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.”
This session addresses one of the most overlooked challenges in enterprise AI — not model quality, not prompt engineering, not data volume. Context. What your AI actually sees when it needs to reason, decide, and act.
We’ll cover:
- What is a context layer and what AI agents, assistants, and applications actually need
- Why enterprise AI stalls between pilot and production
- The data architecture requirements for AI that must reason and act in real time
- How a Contextual Data Layer bridges grounds your LLMs with domain specific enterprise data for trusted responses
- Design patterns for managing business context once and reusing it everywhere — across agents, and AI-powered applications
This is a candid, technical discussion for builders, engineers, data architects, and AI practitioners. Sharing knowledge and experience to build solutions for production.
Speakers
Ravi Marwaha
Chief Operating Officer & Chief Technology Product Officer
Arango
Mark Milinkovich
Director of Product Marketing
Arango
