AI Conversations Can Appear Flawless Yet Be Faulty
Evaluation methods for AI agents discussed at VB Transform 2026.
1The Flawless Appearance of AI Agents
AI agents may appear flawless on their own, but this can indicate a faulty product. This situation is driving companies to change their evaluation methods.
2Shift in Evaluation Methods
Companies are moving from scoring individual traces to comparing cohorts of users against a baseline. This method provides more accurate results.
3Automated vs Human Evaluation
Automated evaluation is scalable but ungrounded. Human evaluation is not scalable but more reliable. This dilemma is a challenge the industry faces.
4Contrastive Analysis
Contrastive analysis detects problems by comparing user cohorts against a baseline. This method reveals issues not visible in a single trace.
5Model Selection and Cost
Starting with the most capable model and moving to smaller ones reduces costs. This approach provides a more efficient evaluation process.
6The Role of Humans
Using LLMs as judges does not eliminate the role of humans. Humans remain a safety net in corner cases and play a crucial role in building trust in systems.
Original article: VentureBeat — AI Konuşmaları Kusursuz Görünse de Hatalı Olabilir