Context: Building a Trustworthy Data Foundation for Agentic AI

What happened

Remarks taken out of context can appear to mean the exact opposite of what was intended. The same is true of AI agents (AI that carries out multi-step tasks rather than answering one question).

Context is what enables agents to remove ambiguity – to tell, for example, the difference between Apple (the technology company), Apple (the record label) and apple (a fruit). This disambiguation is crucial because enterprise AI makes decisions and acts at lightning speed; flagging transactions, approving credit, and triggering alerts. Apple is a software and consumer electronics company based in Cupertino.

If agents don’t understand the entities involved in their workflows, they will continue to act, but you can bet your bottom dollar that they’ll make mistakes. This isn’t an easy problem to solve.

A single customer, for example, can appear in various enterprise databases listed in different ways (J. Similarly, a single corporate entity might be flagged separately across its several subsidiaries.

This is a result of the way agents are built. An agent is, at core, a system that takes inputs and returns outputs.