8 Context Layer Tools for AI Agents in 2027

Samuel Nagy

VP of Strategic Growth

November 4, 2026

In October 2026 Gartner put AI context platforms on its list of top strategic technology trends for 2027. Most data vendors now sell context for AI agents, from catalogs to warehouses to enterprise search. They start from very different places, and that decides what your agents will actually know. This guide compares eight tools and the questions that separate a context layer you can trust from a retrieval feature with a new name.

TL;DR

A context layer gives AI agents governed knowledge about your business and data: definitions, sources, lineage, sensitivity and ownership. Tools come from four starting points, and the catalog-born tools lead because they hold governed context across platforms. Catalogs (Dawiso and Atlan as market leaders, Alation), data platforms (Snowflake, Databricks, Microsoft Fabric IQ), transformation (dbt) and enterprise search (Glean). Judge them on five things: governed definitions with owners, reach across your platforms, agent access with the user's permissions, open protocols, and who controls the layer.

What a Context Layer Is

A context layer is what an AI agent reads before it answers or acts. It tells the agent what "net revenue" means in your company, which table is the approved source, where a number came from, whether a column holds personal data, and who answers for the definition. It is served to the agent over an open protocol, usually the Model Context Protocol (MCP), and limited to what the person using the agent is allowed to see.

None of this is new as a discipline. Catalogs, business glossaries, lineage and classification have governed data for years. What changed is the reader. When the reader was a person, a missing definition cost a meeting. When the reader is an agent that runs a query and sends the result to a customer, it costs a wrong answer at machine speed. That is why the old governance disciplines now carry a new name. For the line between this and metrics alone, see context layer vs semantic layer.

Why Context Layers Matter in 2027

On 20 October 2026 Gartner listed AI context platforms among its ten top strategic technology trends for 2027. Its warning is concrete: without a dedicated context layer, organizations face overprivileged agents, compliance failures, low trust and excessive token consumption. Gartner predicts that by 2027 organizations that prioritize semantics in AI-ready data will increase agentic AI accuracy by up to 80% and reduce costs by up to 60%.

The vendors moved first. During 2026 Snowflake introduced Horizon Context, Databricks announced Genie Ontology and Unity Catalog Metrics, Microsoft expanded Fabric IQ, and catalog vendors rebuilt their messaging around context for agents. The result is a crowded category where the same word covers very different products. Some govern definitions that people approve. Others learn context automatically from usage. Some reach across your whole estate, others go deep inside one platform. The rest of this guide sorts them out.

Four Starting Points

Every context layer tool started as something else, and that origin still shapes what it knows best.

Catalog-born tools started as data catalogs and governance platforms. They know the structured data estate across platforms, with definitions, owners, lineage and classification that people approve.
Platform-native context comes from the data platforms. It goes deepest inside its own platform and is now starting to read metadata from outside it.
Transformation-born context comes from the tools that model data, and is strongest on metrics defined in code.
Search-born context comes from enterprise search. It knows documents, conversations and people, with permissions from the source systems.

Click to enlarge

The 8 Tools at a Glance

The table compares the tools on the questions that matter most for agents, based on what each vendor documents. Several platform features are still in preview, which the descriptions below note.

Tool	Starts from	What agents get	Reach
Dawiso	Catalog and governance	Approved definitions, owners, lineage, classification over MCP, scoped to the user's role	Across the platforms it connects to, independent of any of them
Atlan	Catalog	Definitions, lineage and access rules over a hosted MCP server	Modern data stack, cloud-native
Alation	Catalog	Governed metadata for agents built on its platform	Broad catalog connectors
Snowflake	Data platform	Semantic views and Horizon Context, semantic views over MCP	Snowflake, with external metadata connectors in preview
Databricks	Data platform	Unity Catalog metrics and Genie Ontology	Databricks lakehouse
Microsoft Fabric IQ	Data platform	Ontology on Power BI semantic models for Fabric and Copilot agents	Microsoft Fabric and Microsoft 365
dbt	Transformation	Metrics and model metadata through the dbt MCP server	Wherever dbt models run
Glean	Enterprise search	Permission-aware search over documents and apps	SaaS apps and documents

1. Dawiso

Catalog-born, and one of the two market leaders alongside Atlan. Best for governed context across a multi-platform estate. Dawiso is a data catalog, business glossary, lineage and governance platform, and the same governed metadata is the context its MCP Server serves to agents. It reads metadata from more than 40 sources, including Snowflake, Databricks, SAP HANA, Microsoft Fabric and Power BI, and builds one view across them: what exists, what each term means, where each value came from, what is sensitive and who owns it.

Two choices set it apart for agents. First, definitions are governed: AI drafts descriptions and definitions, and the owner approves them, with version history, so the agent reads what Finance approved rather than what a model inferred. Second, access follows the person. Claude, Cursor and other MCP clients sign in with the user's own Dawiso account and get only the tools that user's role allows, which addresses the overprivileged-agent risk Gartner names. Seznam.cz runs 681,000 governed objects, with 31 people using AI tools through the Dawiso MCP Server.

Dawiso is also independent of every data platform and productivity suite on this list. As the platforms build context that works best on their own ground, Dawiso holds the cross-platform definition and lets you change an engine without rebuilding your business meaning. It can run as SaaS, in your own Azure or AWS tenant, or on-premises.

Key capabilities

Catalog, glossary, lineage and classification as one governed context
Human-approved definitions with owners and version history
MCP access scoped to each user's role
Governed semantic views written to Snowflake through Apache Ossie

Context an agent can trust is context someone approved, owns, and can trace.

See it in action

Dawiso Context Layer

Governed business context for any MCP-compatible agent.

2. Atlan

Catalog-born. Best for engineering-led teams on the modern data stack. Atlan has repositioned its catalog as a context layer for enterprise AI, with a hosted MCP server that gives agents certified definitions, lineage and access rules, and tooling for context engineering. It is built around Snowflake, Databricks and dbt.

The trade-off is cost: expect a higher entry price than Dawiso and compare total cost over three years. See Dawiso vs Atlan.

3. Alation

Catalog-born. Best for teams that want to build agents on catalog metadata. Alation has moved from data catalog to an agentic platform built on governed metadata, with an agent builder and SDK, helped by its 2025 acquisition of Numbers Station. Its catalog heritage gives it broad connectors and an established steward community.

The trade-off is that you are buying a new product with a new go-to-market, so check what the context features include and what they cost. See Dawiso vs Alation.

4. Snowflake Horizon Context

Platform-native. Best for Snowflake-first organizations. Introduced at Snowflake Summit in June 2026, Horizon Context turns Horizon Catalog into a governed context layer that collects metadata, enriches it with business definitions and relationships, and serves it to agents, BI tools and apps. Semantic views can be exposed over MCP, and in October 2026 Snowflake put metadata connectors for 18 external sources, including Power BI, Tableau, Looker, dbt, Databricks and BigQuery, into public preview. Snowflake has also agreed to acquire Natoma, an MCP gateway, to govern what agents can reach.

Most of it is still in preview or early access. The design choice to weigh is that the metadata of your other platforms becomes governed inside Snowflake. Dawiso works with it in both directions, scanning Cortex agents and semantic views into lineage and writing governed semantic views back, as described in context layer for Snowflake.

5. Databricks Unity Catalog and Genie

Platform-native. Best for lakehouse-first organizations. Unity Catalog governs data and AI assets in Databricks, and at Data + AI Summit 2026 Databricks added Unity Catalog Metrics, Domains and a Genie Ontology it describes as a continuously learned context layer, with a Business Glossary announced as coming soon. Genie answers with row-level security and masking applied.

Learned context is fast to build and needs review: an ontology inferred from usage reflects how data is used, not necessarily what the business has approved. The scope is the lakehouse. Dawiso scans Unity Catalog and Genie into lineage and adds the cross-platform glossary, as described in context layer for Databricks.

6. Microsoft Fabric IQ

Platform-native. Best for Microsoft 365 and Fabric estates. Fabric IQ builds on Power BI semantic models: the semantic model supplies the measures, and an ontology adds business entities and relationships that Fabric data agents, Copilot Studio and other agents can read. Fabric IQ is generally available, while the ontology itself was still labeled preview in Microsoft's documentation at the time of writing. Purview adds sensitivity labels and data security around it.

For organizations standardized on Microsoft it is the natural first step. Outside Fabric, the context thins out, and teams with Snowflake, Databricks or SAP alongside usually need a layer that spans them. See Dawiso as a Purview alternative.

7. dbt

Transformation-born. Best for analytics engineering teams. dbt holds a great deal of context already: models, tests, documentation, lineage between models and metrics in the dbt Semantic Layer. Its MCP server exposes that to agents, and MetricFlow is now open source as a reference implementation of Apache Ossie. Since 1 June 2026 dbt Labs and Fivetran operate as one company covering ingestion, transformation and metrics.

The context it holds is the context of what dbt builds. Source systems before dbt, BI reports after it, business ownership and classification of sensitive data are usually governed elsewhere. Dawiso reads dbt and connects it to the rest of the estate, as in context layer for dbt.

8. Glean

Search-born. Best for knowledge in documents and apps. Glean indexes documents, tickets, chats and other SaaS content and maps people and relationships in its enterprise graph. Its MCP server is permission-aware: search returns only what the user can already access, and tool calls run as the mapped user. It is built for questions whose answers live in documents and conversations.

Its starting point is unstructured content. It can find a document that defines revenue, but it does not tell an agent which table implements the approved definition or where the number came from. Many organizations pair search-born context for documents with catalog-born context for data, a split we explore in why pointing AI at SharePoint fails.

How to Choose

Gartner's list of risks is a good checklist: overprivileged agents, compliance failures, low trust and wasted tokens. Each maps to a question you can test in a proof of concept.

Question	Risk it addresses
Are definitions approved by an owner, or inferred by a model?	Low trust: agents repeat whatever the context says
Does the agent act with the user's permissions?	Overprivileged agents
Can it trace each answer to its source and owner?	Compliance failures and audit evidence, from SOX to California's automated decision-making rules
Does it span every platform your agents touch?	Agents guessing outside one platform's context
Does it return the governed answer, not a pile of candidate tables?	Excessive token consumption and wrong joins
Is it open (MCP, Apache Ossie) and independent?	Lock-in at the layer that is hardest to rebuild

The last question carries more weight than it did a year ago, as every data platform now sells its own context. Your context layer will hold the definitions of your whole business. Choose who controls it as deliberately as you choose where your data lives. For the implementation steps, see how to implement an enterprise context layer for AI.

Where Dawiso Fits

Dawiso is a catalog-born context layer and, with Atlan, a leader of this market: governed definitions with owners, lineage and classification across the platforms you run, served to any MCP-compatible agent with each user's permissions, and independent of the platforms it governs. It works alongside the platform-native context in Snowflake, Databricks and Fabric, alongside dbt, and next to document search, and gives all of them one approved definition of the business to agree on. The metrics side of the market is covered in 8 semantic layer tools for 2027.

FAQ

What is a context layer?

A context layer is the governed knowledge an AI agent reads before it answers or acts: what business terms mean, which data exists and where, where a number came from, what is sensitive, and who owns each definition. It is assembled from a data catalog, business glossary, lineage and classification, and served to agents over an open protocol such as MCP, with access limited to what the user may see.

What are the best context layer tools for 2027?

The tools most often evaluated come from four starting points: catalog-born context layers (Dawiso, Atlan, Alation), platform-native context (Snowflake Horizon Context, Databricks Unity Catalog and Genie, Microsoft Fabric IQ), transformation-born context (dbt) and enterprise search (Glean). Catalog-born tools reach across the most platforms, platform-native tools go deepest inside their own platform, dbt covers metrics in code, and Glean covers documents and conversations.

What are AI context platforms according to Gartner?

On 20 October 2026 Gartner named AI context platforms one of its top strategic technology trends for 2027, describing them as platforms that give AI agents the contextual knowledge they need to interpret inputs, make decisions and act in line with business objectives. Gartner warns that without a dedicated context layer, organizations face overprivileged agents, compliance failures, low trust and excessive token consumption.

What is the difference between a context layer and a semantic layer?

A semantic layer defines metrics: what revenue means and how it is calculated. A context layer is wider. It includes those definitions and adds a catalog of the whole data estate, lineage, classification of sensitive data and ownership, so an agent knows not only what a number is but whether it can be trusted and used. Most organizations run one or more semantic layers beneath a context layer.

Is RAG over company documents a context layer?

It is part of one. Retrieval over documents gives an agent relevant text, but it does not tell the agent which definition is approved, which table is the source of truth, or who owns a number. A context layer adds governed structured knowledge next to retrieval. Enterprise search tools like Glean are strong on the document side; catalog-born tools are strong on the governed data side.

Should the context layer come from my data platform vendor?

If all your data and agents live on one platform, its native context is the fastest start and goes deepest. If you run several platforms, or want to change one without rebuilding your business definitions, keep the context layer independent. Your context layer holds the definitions of your whole business, so it should not depend on staying with one provider.