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23/06/2026 7:00 pm
TL;DR: Governed context improved agent accuracy from 62% to 92% in a KU Leuven test, while ungoverned agents answered confidently wrong on definition-dependent questions and burned more compute, according to Collibra. The deeper issue is assumption collapse: access control and data governance models built for fixed truth sources fail when agents reason across fragmented ontologies at runtime.
NHIMG editorial — based on content published by Collibra: Data has gravity. Context does too
By the numbers:
- Without it, 62%.
- Only 13% of organisations feel extremely prepared for the reality of agentic AI despite the majority racing toward autonomous adoption.
- 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job.
Questions worth separating out
Q: How should security teams govern AI agents that reason across multiple data platforms?
A: Security teams should govern the meaning layer, not just the access layer.
Q: Why do fragmented ontologies create risk for AI agents?
A: Fragmented ontologies give agents multiple, incompatible definitions of the same business term.
Q: What breaks when AI agents rely on platform-specific catalogs for context?
A: What breaks is enterprise consistency.
Practitioner guidance
- Map high-value business terms across platforms Identify where customer, revenue, active user, and similar terms diverge across CRM, warehouse, SaaS, and legacy systems.
- Test agent output with and without governed context Run the same questions through your preferred model with governed definitions, lineage, and quality signals enabled and disabled.
- Separate platform metadata from enterprise meaning Do not assume a cloud catalog automatically gives you a neutral enterprise ontology.
What's in the full article
Collibra's full blog post covers the operational detail this post intentionally leaves for the source:
- The KU Leuven test design, including how the anonymised data was structured and how the Claude agent was queried.
- The specific examples of incorrect reasoning, including the active tester case and the definition retrieval path that corrected it.
- How Collibra positions ontology, lineage, and quality signals inside its enterprise AI control plane.
- The practical argument for a neutral context layer across Databricks, Snowflake, BigQuery, and on-prem systems.
👉 Read Collibra's analysis of context gravity and AI agent governance →
AI agent context layers: what happens when ontology fragments?
Explore further
25/06/2026 2:45 am
AI agent governance fails when enterprise meaning is fragmented across platforms. The article shows that each catalog can be internally consistent while still disagreeing with the rest of the organisation. That means the governance problem is not isolated bad data, but contradictory definitions that agents will combine into one answer. For practitioners, this elevates context consistency to a first-class control objective across IAM, data governance, and agent oversight.
A few things that frame the scale:
- Only 13% of organisations feel extremely prepared for the reality of agentic AI despite the majority racing toward autonomous adoption, according to The 2026 Infrastructure Identity Survey.
- 69% of security leaders agree identity management must fundamentally shift to address agentic AI systems, according to The 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: Who should own governed context in an AI operating model?
A: Governed context should sit with the teams responsible for enterprise data definitions, policy, and access governance, not only with the platform running the model. The control has to travel with the agent across systems, because the risk appears when reasoning crosses boundaries, not when the model is trained.
👉 Read our full editorial: Context gravity and AI agent governance across fragmented data estates
