Confidently Wrong, and Expensive: What AI Agents Cost You When They Don’t Understand Your Business
When AI agents fall short, it’s rarely the model. It’s that they don’t know your company. An agent reads one fragment, treats it as the answer, and responds with confidence. When it can’t resolve something, it keeps exploring, which means slower responses, worse answers and higher costs. Meanwhile every other agent in the company is doing the same thing separately, paying to rediscover what the business already knows.
Getting past it takes three views held at once. Semantic, knowing what things are and what your company means by them. Structural, knowing how they relate, who owns them, and what depends on what. Intent, knowing why it was built that way and what was already ruled out. Most tools give you one. Almost none give you all three, and maintaining them is its own problem.
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What we’ll cover
What context failures actually cost, in tokens, time, and output quality
The three views an agent needs, and how to tell which one you're missing
What it takes to make an agent context-aware, including the reasoning behind the record
Speakers

Nave Ben Naim
Founding Engineer at Uvi
Founding engineer at Uvi, where he's spent the last two years on the hard parts of enterprise context: entity resolution, permissions, and keeping it all current.