How to Automate Your Intellectual Toil with Agent-Driven Development

Introduction

As an AI researcher, you often find yourself drowning in repetitive intellectual labor: analyzing hundreds of thousands of lines of JSON trajectories from coding agent benchmarks. The task is necessary but tedious. By harnessing GitHub Copilot and agent-driven development, you can automate this process, freeing your mind for higher-level creativity. This guide will walk you through building your own evaluation agents, inspired by the success of the eval-agents project. You'll learn to identify repetitive analysis loops, use Copilot for pattern surfacing, and package your automation into sharable, extensible agents.

How to Automate Your Intellectual Toil with Agent-Driven Development — Source: github.blog

What You Need

GitHub Copilot (enabled in your IDE)
Access to benchmark datasets (e.g., TerminalBench2, SWEBench-Pro) containing trajectory .json files
Python (3.8+) installed locally
Basic knowledge of JSON parsing and scripting
A code repository (GitHub) to version control your agents
Collaborators or a team (optional but recommended for sharing)

Step-by-Step Guide

Step 1: Identify Your Repetitive Analysis Loop

Start by examining your daily workflow. Which tasks consume hours of manual effort? For example, evaluating coding agent performance requires combing through trajectories—each a JSON file with hundreds of lines showing thought processes and actions. If you find yourself repeating the same pattern—such as using Copilot to surface trends and then investigating anomalies—you've found your automation opportunity. Write down the exact steps: pattern detection, filtering, manual review. This loop is the foundation for your agent.

Step 2: Use Copilot to Explore Trajectories

Open a few trajectory files in your IDE with Copilot active. Ask Copilot to summarize patterns: for instance, prompt it with “Find all cases where the agent used a specific tool incorrectly”. Copilot will generate scripts that filter and highlight relevant data. Iterate, refining your prompts until you get usable results. This step validates the feasibility of automation and gives you a prototype script. Save these scripts as the core of your future agent.

Step 3: Codify Pattern Detection into an Agent

Now, package your Copilot-generated scripts into a reusable agent. In a new Python file, define functions that:

Accept a trajectory path or directory
Parse JSON and extract key events (e.g., thought steps, actions, errors)
Apply detection rules (e.g., counting tool usage, flagging long response times)
Output a summary report

Use eval-agents as inspiration—your agent should be a standalone module with clear inputs and outputs. Add configuration options so others can adjust thresholds without editing code.

Step 4: Make Your Agent Easy to Share and Use

Publish your agent on GitHub with a README that explains how to install dependencies (pip install -r requirements.txt) and run it. Use GitHub Actions to test agent runs on sample data. Ensure your code is well-commented and modular. Consider adding a CLI interface so teammates can run it without opening the IDE. This step mirrors the project’s goals of making agents easy to share.

Step 5: Collaborate and Iterate with Your Team

Encourage colleagues to clone the repo and create their own agents. Use GitHub Issues to discuss new pattern-detection ideas. Create a contrib directory where team members can add custom agents. As the original researcher, you become the maintainer of the ecosystem, not just a toolmaker. This aligns with the principle that engineering and science teams work better together.

Tips for Success

Start small: Automate one pattern at a time rather than building a monolithic agent.
Document your decisions: Include rationale for pattern thresholds—future team members will thank you.
Use Copilot for the manual part: Even after automation, Copilot helps quickly edit or extend agents.
Test on varied data: Trajectory format may differ across benchmarks; build in error handling.
Celebrate reuse: The more shareable your agent, the more collective efficiency you unlock.

By following these steps, you'll transform your intellectual toil into an automated, collaborative system. Your role shifts from manual analyst to maintainer of intelligent tools—exactly the kind of creative work you wanted to focus on.