it, rather than approving each suggestion one at a time. The word alone does not guarantee the mechanism behind it.
8. How do you verify that AI-generated metadata is accurate?
Check what the draft was generated from. Metadata drafted from real evidence, query history, lineage, an existing glossary entry, is verifiable against that evidence. Metadata free-generated from a column name alone is not. A human steward should certify the draft against its source before it becomes the answer an agent relies on.
9. How does an AI data catalog power the enterprise context layer?
An AI data catalog maintains the continuously updated business context, definitions, ownership, lineage, and classifications, that an enterprise context layer depends on. Manual documentation cannot keep pace with the metadata volume an AI agent needs across hundreds of systems; autonomous context creation makes maintaining that context at enterprise scale realistic instead of a permanent backlog.
