“The success of AI agents requires a foundational platform to handle data integration, effective process automation, and unstructured data management,” says Rich Waldron, co-founder and CEO of Tray.ai. “AI agents can be architected to align with strict data policies and security protocols, which makes them effective for IT teams to drive productivity gains while ensuring compliance.”
Mike Connell, COO of Enthought, says you need a high volume of clean and (for some applications) labeled data that accurately represents the problem domain to train and validate models. Connell says, “A robust data pipeline is essential for preprocessing, transforming, and ensuring the availability of real-time data streams to refine the model and keep it calibrated to a changing world. Additionally, you should consider the need for domain-specific ontologies or embeddings to enhance the agent’s contextual understanding and decision-making capabilities.”
Regarding security and compliance, Joseph Regensburger, VP of research at Immuta, says AI agents have identities, so access to complex AI chains and knowledge graphs requires controls as if they were human. Regensburger recommends, “Capture the frequent changes in regulations and business agreements in an access control solution and enforce them on all potential human and machine actors.” Keeping up with changing business rules is essential to ensure AI agents are not developed based on outdated usage agreements.