Agentic AI Verification: A Business Imperative

Agentic AI systems are doing more and more work, but humans need to figure out how to verify it all. This is a fundamental challenge for business leaders. The top priority is accountability. This means being able to trace all the steps an AI system took for a task. Edwin Olson of May Mobility emphasizes building systems that are as right as possible, but also transparent, so mistakes can be understood and fixed. Caitlin Halferty of Thomson Reuters also stresses validating AI output. Thomson Reuters has four pillars for its "fiduciary grade" AI products: transparency, data privacy and security, subject matter experts, and reliable content. Another technique is designing systems that can regulate each other. Elena Kvochko of Trustguard AI calls this the "LLM as a judge" technique. She explains it like a newsroom, where one AI writes and another edits to find mistakes. The key is that separate AI systems must handle the verification. You don’t want AI to grade its own work. This smart structure for AI verification is critical as AI takes on more tasks than humans can possibly verify.