Overview:

Autonomous AI agents are reshaping how enterprises think about automation and decision-making. But the reality is harsh: more than 95% of generative AI pilots miss the mark on measurable ROI. And it’s almost never the model’s fault. It’s the data architecture behind it.

Most organizations don’t have the secure, scalable foundation required to safely connect agents to the full breadth of their knowledge base. Agents need real-time, identity-aware access to unstructured documents, structured systems, and live operational data without blowing up your security posture or your cloud bill.

This webinar gives you the architectural playbook to move from pilot experiments to production systems. We’ll go well beyond basic retrieval and dig into Agentic RAG - a modern approach that combines reasoning, planning, memory, and tool use (via MCP) with enterprise-grade data access. You’ll walk away with a concrete understanding of how to build a secure Agentic Data Fabric that manages context, enforces zero-trust boundaries, and maintains full auditability so you can deliver governable, mission-critical data to multi-agent systems.

Technical Details:

In this session, we’ll walk through:

• Why context engineering is the safest, most scalable way to give agents enterprise knowledge.
• Common RAG data access patterns
• How MCP enables agents to safely call APIs, query databases, and interact with live systems.
• A reference architecture built for secure, governed, scalable multi-agent systems.

This session includes a practical walkthrough and live Q&A. Can’t make it live? Register anyway and we’ll send you the recording.