Building and Sharing Agent-Driven Analysis Tools with GitHub Copilot

Overview

Software engineers and AI researchers often find themselves trapped in a cycle of intellectual toil—repetitive analysis tasks that demand deep focus but offer little creative reward. One common scenario is evaluating the performance of coding agents against standardized benchmarks like TerminalBench2 or SWEBench-Pro. Each evaluation run produces dozens of trajectory files (JSON logs of agent actions and thoughts), and analyzing hundreds of thousands of lines of such data manually is impractical.

This tutorial demonstrates how to leverage GitHub Copilot to automate that analysis, turning a manual grind into a reusable, shareable tool. You will learn to identify repetitive intellectual work, design a modular agent system, and empower your team to contribute their own analysis agents—all while keeping the development loop fast and collaborative.

Prerequisites

• Familiarity with GitHub Copilot (either in VS Code, JetBrains, or GitHub Codespaces).
• Basic understanding of JSON and Python (or your language of choice).
• Access to agent trajectory data (e.g., from a benchmark like SWE-bench).
• GitHub repository to host your tool (for team sharing).
• A mindset of automation over toil.

Step-by-Step Instructions

1. Identify the Repetitive Intellectual Task

Examine the work you or your team does repeatedly. In the original example, the task was analyzing agent trajectories after each benchmark run. The pattern was:

1. Load dozens of JSON trajectory files.
2. Use Copilot to surface patterns (e.g., common failure modes, token usage, action counts).
3. Manually investigate a few hundred lines of interest.
4. Write a report or share findings.

Document this loop precisely. The key is to make the analysis scriptable—each step should be definable as a function or module.

2. Design a Modular Agent System

With your task identified, structure your automation around small, interchangeable agents. In the project eval-agents, the goals were:

• Easy to share and use – run an agent with a single command.
• Easy to author new agents – add a new analysis by writing one file.
• Coding agents as primary contributions – encourage team members to write code, not config.

Create an agent registry (e.g., a Python dictionary) that maps agent names to functions. Each agent receives the trajectory list and returns a summary or visualization.

# agents/registry.py
from typing import List, Dict

def agent_1(trajectories: List[Dict]) -> Dict:
    # Example: count total actions per trajectory
    return {"total_actions": len(trajectories)}

AGENTS = {
    "count_actions": agent_1,
    # Add more agents here
}

3. Use Copilot to Accelerate Development

As you code, rely on GitHub Copilot suggestions to write boilerplate, generate data exploration code, and even create new agents. For instance, when writing an agent that analyzes failure reasons, start with a comment:

# Count how many trajectories ended with a "timeout" or "error" status

Copilot will propose a complete function. Accept, test, and iterate. This keeps the development loop blisteringly fast.

4. Build a Shareable Command-Line Interface

Wrap your agent system in a simple CLI so teammates can run it without diving into code. Use a library like click or argparse.

# cli.py
import click
from agents.registry import AGENTS

@click.command()
@click.option('--agent', default='count_actions')
@click.argument('trajectory_dir')
def run_agent(agent, trajectory_dir):
    # Load trajectories from directory
    # Execute agent
    click.echo(f"Running {agent} on {trajectory_dir}")

if __name__ == '__main__':
    run_agent()

Share the repository on GitHub. Add a README.md with installation and usage instructions.

5. Enable Team Contributions

Lower the barrier for teammates to add agents. Provide a template:

# agents/template.py
def my_new_agent(trajectories):
    """
    Describe what this agent does.
    """
    # Your analysis here
    return {"result": None}

Encourage them to write agents that address their own pain points. Copilot helps them fill in the body quickly. Review pull requests together.

6. Iterate Based on Feedback

After the tool is in use, collect feedback. Common requests: more visualizations, export to CSV, integration with dashboards. Treat each feature as a new agent. This keeps the system organic and adapted to real needs.

Common Mistakes

• Over-automating from day one – Don't try to build the perfect system immediately. Start with automating the most painful step, then expand.
• Ignoring edge cases in trajectory data – Some JSON fields may be missing or malformed. Always add defensive checks.
• No documentation – If you don't document how to run an agent, your team won't use it. Keep a simple README.
• Single-person focus – You built this for yourself, but others have different needs. Involve them early.
• Not using Copilot for the boring parts – Many devs still write boilerplate manually. Let Copilot handle configuration parsing, file I/O, and data transformations.

Summary

By applying the principles of agent-driven development with GitHub Copilot, you can automate previously manual intellectual analysis, share your tools effortlessly, and empower your team to contribute. Start small, design modularly, and lean on Copilot to accelerate every step. The result is a faster development loop and a library of reusable insights.