Case study · Cal Hacks
BenchMind
An agent-benchmarking platform built during Cal Hacks to make AI-agent behavior easier to compare and reason about.
- Discipline
- AI agents · Evaluation
- Technology
- AI Agents · Benchmarking · Evaluation · Hackathon
Overview
BenchMind grew from my interest in moving beyond impressive agent demos toward repeatable evaluation: defining tasks, observing behavior, and comparing results.
System map
How the pieces connect
The challenge
What made the problem worth solving
Agent systems can look capable in a single demonstration while behaving inconsistently across tasks. A useful evaluation platform needs to make those differences visible.
The approach
Turning the problem into an engineering plan
- 01
Framed agent quality as a repeatable benchmarking problem rather than a one-off demo.
- 02
Organized evaluation around tasks and observable outcomes that could be compared.
- 03
Used the hackathon format to rapidly test the platform concept and interaction model.
Result
BenchMind strengthened my interest in evaluation infrastructure: understanding not only what an AI system can do, but how reliably it does it.