What Is a Unified Context Layer? (And What Unified Doesn’t Mean)

“Unified context” has become a fixture of AI infrastructure pitches, and nearly every vendor in context management now claims it. The catch is that most of them mean roughly the same thing by it: move your context into our platform, and it’s unified. For an enterprise running a warehouse, a lakehouse, several BI tools, and agents built in three different frameworks, that definition relocates the problem without solving it.

The problem itself is real. According to the 2026 State of Context Management Report, 57% of organizations duplicate AI efforts across departments because they lack a comprehensive, unified context graph. Every agent built without shared context starts from scratch, with its own definitions, its own retrieval logic, and its own guess about which table is the right one.

What a unified context layer is

A context layer sits between your enterprise data and the people and agents who use it. It supplies the meaning that raw data and schemas can’t:

• What a metric means
• Which table is trusted
• Who owns it
• What methods and exceptions to use when calculating a metric

A unified context layer holds all of that in a single governed graph that every consumer reads from. Structurally, it resembles a knowledge graph, scoped to the definitions, ownership, and rules of one specific business.

That graph covers two kinds of context.

1. Structured context includes schemas, lineage, ownership, quality signals, and glossary terms.
2. Unstructured context includes runbooks, policies, FAQs, and decision logs.

In a unified layer, both are first-class and linked, so the graph can capture complex relationships between them. A policy document connects to the tables it governs, and a decision log connects to the metric definition it changed.

Both people and agents read from the same graph. An analyst checking a metric definition and an agent generating SQL for that metric should land on the same answer, because they’re consulting the same source.

What’s most often missing: institutional knowledge

Structured and unstructured context draw on the same source: institutional knowledge, the understanding of how the business actually works that teams carry with them long before anyone writes it down. That institutional knowledge is the context most “unified” layers leave out, because no connector can ingest what nobody has written down.

Here’s what institutional knowledge looks like once someone writes it down:

Type of knowledge	Example
Metric caveats and business rules	“Active customer excludes trials and internal test accounts. Finance counts a customer active only after the first paid invoice.”
Runbooks	“If the nightly revenue pipeline fails, check the upstream billing export first, then rerun the dbt job, then notify #finance-data.”
Institutional knowledge about data quality	“The orders table double counts refunds before March 2024. Use orders_v2 for anything historical.”
Decision history	“We moved from last touch to multi-touch attribution in Q2 because of the partner channel. Here’s the memo.”
Query recipes and approved patterns	“To calculate net revenue retention, join subscriptions to invoices on account ID and exclude one-time services.”
Policies and governance guidance	“PII columns can’t be used in dashboards shared outside the company. Here’s the approved masking approach.”
Onboarding and domain guides	“How the sales team defines pipeline stages and where each stage lives in Salesforce.”
Incident postmortems	“The March outage was caused by a schema change in the events table. Here’s what downstream assets were affected and the fix.”

Before Trustpilot brought 500,000+ assets across AWS and GCP into DataHub, knowing who owned a dataset or what depended on it meant searching GitHub, Slack threads, and colleagues’ memories. Capturing that knowledge as ownership records and descriptions in the shared graph is what made it usable.

“That institutional knowledge was in people’s heads. Get it out into DataHub and it becomes much more accessible.”

David WalkerStaff Engineer, Trustpilot

A unified context layer belongs in shared infrastructure

Most organizations already agree that context belongs in shared infrastructure. The State of Context Management Report found 93% are likely to treat context as shared infrastructure rather than team-specific tooling.

Practice is catching up more slowly. BARC’s Context Engineering for Agentic AI study found that 44% of organizations still manage context within a single agent, team, or platform, compared with 43% that manage it across teams, platforms, or the full enterprise. BARC notes that the second group is better positioned to reuse context across business domains. The same study found that context leaders were four times as likely to qualify as AI leaders (49% vs. 12%), while cautioning that the relationship doesn’t prove causation.

Three things unified doesn’t mean

Most confusion about unified context comes from borrowing the word’s meaning from elsewhere in the data stack. Three borrowed meanings come up most often.

1. Unified isn’t consolidated onto one platform

Most enterprises will stay multi-platform. Warehouses, lakehouses, BI tools, and agent frameworks each earn their place, and few organizations will standardize on one vendor for all of them. A context layer that’s unified only inside one platform becomes another context island, correct within its walls and invisible to everything outside them.

A unified context layer sits beneath the platforms instead. Context is authored once, served through open interfaces (MCP, APIs, and SDKs) to any agent, and pushed back out to platforms like Snowflake and Databricks, so the same definition shows up wherever people work.

FIS shows what this looks like in practice. To offer natural language query to its banking clients, FIS grounded Databricks Genie in context authored in DataHub, then published that context into Unity Catalog on a schedule. Genie stayed the agent and Databricks stayed the platform. The context lived in a layer neither one owned, which is what kept it consistent across systems. (Interoperable Context covers the properties that make that kind of portability possible.)

“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

MCP doesn’t unify context either. It’s a transport protocol that gives agents reach, but it doesn’t decide which definition is correct. An agent connected to five MCP servers carrying five definitions of “active customer” has more reach and exactly the same inconsistency.

2. Unified isn’t a router over separate context stores

A second reading treats unification as a routing problem. Each team’s metadata store stays in place, and a router in front of them queries each one at runtime. Agents get a single entry point, but behind it sit many context stores, each drifting on its own schedule. Definitions diverge, ownership records conflict, context rot sets in, and the router has no way to tell which version is right.

It helps to separate data from context here. Querying data where it lives, across sources, is a legitimate pattern. But leaving context scattered across stores is how drift starts. A unified context layer keeps one graph even when the data underneath it stays distributed.

Pinterest‘s engineering team ran into this directly. While building an analytics agent across more than 100,000 analytical tables, the team found that quality signals, authoritative schemas, and established query patterns lived across multiple systems, so no single search could retrieve everything the agent needed. Pinterest’s internal catalog, built on open source DataHub, became the system of record for table tiers, ownership, and column-level glossary terms, and the foundation its analytics agent builds on.

According to Pinterest’s engineering blog, the agent now sees 10x the usage of the next most-used agent at the company. (DataHub’s breakdown of Pinterest’s architecture covers the patterns behind it.)

Related: How to Build a Context Layer covers unification as a build step, including why agents need a single access surface.

3. Unified isn’t unscoped

This misreading gets the least attention. If the context is unified, the reasoning goes, every agent should see all of it. In reality, exposing the whole graph to every agent makes answers worse. An agent choosing among tens of thousands of tables can’t reliably tell the production semantic view from the raw staging table with a similar name.

The answer is to unify the foundation and scope the activation to its domain. A finance agent draws on certified finance assets, and a marketing agent draws on marketing’s. Each starts narrow and expands as accuracy holds. Because both agents draw from the same graph, domain scoping them doesn’t fork the context. A metric defined once means the same thing to both.

Miro reached the same conclusion. With DataHub as the context layer between Claude Code and Snowflake Cortex, Miro curated only the data products critical to its AI use cases and deliberately left the rest of its 20,000+ datasets out of scope. Analysts choose between two modes. Broad exploration across the catalog runs at 85% to 90% accuracy. A mode restricted to well-documented data products and semantic views stays above 90%.

“Some people want to explore data broadly… Some people can say, I want only data products or semantic views that are well documented, and this gives us a higher than 90% level of accuracy.”

Ronald AngelLead Product Manager, Miro

Pinterest drew a similar lesson from its own build: grounding an agent in the organization’s specific schemas, metrics, and assets produced more reliable results than a generic setup.

The tests: how to tell whether a context layer is actually unified

The same tests apply whether you’re evaluating a vendor’s claim or checking a layer you’ve already deployed.

Two agents give the same answer to the same question

Consistency is the core test. If a finance agent and a sales agent return different revenue figures for the same quarter, the context isn’t unified, regardless of where it’s stored. Practitioners rank this highly: in BARC’s study, 38% cited consistency and reliability as their top priority for context engineering, ahead of accuracy at 34%.

Consistency depends on resolving conflicting definitions once, upstream, before agents rely on them. When two teams define “active user” differently, a unified layer surfaces the conflict to the people who own those definitions, rather than letting each agent pick one.

Consistency should be measured, not assumed. Miro benchmarked against 900 curated business questions across finance, marketing, and product and cut wrong-table routing from over 60% of cases to under 10%. Evals are how you confirm consistency holds as the layer changes.

A new agent inherits the organization’s context on day one

In a unified layer, a new agent connects and starts with what the organization already knows: definitions, trusted assets, ownership, and the institutional knowledge attached to them. Nobody rebuilds prompts, retrieval logic, or a private glossary for it.

That should hold across platforms. An agent built in LangChain, a Snowflake Cortex agent, and a Databricks Genie space should all retrieve the same definition of the same metric. If each new agent requires its own context project, the duplicated effort the State of Context Management Report measured is still happening.

The layer stays unified as it grows

Launch is the easy part. Schemas change, definitions evolve, and new agents arrive. A layer that’s unified on day one can fragment within months if agents keep what they learn to themselves.

Write-back prevents that. When agents record what they learn in the shared graph by adding tags, updating descriptions, and assigning ownership, every agent benefits from every other agent’s work.

Pinterest built a similar loop at the query level, where each analyst query becomes reusable context for the next analyst or agent. Human review and an audit trail of what each agent changed keep those additions trustworthy, so growth doesn’t reintroduce the drift unification was meant to remove.

How DataHub delivers a unified context layer

DataHub is a context platform built to pass all three tests while your existing platforms stay in place.

One graph, served everywhere, for the day-one test:

• Context Ingestion unifies metadata from 150+ systems with documentation from Confluence and Notion in a single context graph.
• Context Intelligence derives business meaning from query logs, dbt, and BI tools.
• Context Activation serves that graph through the DataHub MCP Server, APIs, and SDKs, and through the Agent Context Kit, which covers Claude, Cursor, Databricks Genie, Snowflake Cortex, LangChain, Google ADK, Vertex AI, and Copilot Studio.
• Ask DataHub serves the same graph to people through natural language. A new agent in any of those frameworks starts from the same context as every other agent.

At Trustpilot, engineers already reach the same lineage through the DataHub UI, chat, or the MCP integration inside Claude Code and Copilot.

Governed review and scoping for the consistency test:

• Context Hub in DataHub Cloud gives domain experts a place to review proposed context and resolve conflicting definitions before agents rely on them.
• Domain-scoped agents in DataHub Cloud let teams activate a narrower slice of the graph without forking it, such as a Marketing Domain agent that guides analysts to certified tables.

Write-back for the growth test: The DataHub MCP Server includes write tools alongside its read tools, so agents can tag assets, update descriptions, and assign ownership directly in the shared graph.

The platforms in your stack aren’t going anywhere, and a unified context layer doesn’t ask them to. What gets unified is the meaning: one graph, governed once, served to every person and agent that needs it.

FAQs

Is a unified context layer the same as a unified data layer?

No. A unified data layer, sometimes described as connected data, is about access to the data itself and the relationships between data points, usually by connecting or federating queries across sources. A unified context layer is about meaning: definitions, lineage, ownership, quality signals, and institutional knowledge. The two can coexist, and data can stay distributed across platforms while the context describing it lives in one governed graph.

Does a unified context layer require moving data onto one platform?

No. Unifying context doesn’t require consolidating data or platforms. Data stays in your warehouses, lakehouses, and source systems. The context layer sits beneath those platforms, describes the data where it lives, and pushes consistent definitions back out to each platform so people and agents see the same meaning everywhere.

Can Snowflake and Databricks agents share the same context?

Yes, if the context is authored outside both platforms. A unified context layer serves the same graph to agents in Snowflake Cortex, Databricks Genie, and other frameworks through open interfaces. When context lives inside one platform’s catalog, agents on other platforms can’t reach it, and each platform ends up with its own version of the truth.

How does a unified context layer handle unstructured data?

It treats unstructured context as first-class and links it to structured metadata. Runbooks, policies, FAQs, and decision logs sit in the same graph as schemas, lineage, and ownership, connected to the assets they describe. That lets an agent retrieve both the table it needs and the documented business rules that govern how to use it.

Does MCP create a unified context layer?

No. MCP is a transport protocol. It gives agents a standard way to reach context, but it doesn’t reconcile conflicting definitions or govern what agents receive. An agent connected to several MCP servers with different definitions of the same metric will still get inconsistent answers. Unification happens in the context graph behind the interface.

How do you know if a context layer is unified?

Apply three tests. Two agents asked the same question should give the same answer. A new agent should inherit the organization’s context on day one, without its own context project. And the layer should stay consistent as it grows, which requires agents to write what they learn back into the shared graph and humans to review it.