AI agents need more than access to data. To produce reliable and contextually relevant results, they need the business meaning, current operational conditions, and provenance surrounding that data.
A context layer can help AI agents understand what enterprise data means, whether it reflects current operational conditions, and where it originated.
In this complimentary report, you will learn:
- The role of semantics, operational state, and provenance as the three core components of a context layer for AI agents
- An approach for retrieving, organizing, and selecting the context supplied to AI agents
- How the operational state can help ground AI agents in current business conditions
- How a robust context layer can support more reliable, relevant, and cost-efficient AI decision-making
Denodo operationalizes the data foundation that agentic AI requires, unifying distributed data with real-time access, semantic consistency, and governance enforced at runtime.
Gartner Strategic Planning Assumption: By 2027, organizations that prioritize semantics in AI-ready data will increase their agentic AI accuracy by up to 80% and reduce costs by up to 60%.
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