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Stephanie Baladi

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Stephanie Baladi sur AI evaluation criteria, context layer efficacy, enterprise AI architecture. Explorez 3 points de vue par thème, avec des éléments tirés de 1 source.

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enterprise AI architecture

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enterprise AI architecture

Enterprise context layer requirements

AI agents require an enterprise context layer—not just enterprise search—that retrieves authoritative information from connected systems, understands cross-tool and cross-team relationships, enforces source permissions at every retrieval, and delivers precise evidence at each step of a workflow.

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Stephanie Baladi·

From enterprise search to enterprise context: what AI agents actually need

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AI agents need more than just enterprise search to do reliable work. They need an enterprise context layer that retrieves the most authoritative information from connected systems, understands how that information relates across tools and teams, enforces source permissions every time, and delivers the right evidence at each step of a workflow.
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Stephanie Baladi
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AI evaluation criteria

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AI evaluation criteria

Context quality over model or connector metrics

When evaluating enterprise AI platforms, teams should prioritize context quality—whether the system grounds every step of every workflow in current, relevant, permissions-aware knowledge—over traditional metrics like model quality, context window size, or connector count.

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Stephanie Baladi·

From enterprise search to enterprise context: what AI agents actually need

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That context layer is the piece most enterprise AI evaluations still overlook. Teams compare model quality, context window sizes, and connector counts. All of these are important, but once AI moves from answering one-off questions to helping with research, decisions, drafting, and execution, the harder problem is context quality — whether the system can ground every step of every workflow in current, relevant, permissions-aware enterprise knowledge.
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context layer efficacy

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context layer efficacy

Measurable reliability advantage of strong context

In Glean’s evaluation, human graders selected answers grounded in its context layer as correct 1.9× more often than those built on ChatGPT’s company knowledge for complex enterprise queries—demonstrating that strong context improves answer reliability, reduces noise, and enables trustworthy agentic workflows.

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Stephanie Baladi·

From enterprise search to enterprise context: what AI agents actually need

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The difference is measurable: in Glean’s evaluation write-up, Not all enterprise context is created equal , human graders who expressed a preference chose answers grounded in Glean’s context layer as correct 1.9× as often as those built on ChatGPT’s company knowledge for complex enterprise queries. Weak context makes agents work harder and trust less; strong context lets them retrieve precisely, pass less noise, and produce answers people can rely on.
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Stephanie Baladi
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