6 Data Requirements to Take AI Agents from Demo to Production

Why do AI agents work in demos but struggle when deployed into production? 🤖 AI agents are moving beyond simple chatbots to take actions and make decisions across enterprise systems. But production environments introduce challenges that controlled demos often overlook, including changing data, different user permissions, inconsistent responses, latency, and rising costs. In this webinar hosted by The New Stack, Kim Fessel joins Ravi Marwaha, COO and Chief Product & Technology Officer at Arango, to explore six data requirements for providing AI agents with reliable business context. 🔍 What you'll learn: - Why AI agent demos struggle when scaled to real-world production environments - The six capabilities of a contextual data platform, including semantic clarity, relationships, temporal awareness, governance, agent-ready context, and persistence - Why vector similarity alone does not provide complete business context - How to connect structured and unstructured data while preserving relationships and data provenance - How contextual data platforms can complement existing data warehouses, data lakes, and enterprise infrastructure - Why real-time updates, auditability, and shared context matter for autonomous AI agents - Real-world examples involving customer support and clinical trial site selection - How to approach AI agent deployment in regulated industries ⏱️ Chapters: 00:00 Introduction: Six Data Requirements for AI Agents 01:25 Why AI Agent Demos Struggle in Production 04:25 Warning Signs Before Production Deployment 07:20 Why Enterprise Data Challenges Matter for AI Agents 10:47 Why Traditional Data Architectures Fall Short 13:59 Defining Business Context for AI Agents 17:33 The Six Capabilities of a Contextual Data Platform 19:51 Integrating Context with Existing Infrastructure 24:04 The Biggest Blind Spot: Why Vector Search Isn't Enough 27:45 Q&A: One Database vs. Multiple Databases 31:10 Context Governance and Auditability 33:29 Designing a Production-Ready Data Architecture 37:30 Managing Data Freshness and Real-Time Updates 41:30 Real-World Customer Use Cases and Results 48:00 Why Multiple AI Agents Need Shared Context 52:48 Defining Context for Your Organization 56:00 AI Agents in Healthcare and Regulated Industries 58:27 Building Long-Term Knowledge and Agent Memory 1:02:20 Arango Resources and Closing Remarks 💡 About Arango The Arango Contextual Data Platform helps organizations provide AI agents with relevant, connected, and governed business context. It supports semantic understanding, relationships, temporal awareness, and traceability, helping enterprises build a reliable data foundation for agentic applications. Rather than replacing existing enterprise infrastructure or building AI agents themselves, Arango focuses on providing the contextual data layer those agents need. 🌐 Learn more about Arango: https://arango.ai/ #Arango #EnterpriseAI #AIAgents #AgenticAI #BusinessContext