Business context, definitions and trust for AI agents

A semantic layer resolves a business term to one approved meaning before an agent uses it, instead of leaving each agent to infer meaning independently from whatever it retrieved.

Placed against what most teams already have: A catalog holds the assets. A glossary holds the approved meanings. An index holds the text. The semantic layer is what connects a term appearing in the index to its approved meaning in the glossary, at the moment an agent needs it.

Without one, every agent, index and application resolves meaning independently, and each returns a different answer, consistent with whatever inputs it happened to work from.

A semantic layer has to be populated and actively kept current. Built once and never updated, it fails the same way an ungoverned glossary does: Technically present, quietly wrong.

For a team with a glossary but no semantic layer yet, the practical entry point is starting with the terms that already appear in the questions people are actually asking their agents, not attempting to model every term in the business at once. For the wider case on why agents specifically need this layer, see our post on the semantic layer for AI.

A governed term record needs specific fields for an agent to actually consume it correctly:

A business glossary, in short, holds the agreed business meaning of a term. This page covers what a record has to contain for an agent to use it correctly, not what a glossary is in general. For that foundational explanation, see our business glossary content.

A glossary holds business meaning, owned by the business. A data dictionary holds technical field descriptions, owned by data teams. They describe different layers of the same underlying asset, not competing versions of the same thing.

An agent reads from the semantic layer first, which in turn draws from the glossary. It doesn't read from a data dictionary directly, since a dictionary answers a technical question an agent isn't asking. For deeper coverage of the glossary and dictionary comparison specifically, see our guide to business context and semantic consistency.

Two people ask two agents the same question. They get two different numbers. Both agents can show their work.

That's the symptom, and it's a specific, common failure worth walking through directly. Consider two agents, both asked for a company's "active customer" count. One resolves the term against a definition maintained inside a CRM tool. The other resolves it against a definition embedded years ago in a reporting index and never updated. Both answers cite a source. Both are internally consistent. They don't match, because they're each traceable to a different definition of the same term.

Conflicts like this arise in predictable ways:

• Local definitions get created inside a specific tool, disconnected from any central glossary
• A term gets copied into an index and then edited independently of its original source
• Two domains define the same word for genuinely different purposes, and nobody ever reconciles the two

A conflict resolution workflow that handles this consistently:

1. Detect the duplicate. Usually surfaced by two near-identical terms appearing in a governance review.
2. Identify both owners. Find who's accountable for each version.
3. Decide whether the terms are genuinely different or accidentally divergent. This is the judgment call that determines everything that follows.
4. Retire or scope one. If accidentally divergent, deprecate one and redirect. If genuinely different, scope both explicitly by domain.
5. Republish to every consuming system. A resolution that doesn't reach every index and agent hasn't actually resolved anything.

Some conflicts resolve to two correct answers rather than one winner. When two domains genuinely need different definitions for legitimate reasons, the fix is scoping each term clearly by domain, not forcing an artificial merge that satisfies neither.

An owner is the person who decides what a term means when two parts of the business disagree. Without that named authority, a disagreement has nowhere to go.

Three roles matter here, and one person often holds two of them:

• The owner decides the meaning.
• The steward maintains the record and its mappings day to day.
• The approver signs off on changes before they reach consuming systems.

When an owner leaves or a domain reorganizes, this is exactly how terms become orphaned: Technically still approved, with nobody actually accountable for keeping them current. For the mechanics of the approval workflow itself, see our guide to access controls and policy enforcement for AI agents.

The change path, as a sequence: A definition changes. The change gets approved. The record gets republished. Consuming indexes get rebuilt. Agents start answering from the new version. Each step takes real time, and the gap between "change approved" and "every agent answering correctly" is exactly where stale answers happen.

Freshness deserves specific attention here. Every index that embedded a term holds its own copy, and each one continues answering from the old meaning until it's actually rebuilt. Approving a change doesn't propagate it. Rebuilding does.

A readiness checklist for agent-ready definitions:

• The term has a named owner
• The definition is approved with a date
• The term is mapped to the data assets it governs
• Conflicting or related terms are recorded
• Every consuming system is known
• A change triggers a rebuild, not just a ticket

This section covers lineage and provenance only as signals attached to an agent's answer. Both are covered as full disciplines in our cross-platform AI governance content, which owns the deeper explanation.

A trust signal table, mapping a question to the signal that answers it:

A user should be able to see these signals alongside an agent's answer, not just the answer itself. For each item to actually appear, the upstream governance work has to already exist: The definition has to be approved, the mapping has to be current and the provenance has to have been captured somewhere durable.

When a signal is missing, and most teams are retrofitting rather than starting from a clean state, the honest approach is showing the gap directly in the answer rather than hiding it, and treating an answer with an unverifiable signal as lower confidence than one with a complete trail.

Business context depends on governed definitions actually reaching the systems that need them, not sitting documented separately from the agents consuming them.

Collibra Platform, built on eighteen years of ontology engineering, connects governed definitions, ownership and approval workflows, the mapping between terms and the data behind them and the trust signals that travel into an agent's answer. In Collibra, ontologies power intelligent discovery, lineage and AI-ready governance across the enterprise, which is what turns business context from a glossary entry into governed context an AI system can actually rely on.

Give your business definitions a governed home with Collibra Data Catalog.

Learn more about Collibra Platform, the Enterprise AI Control Plane. Context and control for every agent you put in production.