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title: Building the Contextual Data Layer for Enterprise AI
description: Enterprise AI agents often fail not because of model limitations but due to missing or incorrect context at inference time. The post explores building a...
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og:description: Enterprise AI agents often fail not because of model limitations but due to missing or incorrect context at inference time. The post explores building a...
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# Building the Contextual Data Layer for Enterprise AI

**[Agentic AI Context Builders](https://daily.dev/sources/agentic_ai_context_builders)** · [@markmilinkovich](https://daily.dev/markmilinkovich) · 1 min read · 1 upvotes · 0 comments

## Summary

Enterprise AI agents often fail not because of model limitations but due to missing or incorrect context at inference time. The post explores building a contextual data layer to ensure AI agents reason from accurate, complete information, featuring insights from Arango's CPO & CTO on how models fail plausibly rather than gracefully.

## Content

Your AI agents are answering questions. But are they answering them _correctly_?

At inference time, a model only reasons from what you give it. If context is missing, it fills the gap. If context is wrong, it follows it confidently. As Ravi Marwaha, CPO & CTO of Arango, puts it: _“Models don’t fail gracefully. They fail plausibly.”_

Read the full blog with video recording: 
[https://arango.ai/resources/building-the-contextual-data-layer-for-enterprise-ai/](https://arango.ai/resources/building-the-contextual-data-layer-for-enterprise-ai/)

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---

Tags: [#ai](https://daily.dev/tags/ai), [#llm](https://daily.dev/tags/llm), [#context-engineering](https://daily.dev/tags/context-engineering)

[View this post on daily.dev](https://daily.dev/posts/building-the-contextual-data-layer-for-enterprise-ai-ih8t0h8mv)

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