Overview
This project focuses on evaluation infrastructure for AI agents, especially workflows that use language models to judge the quality of another system’s behavior. The goal is to turn evaluation from a manual bottleneck into something that is measurable, repeatable, and useful in product development.
The work spans both methodology and implementation, from defining evaluation criteria to building the tooling needed to run those evaluations consistently.
Key Features
LLM as a Judge
Implements automated evaluation workflows where LLMs assess the output quality of other systems.
Standardization
Builds repeatable evaluation methodology for comparing agent behavior across tasks.
Multi-Domain Coverage
Targets evaluation patterns that can transfer across different product and research contexts.
Implementation
Turns the methodology into working infrastructure that can be run in real development workflows.
Technologies Used
Challenges Overcome
- Ensuring the reliability and consistency of LLM-based judgments.
- Defining metrics that remain useful across diverse AI agent tasks.
- Reducing bias in automated evaluation pipelines.
Outcomes & Impact
- Established a practical framework for AI agent assessment.
- Improved iteration speed in AI development workflows through automation.
- Contributed to standardizing evaluation practices for agent systems.