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 that 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
Before you build anything, ask yourself: Why are you doing this? Clearly define the problem statement, identify your users, and ensure you have access to the necessary data. Start small—you can always scale later.

Step 2: Identify and Scope Data
Select the key datasets needed to achieve the goal you defined in Step 1. You do not need to centralize all your data, but avoid sourcing it from too many locations to maintain efficiency.

If you’re incorporating a Large Language Model (LLM), consider using unstructured data sources such as documents, PDFs, chat logs, or web content.

Step 3: Make Your Data AI-Ready
To build a reliable AI agent, you need a semantic foundation that extracts and aligns metadata across different data types:

• Unstructured data (documents, PDFs, chat logs) requires tagging, enrichment, and meaningful segmentation based on semantics.
• Structured data (databases, APIs, metadata repositories) needs a well-defined data catalog, ensuring terms are standardized and aligned across sources.

At a minimum, AI agents require consistent terminology across datasets to be effectively queried. But this is just the start—advanced approaches like building a knowledge graph, linking related concepts, and enforcing governance policies can further enhance retrieval, reasoning, and AI accuracy.

Step 4: Orchestrate and Test
With your data prepared, it’s time to start building! Begin with a single AI agent and expand from there. Continuously test and refine by adjusting the agentic structure, the taxonomies used to tag content in Step 3, the relationships between entities in your knowledge graph, and the prompts guiding the LLM. Iteration is key—optimizing these components will improve accuracy, reasoning, and adaptability over time.

Step 5: Expand
You now have a foundation to expand your AI agents and their capabilities. You can allow others to access your data with different instructions to build additional agents. You can also incorporate more data to enhance and refine existing agents, or use new data to build entirely new ones. Over time, this approach enables you to map your entire enterprise, transforming any concept that can be communicated in human language into a functional AI agent.

TopQuadrant’s Role in Your AI Journey

TopQuadrant provides the semantic foundation that makes AI agents scalable, reliable, and governable. Our platform helps organizations structure, connect, and manage data so AI systems can deliver accurate and context-aware results.

• Step 3: Make Your Data AI-Ready – TopQuadrant enables metadata alignment, semantic tagging, and ontology management, ensuring AI agents can interpret and use data effectively.
• Step 4: Orchestrate and Test – Our platform provides knowledge graph-based governance, allowing organizations to structure relationships, refine taxonomies, and optimize AI workflows.
• Step 5: Expand – With TopQuadrant’s knowledge graph capabilities, organizations can scale AI agents, integrate new data sources, and manage evolving business rules—ensuring AI systems grow and adapt over time.

By embedding semantics into AI architecture, TopQuadrant ensures AI agents are accurate, transparent, and aligned with enterprise goals.