There’s a context crisis nobody warned you about. While building a context layer for an AI agent could look like a deceptively simple undertaking, what might sound like a low-effort weekend project can quickly bottleneck around sync pipelines, schema mapping, permission layers and staleness guarantees. What AI agents actually need is a context engine that delivers the right context, in the right format, at the right time. That is emphatically not something you cobble together over a weekend.1 ( TLDR Tech, June 2026)

This structural gap is precisely why so many enterprise AI initiatives stall before reaching production. Without a reliable context layer, models are forced to operate on raw, ungoverned data, which can cause agent accuracy to plummet and expose businesses to significant operational risk.2

Microsoft CEO Satya Nadella put an economic framework to this exact challenge. In his post on the future of the firm, which racked up more than 65 million views, he argued that every company must build "token capital": the unique AI capability a firm builds and owns. The real opportunity is not picking the best model; it is building a learning loop that compounds organizational expertise on top of models and compounds it over time. He named a queryable knowledge base as the new IP of the firm.3

In other words, context isn’t a feature. In today’s agentic AI world, It’s the moat.

But context is also a living organism. It decays. It drifts. As Dr. Jerry Smith argues, your ontology is wrong the moment you finish it. Business terms evolve with strategy, lineage shifts with pipeline migrations, and regulatory policies change with legislation. The companies that win the agentic era are not those who built context once and declared victory. They are those who built the infrastructure to keep context continuously certified, governed and alive.

That is the problem Collibra was built to solve, and it is the only vendor with a maturity curve that meets every type of user exactly where they are, from business steward to AI engineer.

The context maturity curve: Three ways Collibra meets you where you are

Context maturity is not a binary. Organizations range from those still unifying their data estate to those embedding governance directly into engineering workflows. Collibra Platform delivers three progressively deeper layers of context, each backed by a full suite of governance capabilities.

Level 1: Connected context: Building the foundation of certified truth

Trust begins with unification. Before any agent can act reliably, the enterprise needs a governed view of its data estate that answers four foundational questions:

Who owns it?
What does it mean?
How does it flow?
Can it be trusted?

The Collibra Data Catalog ingests metadata from every corner of the enterprise and stitches it into a unified knowledge graph. Collibra Data Lineage traces every dataset from origin to consumption so agents understand provenance before they act. Collibra Data Quality & Observability monitors quality continuously, surfacing anomalies before they corrupt downstream decisions. And our compliance capabilities embed policy enforcement as a structural property of every registered asset, treating governance not as a post-hoc audit but as a design-time requirement.

What makes connected context differentiated is ecosystem reach. Collibra offers a bi-directional sync of business terms and policy tags to partner catalogs, including Snowflake and Databricks.

When the business changes the definition of "revenue," that change propagates across every connected environment, automatically and without manual reconciliation.

Competitors in the catalog space treat metadata as a static inventory: They index assets, but they don’t govern them. Collibra ensures every asset carries a certification state, a quality score, a policy tag and a lineage chain. That’s the difference between knowing what data exists and knowing whether it can be trusted.

Level 2: Actionable context: Governance in the flow of work

For organizations already iterating in Databricks, Snowflake, or custom agent frameworks, governance delivered only through a UI is governance that gets bypassed. If you want AI developers to use certified context, you have to deliver it where they are already working.

The Collibra MCP Server is the centerpiece of actionable context. It gives AI agents a standard, runtime path to query your governed knowledge graph, surfacing certified business term definitions, data quality status, lineage, ownership and policy classifications directly into agent prompts and tool calls. More than 100 enterprise customers are already using it, driving our MCP Server to a top position in the Databricks Marketplace through genuine production adoption rather than proof-of-concept theater.4

An agent asked "What does 'adjusted EBITDA' mean in this context?" no longer has to guess or hallucinate. It queries Collibra. Governance stops being a gate that slows teams down and becomes a trusted input that makes every agent decision more defensible.

AI governance and access governance sit at this layer in full force. The Collibra AI Command Center, launched in May 2026, provides a unified control plane for every agent, model and use case. Out-of-the-box templates aligned with AI UC-1, EU AI Act and NIST AI RMF mean organizations operationalize compliance rather than build it from scratch. Code-first AI registration via developer CLI means governance travels alongside engineers at the speed of development, rather than trailing behind as a compliance afterthought.5

In Collibra, data products carry embedded governance-by-design, so every product published is already validated for compliance and quality. Unstructured governance ensures PDFs, meeting notes and informal documents that quietly become load-bearing institutional knowledge are governed alongside structured assets, closing the gap most competitors leave entirely open.

Level 3: Embedded context: Governance as infrastructure

For the most mature organizations, the goal is to make governance invisible by making it structural, so every agent operates within certified boundaries by default rather than by exception. At this level, our certified context is woven directly into the ecosystems where agents operate, delivered as semantic views, governed Genie spaces, or synchronized semantic models, all without requiring agents to make additional external calls.

Our support for Open Semantic Interface (OSI) makes this possible. Governed glossaries and standardized metrics authored in Collibra synchronize to external platforms using emerging standards. When a Genie space in Databricks or a governed view in Snowflake surfaces data to an agent, the definitions, quality certifications and policy constraints are already embedded. The agent does not need to ask for context, because context is structurally present where it operates.

Lineage at this level becomes end-to-end AI traceability across Vertex AI, SageMaker and Databricks, captured automatically from source datasets through training, inference and deployment. When a regulatory inquiry arrives asking why an AI system made a recommendation six months ago, the answer is not a forensic excavation. It is a query. With the Collibra AI Command Center, continuous trust signals provide real-time automated control over every agent in production, rather than periodic review cycles that cannot keep pace with agentic velocity.

Why the competition is thinking too small

Most vendors are building one layer of the stack and calling it done.

Catalog-only vendors index metadata without governing it: They can tell you what assets exist, but not whether those assets are certified for an agent to act on.
Quality-only vendors monitor pipelines but have no concept of business semantics, so an agent that knows a dataset is fresh but not what it means is still operating with dangerously incomplete context.
Model registry vendors track versions but carry no lineage to training data and no access governance to control what models can touch.

The context problem is not a data problem, a quality problem, an access problem or a model problem. It is all four, simultaneously and continuously. Solving it requires a single AI control plane spanning catalog, lineage, quality, compliance, AI governance, access governance, data products and unstructured governance, one that delivers unified context to agents at every maturity level without requiring organizations to stitch it together themselves.

Collibra is the only vendor building that platform.

Context isn’t a project. It’s a practice.

Context cannot be front-loaded. The ontology that’s correct today will be wrong tomorrow, because the business it represents is alive. Every agentic AI system acting on stale or ungoverned context is accruing what Collibra CEO Felix Van de Maele calls the "hallucination tax": The hidden cost of oversight, rework and compounding risk that grows with every new agent in production.

The enterprises that will compound their advantage are not those chasing the best foundation model. They are those building the infrastructure to certify, govern and continuously refresh the context those models operate on. That institutional knowledge is exactly the token capital Nadella describes: The IP that cannot be replicated by a competitor who simply licenses the same model.

In a world of agentic AI, certified context is not a nice-to-have. It is the foundation everything else is built on.

References:

2 Why AI agents fail. They don't understand your business. Collibra Blog. July 2026.

3 Satya Nadella on the Future of the Firm. X / Microsoft, June 2026

4 Enabling Governed AI Everywhere with Collibra MCP Server. Collibra Blog