Why Your AI Agents Aren’t Accurate — And How Ontology Fixes It
12
DAYS
11
HOURS
44
MINUTES
10
SECONDS

Speaker(s):

Kumar Kadam
Technical Architect at iLink Digital

Sneha Rajashekhar
Senior Software Engineer at iLink Digital
Event Overview
Enterprise AI agents are only as effective as the knowledge they are grounded in. While vector search and Retrieval-Augmented Generation (RAG) can improve information access, they often fall short without a semantic layer that reflects how your business actually operates. The result? AI agents that hallucinate, miss business context, and struggle to scale beyond pilot initiatives.
Join iLink Digital for a practical, demo-driven session on building an Enterprise Knowledge Layer using iWeave, our ontology platform, alongside Microsoft Fabric IQ.
In this session, we’ll demonstrate how to design a domain ontology, operationalize it within Microsoft Fabric, and connect it to Foundry IQ and Work IQ experiences — enabling AI agents to reason over your enterprise knowledge, not just your documents.
You’ll see how ontology-driven architectures help enterprises move from disconnected data ecosystems to grounded, explainable, and context-aware AI agents that deliver measurable business outcomes.
Whether you’re evaluating your first AI agent initiative or looking to scale beyond proof-of-concept deployments, this session will provide a practical reference architecture and actionable next steps for enterprise adoption.
Description
See how iWeave and Microsoft Fabric IQ create a semantic knowledge foundation that enables enterprise AI agents to reason with business context. Includes live demos, reference architecture, and Q&A with iLink’s Data & AI team.
Key Takeaways
- Why ontologies are the missing layer between enterprise data and AI agents
- How iWeave operationalizes domain knowledge on Microsoft Fabric
- The reference architecture connecting Fabric IQ, Foundry IQ, and Work IQ
- A live demo: from raw Fabric data to a grounded, ontology-aware AI agent
- How to approach your first 90 days: what to model first and what to defer
Register Now


