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Best Context Layer Platforms for AI Agents in 2026
An AI agent is only as good as the context it gets. Pointed at raw tables and scattered documents, it guesses what things mean, which records connect, and who is allowed to see them. A context layer replaces those guesses with governed answers.
This guide compares the eight platforms that show up most in “context layer” evaluations in 2026: Atlan, DataHub, Snowflake Horizon Context, Databricks Unity Catalog and Genie, Fluree, Microsoft Fabric IQ, Glean, and Collibra. For each one: what context it holds, how it reaches agents, where policy is enforced, and what it costs. AWS Context is not profiled because AWS still lists it as coming soon.
Every vendor claim on this page is sourced and was last verified October 2026.
What is a context layer for AI agents?
An enterprise AI context layer is the governed layer between your data estate and your AI agents that turns fragmented enterprise data into trusted, queryable context: entities resolved, relationships explicit, policies enforced, and every answer traceable to versioned source.
Vendors build that layer from different starting points. Catalogs start from metadata: definitions, owners, and lineage. Data platforms start from the tables they already govern. Search tools start from documents and permissions. Knowledge graphs start from the business facts (see our enterprise knowledge graph platform page). Nearly all of them now hand context to agents over MCP (the Model Context Protocol, the open standard agents use to connect to tools).
So the useful question is not who uses the label. It is what an agent receives: a description of your data, or a governed answer drawn from it. A context layer usually contains a semantic layer and goes further. See context layer vs. semantic layer for the difference, the semantic layer tools shortlist for metrics-first options, and the guide to the enterprise AI context layer for the full architecture.
The four types of context layer platforms
Same label, four starting points. Knowing which type a vendor is will shorten your evaluation more than any feature list.
Metadata and catalog context layers
Context is metadata about your data: definitions, owners, lineage, quality, and policies, harvested across the stack and served to agents over MCP. The widest reach across tools. The data itself stays in the source. Atlan, DataHub, and Collibra.
Platform-native context
Context lives inside one data platform, next to the data, under that platform’s permissions. The least new architecture, and it ends at the platform edge. Snowflake Horizon Context, Databricks Unity Catalog and Genie, and Microsoft Fabric IQ.
Workplace knowledge context
Context is an index of documents, messages, and app content, plus a graph of people and activity, with each source’s permissions mirrored. Strongest for knowledge work. Numbers in tables come from live tool calls. Glean.
Knowledge-graph context layers
Context is the business facts themselves, modeled as a governed graph with policy and history kept with the data. Agents get answers, not pointers. Fluree.
Six capabilities to evaluate
These define the comparison table’s columns and the fields inside every profile below.
What the context holds
Metadata about data, a semantic model, a document index, or the business facts themselves. This decides whether an agent receives a pointer or an answer.
Sources and data movement
Which systems it reaches, and whether it reads data in place, copies it into its own store, or harvests only metadata.
Governance and permissions
Where access policy is enforced (at the source, in a gateway, or in the data) and whether it applies the same way to people and agents.
Agent interfaces
MCP servers, APIs, and agent builders, and whether each one is generally available or still in preview.
Provenance and history
Lineage, citations, and audit logs, and whether you can see what the context said at a past point in time.
Openness and pricing
Open-source or source-available options, open standards, and whether prices are published at all.
Context layer platforms at a glance
One row per platform: best for, plus the three capabilities that most often decide the choice. The remaining criteria are answered in full inside every profile.
Short explanations rather than checkmarks — the remaining criteria are answered in full inside each profile below. Verified October 2026.
The best context layer platforms for AI agents
Eight platforms, profiled against the same six criteria. Fluree is ours, and its card says so. Every profile names where that vendor is strongest.
Atlan
The metadata context layer: definitions, lineage, and policy from across the stack, served to agents over MCP.
Atlan calls itself “The Context Layer for AI”, and it has done more than most vendors to make the term mainstream. It crawls the data estate, builds a graph of assets, definitions, lineage, and policies, and serves that context to agents. In its own 174-query benchmark, adding that context improved text-to-SQL accuracy by 38% in relative terms.
What it holds: Metadata about your data: definitions, column-level lineage, access and masking policies, and usage. Atlan extracts metadata only and never your business data. Its Context Engineering Studio holds semantic models (dimensions, facts, relationships, metrics, verified queries) that it can deploy to Snowflake Cortex Analyst and Databricks Genie.
Sources and data movement: 108 connectors crawl structure, lineage, and operational metadata on a schedule. Atlan states that your actual data never leaves your systems. Context is stored in an Apache Iceberg lakehouse that Snowflake, Trino, or Spark can query.
Governance and permissions: The MCP server forwards the caller’s identity to the same persona, role, and domain policies that govern the Atlan UI. Live-data queries also need query permission on the source connection.
Agent interfaces: A hosted remote MCP server enabled for every tenant, with OAuth or API keys. Its tools cover search, lineage, glossary updates, and read-only SQL that returns up to 100 rows. REST APIs plus Python, Java, and JavaScript SDKs.
Provenance and history: Automatic column-level lineage across warehouses, pipelines, and BI tools. Every MCP tool call is logged as a structured event.
Openness: A single-tenant SaaS platform on AWS, Azure, or GCP. The core platform is proprietary; the context store uses open Iceberg tables.
Pricing: Not published. The pricing page opens a sales conversation.
Key features
- Metadata, lineage, and policy across 100+ sources
- Hosted MCP server on every tenant
- Semantic models deployed to Cortex Analyst and Genie
- Context stored in open Iceberg tables
Best for
- Enterprises whose first problem is finding and trusting the right data
- Multi-platform estates running Snowflake, Databricks, and several BI tools
- Governance teams that want one catalog for people and agents
Where it falls short: Atlan describes your data; it does not hold it. When an agent needs an actual number, Atlan runs SQL at the source (100 rows per MCP call) or hands a semantic model to Cortex or Genie, so answer quality still depends on those engines. The core platform is SaaS only.
Verified: October 2026
DataHub
The open-source metadata graph, rebuilt as a context platform for agents.
DataHub now bills itself as “The Context Platform for AI Agents”. DataHub Core is free and open source; DataHub Cloud adds the managed service, finer access control, and the agent features. Its own map of context management tools places DataHub Cloud in the enterprise context platform layer, alongside the catalogs it competes with.
What it holds: Technical, operational, and business context: schemas, lineage, ownership, query patterns, freshness, quality scores, glossary definitions, and runbooks. In DataHub’s words, it helps the AI find the right data while the warehouse answers the question.
Sources and data movement: Metadata only, pulled on a schedule or pushed from code, through 100+ pre-built connectors. Answering with live data goes through Ask DataHub plugins that hand the query to the warehouse, a private beta in DataHub Cloud.
Governance and permissions: Platform and metadata policies control who can see and change what, and MCP tokens are scoped to the signed-in user. DataHub says Core ships with basic access controls; role- and attribute-based control is a Cloud feature.
Agent interfaces: An open-source MCP server that works with Core or Cloud, plus a managed MCP endpoint in DataHub Cloud, and GraphQL and REST APIs. Ask DataHub, custom agents, and scoped MCP servers are public beta.
Provenance and history: Column-level lineage, captured by ingestion or added by hand, and a Timeline API that shows version history for schemas, documentation, tags, and glossary terms.
Openness: The strongest open-source story on this page: DataHub Core is Apache 2.0. Self-hosting it means your team owns install, upgrades, and uptime.
Pricing: Core is free and open source. DataHub Cloud is quoted by sales, scoped to your use cases and data environment.
Key features
- Apache 2.0 open-source core
- Column-level lineage and metadata version history
- Open-source MCP server for Core or Cloud
- Context drafted automatically from query logs and dbt projects
Best for
- Engineering-led teams that want an open-source metadata foundation
- Complex pipelines where lineage is the first question
- Teams that want to self-host first and buy managed later
Where it falls short: Like every catalog, DataHub tells an agent where the right data is and what it means; the answer still comes from the warehouse. The features that close that gap (Ask DataHub, its plugins, custom agents) are beta and Cloud-only, and the open-source path trades license cost for operating cost.
Verified: October 2026
Snowflake Horizon Context
Governed semantics inside the warehouse, enforced at query time.
Snowflake announced Horizon Context in June 2026 as “the context layer for AI and BI”. It extends Horizon Catalog with metadata from outside Snowflake and builds on semantic views, the schema objects Cortex Analyst and Cortex Agents use to turn questions into governed SQL. Horizon Context itself entered preview on October 12, 2026.
What it holds: Business definitions next to the data. Semantic views define logical tables, relationships, facts, dimensions, and metrics over physical tables. Because the data sits in the warehouse, Cortex can answer with it, not just describe it.
Sources and data movement: Answers come from data Snowflake can query: native tables, Iceberg tables, and catalog-linked databases. Metadata connectors (preview) bring in metadata and lineage from 18 sources, including Power BI, Tableau, dbt, Databricks, and BigQuery.
Governance and permissions: Cortex Analyst fully integrates with RBAC, and masking and row access policies on underlying tables propagate to semantic views. MCP access is granted tool by tool.
Agent interfaces: A Snowflake-managed MCP server (GA since November 2025) exposes Cortex Agents, Cortex Search, Cortex Analyst, and SQL. It supports semantic views through Cortex Analyst only.
Provenance and history: Lineage and access history on Enterprise Edition, and Time Travel of up to 90 days. Column lineage is not supported for semantic views.
Openness: Proprietary and Snowflake-hosted. Data can live in open Iceberg tables, and Snowflake’s best practices say to store semantic view YAML or DDL in Git yourself.
Pricing: Semantic views carry no separate line item. Cortex Analyst bills 67 credits per 1,000 messages through its API (token-based AI credits inside Cortex Agents), plus warehouse compute for the SQL it runs.
Key features
- Semantics and data in one governed engine
- RBAC, masking, and row policies flow through to agents
- GA managed MCP server
- Metadata from 18 outside sources (preview)
Best for
- Snowflake-centric estates that want context with no new vendor
- Teams already building on Cortex Analyst or Cortex Agents
- Metric-heavy questions answered as governed SQL
Where it falls short: Context is strongest for data Snowflake governs. Outside sources arrive as metadata through preview connectors, and answers come from data Snowflake can query. History is edition-gated: lineage needs Enterprise Edition and Time Travel stops at 90 days, so it is not a durable record of what the context said last year.
Verified: October 2026
Databricks Unity Catalog + Genie
Lakehouse context: governed semantics in Unity Catalog, delivered through Genie.
Databricks pitches Unity Catalog as “open and adaptive context for agents, apps and BI”. Unity Catalog Semantics went GA in April 2026 and holds the modeled definitions; the Genie Ontology adds context that Genie learns from usage. In Databricks’ own words, the catalog has moved from a system of record to a runtime decision-maker for AI.
What it holds: Modeled and learned context next to the data: metric views, domains, and certification, plus the Genie Ontology (public preview), a map of snippets Genie extracts from metric views, dashboards, SQL queries, and Genie Agents. Genie answers with the data itself.
Sources and data movement: Delta and Iceberg tables in the lakehouse, plus Lakehouse Federation for governed, read-only queries against MySQL, PostgreSQL, Oracle, Snowflake, BigQuery, and others where they live.
Governance and permissions: Unity Catalog row filters and column masks are enforced per user in Genie Agents, and Genie Ontology snippets are gated by the same permissions.
Agent interfaces: The Genie One MCP server went GA in September 2026 inside Unity Gateway, with audit logging on every call. Several other managed MCP servers are still beta or preview.
Provenance and history: Unity Catalog captures lineage to the column level where it can; lineage system tables keep a rolling one-year window. Delta table history is short by default, and Databricks advises against using it as a long-term backup.
Openness: Unity Catalog OSS is Apache 2.0, a sandbox project at LF AI & Data, and Databricks says metrics are open source in Apache Spark and Unity Catalog OSS.
Pricing: Unity Catalog Semantics is free, with no separate SKU; you pay compute and storage. Genie One and Genie Agents usage is free through January 31, 2027, with the compute behind Genie’s queries billed separately.
Key features
- Metric views and semantics GA in Unity Catalog
- Row filters and column masks enforced per user in Genie
- GA Genie One MCP server with audit logging
- Read-only federation to external databases
Best for
- Lakehouse-first teams standardized on Unity Catalog
- Organizations that want Genie grounded in governed metrics
- Buyers who value an open-source catalog
Where it falls short: Context is served through Databricks compute and Genie. Federation is read-only, the Genie Ontology is still public preview, and its map is a ranked set of snippets Genie extracts rather than a model you define end to end. Table history is short-lived by design.
Verified: October 2026
Fluree
The enterprise AI context layer: governed, cited answers for people and agents.
Fluree is our product, so read this card with that in mind. Every other claim on this page links to the vendor’s own docs, and every profile says where that vendor is strongest. Fluree takes a different route from the catalogs: instead of describing your data, it turns it into a governed knowledge graph and answers from it. It’s one product, Fluree AI, with three capabilities: taxonomy and ontology management, golden records from your tables, and graph facts from your documents. Underneath is the engine, FlureeDB, a verifiable knowledge graph database that developers can also run themselves.
What it holds: The business facts themselves, as a knowledge graph of entities, typed relationships, and the ontology that defines them. People and agents get cited answers, not pointers to tables. In Fluree’s 2024 study, GraphRAG for GenAI Accuracy, answers over raw relational tables scored 8–15% zero-shot; a governed knowledge graph reached 95%+ once enriched and joined.
Sources and data movement: Lakehouse tables (Iceberg, Delta) and SQL databases (via Trino/Starburst or a SQL bridge) are read where they live. Only Iceberg tables can optionally keep a synced copy in Fluree. Documents become graph facts stored in Fluree. There is no CDC, and SaaS connectors are in hosted Fluree AI only.
Governance and permissions: Policy is enforced inside the database, on every fact, before data reaches a model. The same rules apply to a person asking a question and an agent calling a tool.
Agent interfaces: An MCP server for agents, plus SPARQL, openCypher, GraphQL, and JSON-LD queries.
Provenance and history: Every commit is signed, and time travel lets you query the graph as of any past commit, so you can check what the data said when an agent answered.
Openness: FlureeDB is source-available under the Business Source License 1.1: free in production, including inside your own product; a commercial license is needed only to offer it as a hosted database service. Each release converts to Apache 2.0 on its fourth anniversary.
Pricing: FlureeDB is free to run in production. Fluree AI is free to start with a fuel allowance, with usage pricing on request. Enterprise is custom.
Key features
- Cited answers from the business facts themselves
- Policy enforced in the database on every fact
- Time travel and signed commits for audit
- MCP server plus SPARQL, openCypher, GraphQL, and JSON-LD
Best for
- Agents that must answer from governed business data, not just find it
- Regulated teams that need to show what an agent could see, and when
- Estates that span lakehouse tables, SQL databases, and documents
Where it falls short: Fluree is not a metadata catalog. It doesn’t harvest owners, lineage, and quality scores from across your tool stack the way Atlan, DataHub, or Collibra do, and a catalog can sit alongside it. Data movement has limits: no CDC, synced copies for Iceberg only, and SaaS connectors in hosted Fluree AI only. The bigger investment is a governed model of your business. It takes more work than tagging tables, and it is what lets agents answer instead of guess.
Verified: October 2026
Microsoft Fabric IQ
An ontology over OneLake, wired into Copilot, Foundry, and Copilot Studio.
Fabric IQ is the business-data part of Microsoft IQ, which Microsoft calls “a unified intelligence layer for enterprise AI.” Its center is the ontology: entity types, properties, relationships, rules, and metrics bound to live Fabric data, which Microsoft describes as a common context layer. The ontology is still labeled preview in Microsoft’s docs.
What it holds: Definitions and bindings, not data: the ontology “isn’t itself a general-purpose data-query engine.” Metrics come from Power BI DAX measures, and agents run their queries against the bound sources.
Sources and data movement: Bindings cover semantic models, lakehouses, warehouses, eventhouses, SQL and mirrored databases, and OneLake shortcuts. Source data stays in the system that owns it, though mirroring replicates data into OneLake and an optional graph materializes a copy.
Governance and permissions: Querying respects OneLake security and source row-, object-, and column-level security. Fabric data agents use the requesting user’s credentials and keep read-only connections.
Agent interfaces: Data agents (GA) publish to Microsoft 365 Copilot, Foundry, and Copilot Studio, and a published agent becomes an MCP server. The ontology MCP server is preview. Data agent answers are capped at 25 rows and 25 columns.
Provenance and history: Named version history (preview) for the ontology definition, item-level lineage, and Purview audit of Fabric activity.
Openness: The ontology can import and export Turtle and RDF files and import OWL (preview), unusual for a platform vendor. Everything runs on Fabric capacity.
Pricing: No separate fee: Fabric IQ draws on Fabric capacity units. Microsoft documents 1,000 CU-seconds per ontology discovery plus dynamic AI consumption, and data agents need a paid F2 or larger capacity.
Key features
- Ontology bound to live OneLake and Power BI data
- Builds on Power BI semantic models already in production
- Permissions enforced as the requesting user
- Distribution into Microsoft 365 Copilot and Foundry
Best for
- Microsoft-standard enterprises with Power BI semantic models in production
- Teams building agents in Copilot Studio or Foundry
- Organizations already running on Fabric capacity
Where it falls short: The core ontology and its MCP server are preview, which Microsoft’s own Foundry docs say comes without a service-level agreement and isn’t recommended for production. Context assumes a Fabric and OneLake estate, version history covers the ontology definition rather than the data, and data agents cap answers at 25 rows.
Verified: October 2026
Glean
Permission-aware context from workplace apps, documents, and people.
Glean comes at context from enterprise search. It indexes content across 275+ connectors and maps people, content, and relationships in its Enterprise Graph, part of what Glean calls its “system of context.” That context reaches other agents through a managed MCP server. For a head-to-head, see Fluree vs Glean.
What it holds: An index of full text, metadata, structured entities, identities, permissions, and activity signals, plus an Enterprise Graph that Glean builds with a machine learning pipeline to infer projects, customers, products, and teams.
Sources and data movement: Content is copied into Glean’s index by design: “Index your data once, then use it everywhere.” Warehouse numbers come from live tool calls, such as read-only SQL against Databricks.
Governance and permissions: Connectors ingest each source’s permission model, and Glean checks the signed-in user against it at query time. Agents run with the identity of the user who triggered them.
Agent interfaces: A Glean MCP server that is fully managed and on by default with OAuth, plus search, chat, and agent APIs and an indexing SDK for custom sources.
Provenance and history: Sentence-level citations that deep-link to the source passage, and admin audit logs that stream to a SIEM. Point-in-time queries over source content are not publicly documented.
Openness: Closed source and single-tenant: Glean-hosted on GCP, or customer-hosted in your GCP or AWS account, managed by Glean either way.
Pricing: No published seat price. Glean’s docs describe per-user, per-month licensing plus model usage at provider rates, or an Enterprise Flex plan with pooled credits.
Key features
- 275+ connectors across apps, documents, chat, and code
- Source permissions checked at query time
- Managed MCP server, on by default
- Sentence-level citations
Best for
- Knowledge-work questions that span SaaS apps and documents
- Enterprises that want one permission-aware index for many AI tools
- Teams that want context without building pipelines
Where it falls short: Glean is index-first: content is copied into its tenant, and freshness depends on its crawlers. The Enterprise Graph is inferred by machine learning rather than modeled by you, and browsing it is still listed as coming soon. Governed business facts in tables stay in the warehouse, reached by live tool calls.
Verified: October 2026
Collibra
Governed business meaning and AI controls, delivered to agents over a GA MCP server.
Collibra positions itself as “the enterprise standard for AI context and control”. It brings a long-standing glossary, catalog, lineage, and policy model, plus AI governance through AI Command Center. Its case is neutrality: a definition of “customer” or “revenue” isn’t owned by any one platform.
What it holds: Metadata and meaning: definitions, classifications, lineage, ownership, and policies, plus a registry of AI use cases, models, and agents. Agents still query Snowflake or Databricks for the values.
Sources and data movement: 100+ out-of-the-box integrations harvest metadata through JDBC, Edge, OpenLineage, and APIs. A governed context compiler (preview) publishes semantic models as Snowflake semantic views and Databricks metric views.
Governance and permissions: Collibra Data Access (GA July 2026) pushes grants and masks down to Snowflake, Databricks, and BigQuery rather than acting as a proxy. MCP actions use Collibra’s existing permission model.
Agent interfaces: An MCP server, GA since November 2025, with 25+ governance tools for reading and writing, a hosted option, and an open-source local version. REST and Java APIs.
Provenance and history: Technical and business lineage, AI lineage from training data to usage, and change history on assets. Point-in-time queries are not publicly documented.
Openness: A proprietary platform. The local MCP server is open source under Apache 2.0.
Pricing: Not published. Collibra sells packages with an annual balance of Collibra Units; the MCP server is free to use, though some tools consume units.
Key features
- Business glossary and policy across platforms
- GA MCP server with read and write tools
- AI Command Center for AI use cases, models, and agents
- Access policies pushed down to the data platforms
Best for
- Regulated enterprises with an established governance program
- Teams that must govern AI use cases alongside data
- Multi-cloud estates that want definitions owned by no single platform
Where it falls short: Collibra governs meaning and policy; it does not hold or answer from the business data. Its value depends on stewardship, and several agent-era pieces (the context compiler, Guardian Agents, Agent Contracts, Live Map) are still preview or coming soon.
Verified: October 2026
How to choose the best context layer for your agents
Find the situation that sounds like yours. Each one resolves to a single recommendation.
“We can’t find or trust our data, and our agents inherit the confusion.”
Atlan
Broad metadata coverage, column-level lineage, and a hosted MCP server that carries your governance policies to agents.
“We want an open-source foundation we can run ourselves.”
DataHub
An Apache 2.0 core, deep lineage, and an open-source MCP server, with a managed cloud when you want one.
“Nearly all of our data already lives in Snowflake.”
Snowflake Horizon Context
Semantics and data in one governed engine, with RBAC flowing straight through to a GA MCP server.
“We’re lakehouse-first, and Genie is where our people ask questions.”
Databricks Unity Catalog + Genie
Free semantics in Unity Catalog, per-user permissions in Genie, and a GA MCP server.
“Agents must answer from governed business data, and we have to prove what they saw.”
Fluree
Business facts in a governed knowledge graph, policy on every fact, and time travel to any past commit.
“We run on Microsoft, and Power BI models already hold our definitions.”
Microsoft Fabric IQ
Turn existing semantic models into an ontology and reach agents in Copilot and Foundry, with a plan for the preview period.
“Our questions live in documents, tickets, and chat, not in tables.”
Glean
A permission-aware index of workplace knowledge, delivered to any agent over MCP.
“One team owns data governance and AI risk, across several clouds.”
Collibra
Glossary, policy, and an AI registry in one place, with a GA MCP server and access pushed down to each platform.
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