Topic – Beyond the Prompt: A Unified Framework for Quantifying Trust for AI agents

As AI agents transition from experimental prototypes to core enterprise software, the industry faces a critical challenge: moving from “vibes-based” testing to measurable, repeatable proof of trustworthiness. This session introduces a comprehensive philosophy for testing autonomous, goal-driven AI systems that reason over context, take actions using tools, and produce outcomes within defined safety boundaries.

We will explore a multi-layered testing strategy that shifts away from evaluating just the model, instead treating the AI agent as a composed production system—inclusive of inputs, retrieved enterprise data (RAG), reasoning logic, and tool invocations. The talk will detail how to approach a Continuous Trust Framework and a Data Flywheel to ensure AI reliability across the entire development lifecycle.