Harness the power of knowledge graphs to manage the metadata driving your AI architecture.
Why AI Agents Need a Semantic Foundation
Most AI agents today are just proof-of-concepts—they demo well but fall apart when deployed at scale. To build an AI agent that lasts, you need a semantic foundation. A knowledge graph helps manage the metadata driving your AI architecture, allowing you to:
- Improve reliability: Get more accurate results
- Enhance governable: Govern the data flowing to your AI pipelines
- Enable scalability: Ensure the tools you build don’t become obsolete within a year
How to Build Scalable AI Agents (the Right Way)
Step 1: Build your business case
Clearly define the problem statement, identify your users, and ensure you have access to the necessary data.
Step 2: Identify and Scope Data
Select the key datasets needed to achieve your goal.
Step 3: Make Your Data AI-Ready
AI agents require consistent terminology across datasets to be effectively queried.
Step 4: Orchestrate and Test
With your data ready, start with one AI agent and iteratively refine to boost accuracy and adaptability.
Step 5: Expand
With a solid foundation in place, you can scale AI agents by adding data, enabling new use cases, and transforming enterprise knowledge into functional, language-driven agents.
TopQuadrant’s Role in Your AI Journey
TopQuadrant makes data AI-ready, governs it with knowledge graphs, and supports scalable AI agents—ensuring accurate, context-aware results that evolve with your enterprise.
