Context Layer Components for AI Agents: What It Takes to Build Each One

According to Gartner® , D&A leaders must architect a robust context layer that enables AI agents to efficiently retrieve, organize, and select relevant knowledge for specific tasks. This context layer should incorporate the following components:

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

Those three components are the right lens, in our view. They describe what a context layer contains, which is the kind of architectural clarity the field has been short of for the past year.

What the framework deliberately does not do is tell you how to build one. According to Gartner®, “No vendor currently offers a fully out-of-the-box solution for a robust context layer. Interoperability between commercial and custom-built components is essential, so plan for integration from the outset.”¹ That’s the honest position, and at DataHub, we agree with it.

The three components, defined

A context layer is the dedicated architectural component that curates, integrates, and delivers the information AI agents need to interpret, decide, and act reliably. The Gartner® framework names three components inside it:

Component	Definition
Semantics	“The explicit representation of meaning and relationships within business data. It includes the development and use of ontologies and knowledge graphs, which act as the connective tissue linking data, metrics, rules, security, ownership, and policies.”¹
Operational state	“An agent’s ability to access right-time information about key business entities such as customers, products, or suppliers, as well as the current status of business processes and environmental conditions.”¹ Gartner® recommends to “utilize protocols such as the model context protocol (MCP) to provide AI agents with seamless and secure access to relevant data required for their tasks, reducing friction and latency in decision making.”¹
Provenance	“The ability to systematically track data lineage, decisions, actions, outcomes, and feedback throughout the life cycle of AI agent operations. Robust provenance mechanisms allow AI agents to reference historical decisions and their results, supporting continuous improvement, adaptation, and auditability.”¹

Together, the three components are what a context layer enables: agents that interpret data correctly, act on current state, and explain their reasoning.

The engineering reality of each component

We believe each of the three components carries its own engineering reality:

1. The cold start problem dominates semantics

You cannot workshop your way into a semantic layer that covers thousands of tables. Customers tell us manual context creation runs about 16 hours per table, which is the math that makes a global rollout untenable before it begins.

Even if the initial documentation gets written, the artifacts decay. Schemas change. Metric definitions get rewritten. Business logic evolves. The drift starts the moment the workshop ends, and within a few quarters, the documentation is closer to a historical record than a current source of truth.

DataHub’s answer: Continuous generation, then SME validation

Context Intelligence runs continuously, generating semantic context automatically from behavioral signals already embedded in the data estate: query logs, dbt semantic models, BI dashboard patterns, and usage patterns.

It filters for high-signal queries, the multi-table joins, interactive user sessions, and high-frequency multi-user patterns that carry the most semantic weight. From those signals it infers metric definitions, common query patterns, and example questions. The output is surfaced as Context Documents linked to the data assets they describe.

The behavioral substrate is what makes this work. The organization has already encoded enormous amounts of business meaning in how its analysts and engineers query the warehouse. The job is to surface that meaning, not to reinvent it.

Automated generation produces a starting point, but not a finished product. Inferred context is grounded in what the organization has been doing historically, which is the right substrate, but it still needs human judgment before it becomes the canonical answer. Subject matter experts need the opportunity to review, refine, enrich, or correct.

Context Hub is the workbench that makes that validation tractable at enterprise scale. It routes divergent metric definitions to the right domain expert. It lets SMEs simulate the downstream impact of their edits on text-to-SQL accuracy before publishing changes. And it compresses what used to be a multi-hour workshop into a roughly five-minute review.

Corrections feed back into the continuous improvement loop, so the system sharpens with use rather than going stale.

2. Freshness and breadth define operational state

A catalog that ingests on a batch schedule will tell an agent what was true yesterday, not what is true at the moment of decision. That is the failure mode that produces confidently wrong answers in production.

An agent quotes last quarter’s revenue as today’s number. It joins against a deprecated table because the deprecation has not propagated. It reasons against a policy that was rewritten three weeks ago and is still being served from a cached document.

The breadth problem runs in parallel. Most organizations don’t have one data platform, they have ten. Metadata lives in the warehouse, the lakehouse, the transformation layer, the orchestration system, the BI tool, and a dozen SaaS sources that each maintain their own operational state. A context layer that only reaches the primary cloud platform, or whatever your catalog ingested last Tuesday, gives the agent a partial map. It reasons confidently from an incomplete picture of what’s actually running.

DataHub’s answer: Event-driven architecture, delivered through MCP

The architectural requirement is event-driven, not scheduled. DataHub’s context platform is built on an event-driven architecture that propagates changes continuously across the unified context graph.

When a Notion document is updated, the change reflects in the context layer. When a schema changes upstream, column-level lineage updates with it. When a dbt model ships with new semantic definitions, those definitions become current immediately rather than waiting for the next ingestion window.

DataHub collects operational metadata from over 100 systems spanning warehouses, lakehouses, transformation tools, orchestrators, BI platforms, and SaaS sources, so the context graph reflects the full operational picture rather than a single platform’s view of it. The Notion and Confluence connectors bring unstructured organizational knowledge into the same context graph as structured metadata. Runbooks, FAQs, policy documents, and business definitions all live alongside schema, lineage, and data quality signals.

Agents query both through a single interface rather than stitching together separate retrieval paths for each source. We feel this is the practical answer to the structured-plus-unstructured requirement Gartner® names.

How agents reach the graph at runtime is its own design decision. The DataHub MCP Server exposes the unified, continuously updated context graph to any MCP-compatible agent without per-framework integration work.

Alongside MCP, the Agent Context Kit provides SDKs for LangChain, CrewAI, Google ADK, Vertex AI, Snowflake Cortex, and Copilot Studio. Agents built in those frameworks inherit the same context the platform exposes elsewhere. Skills, API and SDK surfaces, and personalized UX cover the same ground for human consumers and embedded use cases.

The surface is read-write, not read-only. Agents can propose glossary terms, update descriptions, and flag ambiguities as they work, and those contributions feed back into the same SME validation loop. The context layer compounds in value as agent traffic grows rather than acting as a static repository.

3. Automation and column-level resolution define provenance

Two architectural decisions determine whether provenance actually works at production scale: whether it is captured automatically or manually instrumented, and whether it operates at the table level or the column level.

Manual instrumentation does not scale across the modern data stack. There are too many systems, too many transformations, and not enough engineering time to wire each one by hand.

Table-level lineage falls short for a different reason: The numbers agents need to defend are usually columns in downstream reports, not tables. Provenance that can’t follow a value column by column can’t actually answer where a number came from. And without that traceability, AI outputs can’t be defended to a regulator or an audit team.

DataHub’s answer: Automatic column-level lineage across the stack

DataHub captures cross-platform, column-level lineage automatically across more than 100 systems, including Snowflake, Databricks, dbt, Looker, and the rest of the typical enterprise stack.

Every column’s transformation history is traceable from raw source to final output. When an agent surfaces a metric, the lineage shows where the underlying values originated, what transformations they passed through, and which definitions applied at each step.

Two practical consequences follow:

• Agents can explain where a number came from rather than asserting it, which is the difference between a defensible answer and a confident guess
  
• Compliance teams have the audit trail they need without retrofitting it after the fact, which matters more as the regulatory landscape around enterprise AI continues to land in jurisdictions that did not have AI-specific rules a year ago.
  

The architecture is right. The implementation is the work.

We believe that Gartner® is right that no vendor delivers a context layer fully out of the box, and we shouldn’t pretend otherwise. Every organization that takes this seriously will engineer something. The question is how much.

The three components are useful precisely because they let you make that comparison concrete rather than abstract. Most organizations underestimate the semantics work and overestimate what their existing data catalog handles. Operational state is where catalogs designed for human discovery break down hardest, because they were built for a different consumption pattern. Provenance is the easiest of the three to neglect, until an auditor or a regulator asks.

A context platform that absorbs significant parts of all three is a different proposition from a tool that automates one component well and leaves the other two to custom engineering. That is the architectural choice underneath the platform choice.

Context management as a discipline depends on having all three components in place. The competitive surface for context platform vendors, including DataHub, is implementation: how much of the work the platform handles, how continuously it runs, and how reliably it scales as the data estate keeps changing underneath it.

What separates a prototype from a production context layer is whether all three components hold up over time. That is where I would encourage CTOs and D&A leaders to focus the evaluation.

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