Agreement between AI agents can look reassuring while concealing shared assumptions, shared data problems or a common blind spot. How should a team evaluate the output before making it part of a decision workflow?

Using examples from building iPulse AI, this session separates inspectable reasoning from evidence of correctness. We consider how to preserve a dated forecast record, expose disagreement, compare outputs with subsequent observations and identify the limits of retrospective studies. We also discuss what a human reviewer needs to challenge the system and where evaluation remains incomplete.

The audience leaves with questions and a lightweight evaluation structure that can be adapted to research and other agentic workflows. This is a product-building case study about methodology and limitations.

Audience: AI builders, fintech teams, researchers and technology leaders. Presentation option: 25-30 minute case study. Level: intermediate. Focuses on evaluation methodology and implementation lessons.