AI Agents for Data Catalog: What They Can and Can't Do

What is an AI agent in a data catalog context?

An AI agent in a data catalog is an autonomous system that can query, analyze, and act on catalog metadata — not just surface it. Unlike AI-assisted features (ML models running in the background that suggest tags or descriptions), agents reason over the catalog, use tools to retrieve and update metadata, and can chain multiple steps to complete discovery or curation tasks without human prompting at each step.

The practical difference matters: an AI-assisted catalog might automatically suggest a business term for a column when a steward opens the asset. An AI agent might notice a new table appeared in Snowflake, query the schema and recent query history, generate a first-draft description, detect potential PII patterns, suggest an owner based on access logs, and queue all of that for human review — without anyone initiating the workflow.

The distinction isn’t academic. Governance requirements for an agent — RBAC, audit trails, certification workflows — are meaningfully different from what AI-assisted features need. Understanding which type of AI you’re deploying determines what infrastructure you need to build around it.

What AI agents can do in a data catalog

Agents excel at tasks where systematic coverage and speed matter more than nuanced judgment. Enterprise data estates with millions of assets make the case obvious: no human team can manually document, classify, and monitor everything. Agents close that coverage gap.

Auto-classification at scale

Agents can classify data assets by domain, sensitivity, type, and purpose by analyzing schema structure, sample data patterns, and lineage context. A column named ssn_encrypted with specific data patterns triggers a PII classification. A table with columns including revenue, arr, and churn_date gets a Finance domain classification. At scale, this systematic coverage replaces the months of manual work that leaves most enterprise catalogs partially tagged.

AI-generated metadata

First-draft descriptions, business term suggestions, and tags generated by agents are not final — but they’re a starting point that reduces human documentation effort from hours to minutes per asset. An agent with access to schema, sample rows, query history, and existing business glossary can generate descriptions that a steward needs minutes to validate, not hours to write.

PII and sensitive data detection

Continuous, automated PII scanning across all assets — not just the ones a compliance team gets around to checking. Agents apply pattern matching (formats for SSNs, email addresses, phone numbers) plus semantic analysis (column names and context that suggest sensitivity) across the full data estate on a regular schedule.

Lineage traversal

“Where does this data come from?” answered in seconds across multi-system pipelines. An agent with access to the lineage graph via MCP can trace from a dashboard metric back through Tableau → dbt → Snowflake → the source system in one call — the same investigation that takes a data engineer significant time across multiple tools.

Ownership surfacing

Ownership candidates based on who queries an asset most frequently, which team’s pipelines write to it, and which business unit’s SLAs depend on it. These are signals humans can use to confirm ownership — not final assignments, but a starting point that dramatically reduces the “this has no owner” problem.

Quality monitoring

Continuous anomaly detection, schema drift alerts, and freshness checks — agents watch for changes and surface issues before they reach downstream consumers. Rather than discovering a broken pipeline when a dashboard breaks, agents catch the degradation signal upstream.

AI agents classify, document, traverse lineage, surface owners, and monitor quality — all outputs flow to a human certification queue before becoming authoritative.

What AI agents cannot do in a data catalog

The capabilities above sound comprehensive. They are — for the tasks agents are good at. But there is a set of catalog responsibilities that agents cannot take on, and failing to account for this boundary is where many AI catalog deployments run into problems.

Understand business context without being told

“Revenue” means recognized GAAP revenue in Finance, pipeline value in Sales, and attributed MQL value in Marketing. An agent querying your catalog without explicit domain context will retrieve or generate plausible definitions that don’t match your company’s specific usage. Agents require explicit business context — domain-scoped glossaries, certified definitions — to resolve terminology correctly.

Make certification decisions

Whether a dataset is safe for a specific use case requires accountability, not inference. A data steward who certifies an asset for use in regulatory reporting accepts responsibility for that decision. An agent cannot accept that responsibility, and should not be architected to try.

Navigate lineage gaps confidently

When lineage has gaps — Python transformations not captured, external system handoffs not tracked — agents that follow the lineage graph reach conclusions based on incomplete information. They don’t know the graph is incomplete. This is more dangerous than no agent: a confident wrong answer that sounds well-reasoned is harder to catch than an obviously missing answer.

Carry institutional knowledge

“This table always has extra rows at month-end — the finance team runs a correction job on the 3rd.” That kind of knowledge lives in people, not schemas. Agents can’t retrieve what isn’t documented, and the most important catalog knowledge is often the least documented.

Handle exception cases

Circular lineage dependencies, data that crosses to external systems and returns, regulatory edge cases, datasets with complex access patterns — agents fail at the edges. Human stewards navigate ambiguity; agents fail silently or return structured errors.

The governance layer AI agents need

For agents to be useful in the catalog without being dangerous, three governance mechanisms must exist before you deploy them.

Certification workflows: Every AI-generated metadata field — description, tag, business term, owner — must flow through a human approval gate before it becomes authoritative. The workflow is: agent generates → steward reviews → steward certifies or rejects. Without this gate, agents produce outputs that look authoritative but aren’t.

RBAC for agent identities: Agents need catalog access, but not admin access. An agent classified as a “discovery agent” for the Finance domain should only see Finance-domain assets with the access level a Finance analyst would have. When RBAC isn’t extended to agent identities, agents surface data that the requesting user was never authorized to see — a compliance failure at scale.

Audit trails for AI actions: When a description changes, who changed it — and whether that who is a human or an agent — matters for compliance and debugging. Every AI-generated or AI-modified metadata field should carry a timestamp, the agent identifier, a confidence score, and the ID of the human who reviewed it. This makes AI-catalog interactions auditable, not opaque.

Two additional mechanisms round out the governance picture:

Confidence scoring: Agents should flag uncertainty rather than output false certainty. A description generated from a table with no documentation, no sample data, and no lineage context has low confidence and should say so — so the steward knows to scrutinize it more carefully.

Staleness management: AI-generated metadata must expire and refresh when the underlying data changes. An ownership suggestion from months after the initial assignment may be wrong — the team changed. A certification that references lineage that has since changed may be outdated. Active metadata keeps catalog content current as the data estate evolves.

How to structure agent-catalog collaboration

The right architecture is not “agents doing catalog work” or “humans doing catalog work.” It is “agents as the first pass, humans as the certification layer” — with the catalog’s governance workflow as the interface between them.

This architecture works because each party does what they’re actually good at:

1. Agents run systematic discovery — scan all assets, generate first-pass classification and documentation, surface anomalies, flag missing ownership
2. Agents prioritize the queue — rank items by business impact (high-usage assets first), confidence (low-confidence items need more scrutiny), and risk (PII candidates, uncertified assets used in critical pipelines)
3. Agents route to the right steward — based on domain membership and asset type, not just “send to the data team”
4. Stewards certify — validate AI-generated metadata, add business context that agents can’t infer, approve or reject, add institutional knowledge
5. Agents consume certified context — an agent operating from certified metadata, accurate ownership, and complete lineage is noticeably more reliable than one working from raw, unvalidated catalog content
6. Agents monitor for changes — watch for schema drift, ownership changes, quality degradation, and re-queue affected assets when something changes

The feedback loop matters as much as the initial flow. Steward corrections to AI-generated content improve future agent outputs. Agent monitoring catches changes that would otherwise go unnoticed until something breaks downstream.

The collaboration loop: agents surface candidates, stewards certify, agents consume certified context and produce better outputs on the next pass.

What “AI-ready catalog” actually means

In practice, “AI-ready” is often used to mean “has an AI feature turned on.” That’s not what it means structurally. An AI-ready catalog is one where agents can operate reliably — which requires specific infrastructure, not just an AI toggle.

The checklist:

• Certified assets with explicit trust signals — not just documented, but marked as trusted, with who certified them and when
• Complete lineage — column-level where agents need precision for impact analysis and RCA; not just table-to-table connections
• Accurate, current ownership records — maintained as people move roles, not just initial assignments
• Domain-scoped business glossary — terms defined per domain so agents resolve “revenue” to the right definition for the right context
• MCP server for agent-friendly access — structured, programmatic access to catalog metadata; not just a UI for humans
• RBAC extended to agent identities — agents have their own identity with scoped permissions, not admin access
• Audit trail for AI-generated changes — every agent action logged with timestamp, agent identifier, confidence score, and reviewer

If your catalog is missing more than two of these, agents will surface results that look useful but are wrong in subtle ways — and the wrongness is hard to detect because it’s structured and confident-sounding.

How Atlan approaches AI agents in the catalog

Atlan’s design premise is governed context first, agent capability second. The MCP server exposes certified, active metadata to AI agents — not raw schema dumps, but structured context with governance signals embedded.

What this looks like in practice:

Certified assets as agent context: When an agent queries the Atlan catalog via MCP, each asset returned carries certification status, owner identity, and last-updated timestamp. The agent knows which data is trusted and which is provisional — and can signal that distinction in its outputs.

Active metadata: Atlan’s metadata updates in real time as pipelines run, assets change, and stewards take actions. Agents operating through the Atlan MCP server work from current catalog state, not a weekly snapshot.

Context Studio: Atlan’s tooling for building context products — reusable, governed bundles of catalog context that agents consume reliably. A “Finance Analytics Context Product” packages all Finance-relevant metadata, lineage, and glossary terms into a governed context source that any Finance agent can query without needing catalog admin access.

The enterprise context layer is what separates a catalog that has AI features from a catalog that makes AI agents production-grade.

Real stories from real customers: AI-ready data catalogs at scale

"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

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"We're excited to build the future of AI governance with Atlan. All of the work that we did to get to a shared language at Workday can be leveraged by AI via Atlan's MCP server…as part of Atlan's AI Labs, we're co-building the semantic layer that AI needs with new constructs, like context products."

— Joe DosSantos, VP of Enterprise Data & Analytics, Workday

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Why a governed catalog is the AI agent’s most important dependency

The catalog is not just a place agents query. It is the context source that determines whether agents are reliable at all.

An agent doing data discovery draws from catalog metadata to answer “what does this asset represent?” An agent doing impact analysis traverses lineage to answer “what depends on this asset?” An agent detecting PII relies on classification records to know what has already been flagged. In every case, the quality of the agent’s output is bounded by the quality of the catalog content it works from.

This means the path to trustworthy AI agents in your data stack does not start with the agent. It starts with the catalog — getting assets certified, ownership accurate, lineage complete, and business context documented. Building that context layer is what makes agents reliable rather than confident-sounding.

Teams that deploy agents on an ungoverned catalog discover this the expensive way: agents that produce structured, confident, wrong answers that take longer to identify and correct than the manual process they replaced. Teams that govern first discover something different: agents that accelerate the catalog improvement cycle rather than racing ahead of it.

The AI catalog is ready when the governance layer is ready — not before.

FAQs

1. What can AI agents do in a data catalog?
AI agents can auto-classify assets, generate metadata descriptions, detect PII, traverse lineage graphs, surface ownership candidates, and monitor continuously for quality anomalies. These are all tasks where systematic coverage and speed matter more than nuanced judgment.

2. Can AI agents replace data stewards?
No. AI agents can automate scale tasks — classification, first-draft documentation, anomaly detection — but they cannot make certification decisions, interpret business context, or carry institutional knowledge. Human stewards remain essential for governance, judgment, and accountability.

3. What is AI-generated metadata?
AI-generated metadata is catalog content — descriptions, tags, ownership candidates, business term links — produced by an AI agent or ML model rather than a human steward. It requires human certification before it can be treated as authoritative.

4. How do I make my data catalog AI-ready?
An AI-ready catalog has certified assets with explicit trust signals, complete lineage (column-level where possible), accurate ownership records, a domain-scoped business glossary, an MCP server for agent-friendly access, RBAC extended to agent identities, and audit trails for AI-generated changes.

5. What is an MCP server for a data catalog?
An MCP (Model Context Protocol) server exposes catalog metadata — assets, lineage, business glossary, quality scores — as structured tools that AI agents can call programmatically. Instead of a human opening a catalog UI, an agent calls get_asset_metadata() or get_lineage() as part of its reasoning workflow.

6. How does RBAC work for AI agents in a catalog?
AI agents are assigned identities with scoped catalog permissions — just like human users. They can only see and query assets they’re authorized to access. This prevents agents from surfacing sensitive data to unauthorized requestors and ensures compliance with data access policies.

7. What is the difference between AI-assisted and AI-agentic catalog features?
AI-assisted catalog features run ML models in the background to suggest tags, descriptions, or ownership — humans review and apply the suggestions. AI agents are autonomous systems that can reason over the catalog, chain multiple steps, and take actions without human prompting at each step. Agents require stronger governance guardrails than AI-assisted features.

Sources

1. Andrej Karpathy on context engineering, X/Twitter
2. Workday Context as Culture, Atlan Regovern
3. Mastercard Context by Design, Atlan Regovern
4. Atlan AI Labs Ebook — The 5x Accuracy Factor
5. Model Context Protocol Specification, Anthropic
6. Atlan Context Studio
7. Enterprise Context Layer for AI, Atlan