Context Layer for AI Agents: What It Is and the Three Tests It Has to Pass

Your analytics agent writes clean SQL. It joins the right number of tables, uses valid syntax, and returns a confident answer. But the answer is wrong, because the agent queried a staging table nobody maintains, or applied a definition of “active user” that finance stopped using last quarter.

The model didn’t fail. The context did. A context layer exists to fix precisely this scenario (and others like it). For data teams running agents against an enterprise data estate, a context layer is the architectural layer your context platform delivers to every agent.

Why do AI agents need a context layer?

Large language models already write good SQL. What they can’t do on their own is tell a certified metrics table from a raw staging table, know which definition of revenue the business agreed on, or notice that a column was deprecated last month.

“Analytics accuracy is not a code generation problem. LLMs are good already at writing SQL. The problem is context and routing.”

Ronald AngelLead Product Manager, Miro

The failure this produces is hard to spot because it doesn’t look like a failure. In a July 2026 Pulse Research survey of 101 enterprises, VentureBeat found that 68% had traced a confident but wrong agent answer to missing or inconsistent business context in the past six months. More of them had seen it happen repeatedly (37%) than once (32%). VentureBeat describes its sample as a directional signal, but the pattern matches what data teams report in production.

What a context layer for AI agents is (and isn’t)

A context layer for AI agents has a specific job: It sits between enterprise data and the agents that query it and supplies governed business meaning. That covers which tables are authoritative, what metrics mean, which business rules apply, who owns which assets, how fresh the data is, and where a number came from.

Gartner® frames the layer as three components:

1. Semantic understanding: Interpret and organize information based on meaning, not merely keywords or raw data.
2. Operational state: Integrate right-time data to ensure AI agents operate with the most current information.
3. Provenance: Implement mechanisms to track data sources to insights, decisions and outcomes, supporting continuous improvement and auditability.

That’s the right frame, and our breakdown of context layer components for AI agents covers what it takes to build each one.

But because the term now gets attached to many kinds of products, it helps to be clear about the boundaries:

A context layer for AI agents is…	A context layer for AI agents isn’t…
The shared source of business meaning every agent draws from	Context window management, which decides what fits into a single model call
A governed layer with owners, access control, expert review, and lineage	A database or vector store, which holds data but not agreed meaning
A broader layer that includes semantic definitions	A semantic layer on its own, which standardizes metrics but not ownership, freshness, or provenance
Delivered to agents through open interfaces such as the Model Context Protocol (MCP)	MCP on its own, which carries context to agents but can’t make it trustworthy
Continuously updated and tested	A one-time documentation project

Three tests a context layer for AI agents has to pass

Most definitions of a context layer read like an architecture diagram: They describe what goes inside it. But for agents in production, how the layer behaves over time and across the organization matters just as much. A layer can contain every right component and still fail agents if each team builds its own copy, if it drifts as schemas change, or if nobody can tell whether its answers are right.

These three tests surface those failure modes before your business users do.

1. Does every agent share the same context?

Context built into one agent’s system prompt, one team’s knowledge base, or one platform’s semantic model creates context islands. Each island is internally correct, but none can see the others. The same definitions get authored again for every new agent and slowly drift apart, until the finance agent and the product agent return two different numbers for the same question.

Here are two questions that test whether your context layer is shared:

• Does a new agent inherit the organization’s context on day one, or does someone have to rebuild it?
• Do two agents give the same answer to the same question?

According to BARC’s Context Engineering for Agentic AI study, the market is split almost evenly. 44% of organizations manage context within one agent, team, or platform, while 43% manage it across teams, platforms, or the whole enterprise. Intent is clearer than practice: DataHub’s State of Context Management Report 2026 found that 93% of organizations are likely to treat context as shared infrastructure rather than team-specific tooling.

Passing this test takes three things:

1. Context is authored outside any single consuming platform, so no one agent or vendor owns it.
2. It’s served through open interfaces such as MCP and APIs, so any agent can reach it.
3. It’s pushed back out to the platforms in the stack, so the definitions people see in the warehouse match the ones agents use.

Enterprises appear to agree on the first point. In the same VentureBeat survey, 79% expect to keep at least part of the context layer outside any single model provider.

A shared layer should also learn: When agents write what they discover back into it through governed workflows, every other agent benefits, and onboarding each new agent gets faster.

“The most effective systems carry forward what an organization has already learned and make that knowledge more usable for others.”

Keqiang LiSoftware Engineer, Pinterest

FIS shows what passing this test looks like. Its context is authored outside Databricks, so Databricks Genie draws on it today, and whatever FIS builds next will draw on the same layer. That portability is the core idea behind interoperable context.

DataHub’s Context Activation layer serves one context graph to Snowflake Intelligence, Databricks Genie, Claude, Cursor, LangChain, and custom agents through the DataHub MCP Server, the Agent Context Kit, and APIs. Platform sync pushes governed glossary terms and tags back to Snowflake and BigQuery, and agents can write back tags, descriptions, and ownership as part of governed workflows.

2. Does it stay current and validated as the business changes?

Building a context layer isn’t the hardest part… keeping it trustworthy is.

Indeed, context starts to decay the moment a schema changes, a pipeline moves, or a metric definition gets updated. While people compensate for stale documentation without thinking about it (they notice a number looks off, or they ask the table owner), agents act on whatever the layer tells them.

A context layer for agents needs two mechanisms working together:

• Event-driven freshness: Changes in source systems reach the layer as they happen, rather than at the next scheduled batch.
• Human validation: Generated context lands as a draft, and a domain expert reviews it before agents rely on it.

Freshness is where mature teams pull ahead. BARC found that 58% of context leaders absorb upstream data changes in their AI applications on a (near) real-time basis, compared with 19% of other organizations.

“In any large enterprise, the real knowledge is gonna be tied up in people’s heads, and I don’t know anybody that’s writing an adapter yet that can go into someone’s brain and pull that out.”

Frank ShowalterVP of Data Engineering and Governance, FIS

DataHub Cloud: Context covers both mechanisms:

• Context Ingestion is event-driven across 150+ systems and brings unstructured sources like Confluence and Notion into the same context graph as structured metadata, so agents query both through one interface.
• Context Intelligence derives business meaning from query history, dbt, and BI tools.
• Context Hub routes generated context to subject matter experts as proposals, and nothing reaches agents until an expert reviews, edits, and publishes it.

Learn more about why human-validated semantic context matters and how continuous context keeps documentation from going stale.

3. Can you prove it’s working?

This is the test most teams skip, and the one that tells you whether the other two are holding.

VentureBeat’s survey surfaced a counterintuitive result: Enterprises running or building a governed semantic or context layer reported recurring context failures at 50%, against 21% for those without one. VentureBeat reads this as detection: the governed layer makes failures traceable. Without shared definitions to trace against, the same wrong answers get logged as model errors or go unnoticed. A low failure rate can simply mean nobody is checking.

Most organizations know measurement is a gap. In the State of Context Management Report 2026, 37% cited evaluation and observability gaps as an obstacle to scaling AI agents. Evals close that gap.

Five practices turn evals into a real way to improve context:

1. Let domain experts define what good looks like: The people who know the business write the eval questions and the pass conditions. A pass condition can be strict, such as requiring the answer to reference a specific certified table. It can also be judged against written guidelines for questions with more than one acceptable answer.
2. Write evals before you generate or edit context: Evals defined first give you a baseline. Every change to context can then be measured against it, so you can show improvement rather than assume it.
3. Run evals on a schedule: Context drifts between releases. Evals that run on a set cadence catch regressions before a business user sees a wrong answer.
4. When an eval fails, fix the context, not the prompt: Trace a failing eval to the specific context document behind it and correct that document. A prompt tweak patches one agent. A context fix reaches every agent that draws from the layer.
5. Start with one domain and expand: Scope agents and evals to one business area, such as finance or product analytics. Widen the scope once that domain passes reliably.

Miro shows this discipline in production. Its data experts wrote an evaluation bank of 900 real business questions across finance, marketing, and product, and the team runs it daily. Against that benchmark, Miro’s analytics agent went from under 40% accuracy to over 90%, and wrong-table routing fell from more than 30% of cases to under 10%. The same agent and the same model ran at every stage. The evals also showed the team where to look.

“When we started analyzing why zombie evals were failing, one of the most common reasons was because it was getting the information from the raw table that was not ready.”

Ronald AngelLead Product Manager, Miro

The fix was in the context: ranking signals that pushed documented, trusted tables above raw ones, and quality gates that filtered out stale assets.

In DataHub Cloud, Evals let domain experts create eval questions, run them against agents, and see which context documents each eval referenced. Evals can be attached to context proposals so changes are validated before they’re published, and they can run on a schedule.

A failing eval traces to the exact context document that caused it, so teams fix the right thing. Evals can also be grouped by domain so each team owns its benchmark questions, and custom agents can be scoped to one area, such as a “Marketing Domain” agent, with an Evals tab that tracks its pass rate over time.

Putting the three tests to work

A context layer for AI agents earns the name when:

• A new agent inherits it on day one
• It keeps pace with the business without anyone rewriting documentation by hand
• Expert-written questions show it’s getting answers right

The tests reinforce each other. Shared context that drifts spreads errors to every agent at once, and evals are how you’d know it was happening. If you’re starting from scratch, start with the evals.

To see the full path from defining evals to activating context across your agents, follow the five steps in How to Build a Data Agent on DataHub.

FAQs

How do you know if a context layer for AI agents is working?

You test it with evals. Domain experts write real business questions with pass conditions, and those questions run against your agents on a schedule. Accuracy over time shows whether the layer is improving. When an eval fails, tracing it to the context document behind the answer shows what to fix. A low reported failure rate without evals doesn’t prove the layer is healthy.

Do multiple AI agents need separate context layers?

No. When context is an implementation detail of each agent or platform, you get context islands, where the same definitions get rebuilt and drift apart. A single shared layer, authored outside any one platform and served through open interfaces like MCP and APIs, lets every agent inherit the same context and give the same answer to the same question.

How does a context layer stay up to date?

Two mechanisms keep a context layer current. Event-driven ingestion carries changes from source systems into the layer as they happen, rather than on a batch schedule. Human validation ensures generated context is reviewed by a domain expert before agents rely on it. Scheduled evals then catch any regressions that slip through.

Who owns the context layer for AI agents?

Ownership is a strategic question, and the answer is usually shared. A data or platform team typically runs the context platform and its integrations, while domain experts own the meaning: they validate generated context and define the evals for their area. Scoping by domain makes that split practical. Learn more about who owns context management.

Is MCP a context layer?

No. The Model Context Protocol (MCP) is a standard interface for delivering context to AI agents at runtime. It carries context but doesn’t govern, validate, or keep it current. With a governed context layer behind it, MCP becomes the main channel agents use to reach trusted context. Without one, it delivers inconsistent context through a consistent interface.

Is a context layer the same as RAG?

No. A RAG pipeline is a retrieval system: It finds relevant chunks of content and adds them to a model’s prompt. Vector databases and vector search are the storage and lookup tools many of those pipelines use. A context layer supplies what retrieved chunks lack, including governed definitions, ownership, freshness, and lineage. RAG can run on top of a context layer, so retrieval draws on the same system every agent uses. Learn more about RAG vs. context management.