Your AI Agents Are Only As Good As Your Data

The text discusses the challenges and solutions involved in effectively using agents to answer complex queries across multiple data types and sources. It highlights the traditional difficulties of relying on raw API calls, which often lead to cumbersome and error-prone processes, and contrasts this with the advantages of structured data infrastructure. This infrastructure includes a domain ontology, a relationship graph, and a query engine, which collectively enhance agent capabilities by providing pre-validated, structured data that reduces errors and improves query performance. The text argues that by investing in robust data infrastructure, organizations can significantly enhance agent quality across dimensions such as correctness, groundedness, safety, trajectory, and performance. The dispatch table model is proposed as a more efficient approach than endpoint-per-tool designs, enabling agents to navigate complex queries with fewer errors and more efficiency by using a limited set of generic verbs. Ultimately, the text suggests that a well-modeled data infrastructure not only supports better agent performance but also allows for continuous improvement and scalability without requiring changes to the agent's reasoning layer.

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