Evaluating ChainBuddy: Performance, Usability, and Design Insight
Explore the evaluation of ChainBuddy, focusing on how this AI agent enhances workflow quality, reduces cognitive load, and supports user confidence. Learn from user studies and expert insights about designing collaborative and trustworthy AI assistants, including key challenges like perception bias and overreliance.
In our last lesson, we deconstructed the impressive multi-agent system that acts as ChainBuddy’s “factory,” taking a set of requirements and methodically building a complete workflow. We saw the “architect” (the planner agent) and the “specialist crews” (the worker agents) in action. But for any agentic system that we design, the most important question remains: Does it actually work? More than that, does it provide real value to the user?
In this final lesson of our case study, we will answer that question by looking into ChainBuddy’s evaluation. We’ll explore not just the performance results, but what those results teach us about designing effective and trustworthy AI assistants.
The evaluation framework
To get a clear, comparative result, the researchers designed a within-subjects user study. This is a classic experimental design where each participant acts as their own control. Each of the 12 participants completed tasks under two different conditions.
The control condition: Using the baseline ChainForge interface without any help from the agent.
The assistant condition: Using the same interface but with access to the ChainBuddy agent.
This setup allows us to directly compare how the presence of an agentic assistant changes a user’s behavior, performance, and perception.
How performance was measured
A good evaluation looks at a problem from multiple angles. The researchers used a mix of quantitative and qualitative metrics to get a complete picture.
Cognitive load: Participants completed the
NASA TLX survey NASA_TLX_survey