CluedIn is introducing a significant set of new capabilities across AI Agents, data enrichment, the Global Data Model and Topology Explorer.
Each enhancement solves a specific product challenge. Together, they support a bigger change in how CluedIn helps enterprises manage data, moving from AI that waits for individual instructions towards AI that can work proactively towards a defined outcome.
From helping people manage data to actively helping manage the data itself.
This does not mean removing people, policies or governance from the process. It means reducing the need for a person to identify, initiate and supervise every individual step.
The redesigned CluedIn AI Agent experience will support proactive Agents and long-running work that may continue for minutes or hours.
Instead of asking an Agent to complete one isolated task, users will increasingly be able to give it a broader objective, such as:
The Agent can then work through multiple steps, use the context available to it, take permitted actions and bring a person back into the process when judgement or approval is required.
Agents will also be able to track the work they perform and the results of their actions. Over time, those outcomes can provide useful context for approaching similar work. Support for Model Context Protocol, or MCP, will also allow CluedIn Agents to interact with external tools and services as part of a wider enterprise AI ecosystem.
An AI model can make a change and produce a convincing explanation. That is not proof that the data improved.
CluedIn will combine AI-driven action with independent, deterministic data quality measurement. This makes it possible to assess whether completeness increased, validity improved, duplicate rates fell or the quality of a domain moved in the right direction.
Identify → Reason → Act → Measure → Improve
This creates an objective scorecard for Agent actions. The Agent can reason and act, but CluedIn can independently determine whether the intended result was achieved.
Enterprise enrichment is rarely a simple exchange of one internal record for one external record. Company names vary. Addresses change. Legal entities multiply. Enrichment providers may return historical records, branches, subsidiaries or several plausible matches.
CluedIn is improving enrichment with AI-assisted fuzzy matching, helping identify likely external records even when names, addresses, abbreviations, formatting or identifiers do not align perfectly.
New multi-record matching capabilities will also help CluedIn work with multiple related responses rather than assuming the first result is the right one. The aim is higher enrichment coverage and more accurate matching, while handling the ambiguity that appears in real enterprise data.
The hard part of enrichment is not finding more data. It is knowing which data belongs to whom.
The CluedIn Global Data Model is also evolving with new search, filtering, semantic model capabilities and synchronisation with Microsoft Fabric IQ.
Search and filtering will make larger enterprise models easier to navigate. The bigger strategic change is the growing role of the model as a semantic representation of the business.
AI needs more than access to tables, columns and values. It needs to understand what an entity represents, how it relates to other entities and what those relationships mean. Customers, products, suppliers, assets, locations and legal entities do not exist as isolated records. Together, they form a map of the enterprise.
The Global Data Model can provide this context to CluedIn AI capabilities and, through Fabric IQ synchronisation, help make governed enterprise context available within Microsoft’s wider data and AI ecosystem.
Enterprise data estates never stand still. Sources, mappings, schemas, relationships and properties change continuously. Finding the change that caused a later issue can mean manually comparing complex technical representations across months of history.
The new CluedIn Topology Explorer is designed to work across thousands of historical versions, with improved ways to explore how the topology and model have evolved.
AI-generated explanations will help users investigate questions such as:
For data architects, engineers, governance teams, administrators and auditors, this changes the question from “show me the topology” to “help me understand what happened”.
These capabilities are connected. The Global Data Model provides semantic context. Data quality and topology help CluedIn observe the state of the data estate. AI Agents reason about what needs to happen. Agents and enrichment capabilities perform governed work. Deterministic metrics measure the result. Previous outcomes can then become context for future work.
The Agentic Data Management loop
Understand → Observe → Reason → Act → Measure → Learn
People remain in control of the policies, permissions, approval points and acceptable level of autonomy. The aim is not blind automation. It is useful, explainable and measurable action within clear enterprise boundaries.
This is the direction CluedIn is building towards: a platform that can increasingly understand the state of enterprise data, identify what needs attention, take governed action and prove whether that action improved the result.
From AI assistance to AI action.
See how CluedIn combines AI Agents, graph-native context, data quality and governance to help enterprises build trusted, AI-ready data.