Enterprise AI Moving From Chat to Agents

The shift from chat to agents means enterprise AI is being bought less as a better answer box and more as workflow software that completes expensive jobs. In practice, that means moving from asking a bot to summarize documents to configuring a system that pulls from SharePoint, CRMs, filings, and data rooms, breaks the task into steps, reasons across full documents, and produces a memo, diligence matrix, or contract analysis that can be checked and reused.

• Chat works well for one off questions, but firms in finance and law need repeatable outputs with audit trails, permissions, and human review. Hebbia frames its agents as coordinators that run retrieval, reasoning, calculations, and synthesis across many documents, with Matrix as the tabular control layer behind the scenes.
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• This is also how vertical vendors defend against foundation models. If OpenAI or Microsoft improve raw retrieval and summarization, the harder problem still sits in orchestration, template design, integrations, and adapting to how a diligence team, banker, or lawyer actually works day to day.
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• The market map is splitting accordingly. Glean grew by owning broad internal search and chat across the whole company, with contracts ranging from about $30K per year upward. Hebbia and Harvey charge far more per power user because they are selling completion of high stakes workflows plus hands on implementation and change management.
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The next phase of enterprise AI is likely to look more like teams supervising fleets of narrow, domain trained workflows than employees chatting with a general assistant all day. The companies that win will be the ones that turn messy document heavy work into reliable, low click systems that experts trust enough to run every day.