How to Automate Your Intellectual Work with Agent-Driven Development on GitHub Copilot

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Introduction

Software engineers love automating repetitive tasks. It’s not laziness—it’s a drive to eliminate toil and free up brainpower for creative thinking. As an AI researcher on the Copilot Applied Science team, I took this principle to a new level. I built a tool called eval-agents that automates the tedious analysis of coding agent trajectories—hundreds of thousands of lines of JSON data. Now I maintain that tool so my peers can do the same. This guide walks you through the exact steps I took to create my own agent-driven development loop using GitHub Copilot. By the end, you’ll know how to identify repetitive intellectual work, design a shareable agent system, and empower your team to contribute.

What You Need

• A GitHub account with access to GitHub Copilot (Chat and Copilot for code completion)
• Basic knowledge of Python or your preferred programming language
• Familiarity with JSON or similar data formats (trajectories, logs, benchmarks)
• Node.js or Python environment for building agents
• Open source libraries (optional): e.g., json for Python, or any CLI tool
• A sample dataset of agent trajectories (e.g., from TerminalBench2 or SWEBench-Pro) to test with

Step-by-Step Guide

Step 1: Identify a Repetitive Analysis Loop

Start by examining your daily workflow. What tasks do you perform over and over? In my case, I analyzed coding agent trajectories—massive JSON files tracking every thought and action. Each benchmark run produced dozens of files, each hundreds of lines long. I’d use GitHub Copilot to surface patterns, then manually investigate. The red flag is when you think, “I’m reading the same kind of data again and again just to find the same insights.” That’s your automation opportunity.

1. List all repetitive data-processing tasks you do (e.g., parsing logs, summarizing metrics).
2. Estimate the time wasted on each task per week.
3. Choose the task with the highest pain-to-automation ratio—where a small agent could save hours.

In my case, analyzing trajectories took hours each day. I knew a small script could cut that to minutes.

Step 2: Design Your Agent System for Sharing and Collaboration

Don’t build a solo tool. Plan for your team. My guiding principle: Engineering and science teams work better together. Your agent should be easy to share, easy to author, and primary vehicle for contributions. Follow these sub-steps:

• Modular architecture — Break your agent into reusable components (e.g., a data fetcher, a pattern analyzer, a report generator).
• Simple interfaces — Use command-line arguments or config files so others can tweak without touching code.
• Clear documentation — Write a README explaining how to run, extend, and contribute to your agent.
• Template files — Provide example trajectories or sample outputs so teammates can test instantly.

I borrowed lessons from my time as an open source maintainer on GitHub CLI: if it’s not easy to use, nobody will use it.

Step 3: Build the Agent Using GitHub Copilot as Your Copilot

Now it’s time to code. Use GitHub Copilot to accelerate development. This is where the magic happens—Copilot helps you write the agent’s core logic, suggest patterns, and debug edge cases. Here’s my workflow:

1. Write a high-level pseudocode comment — e.g., “// Load trajectory JSON, extract success rates, print summary.” Let Copilot generate the initial code.
2. Iterate with Chat — In VS Code, ask Copilot Chat: “How to compute average steps per task?” or “Refactor this loop to be parallel.”
3. Test with your sample data — Use a small subset of trajectories first. Copilot can also help write unit tests for your agent.
4. Optimize for speed — If your dataset is large (like mine with hundreds of thousands of lines), Copilot may suggest using pandas or streaming parsers.

Example: For eval-agents, I used Copilot to write a function that highlighted anomalous actions in trajectories—something that previously required manual scanning.

Step 4: Make Authoring New Agents Easy

Your system should be extensible. The goal is that anyone on your team can create a new agent to tackle a different analysis task. How? Provide a blueprint:

• Create a base class or template that handles common tasks (loading data, logging, output formatting).
• Write clear examples: “To create an agent that finds all ‘ERROR’ logs, copy this file and modify the filter function.”
• Use Copilot’s inline suggestions to help teammates—when they start typing a new agent file, Copilot can predict the structure based on your templates.

I packaged my agents as Python modules with a simple CLI. A teammate could run python -m eval_agents analyze_v2 --input new_trajectories.json from day one.

Step 5: Enable Team Collaboration and Continuous Improvement

An agent system lives or dies by its community. Encourage contributions and feedback loops:

1. Host on GitHub — Make the repo public or internal; use Issues for bugs and feature requests.
2. Set up Pull Request templates — Guide contributors to add tests and update docs.
3. Create a “recipe” playbook — List common use cases (e.g., “Generate weekly benchmark report”) and link to the corresponding agent.
4. Use GitHub Copilot for code reviews — When someone submits a PR, ask Copilot to summarize changes or suggest improvements.

After releasing eval-agents, my team started adding their own analysis filters. Within weeks, we had a library of agents that covered every benchmark dataset we used.

Tips for Success

• Start small, scale fast — Automate one repetitive task at a time. Don’t try to build a full platform on day one.
• Embrace feedback loops — Watch how your team uses the agent. If they keep asking for a feature, add it. If they avoid a command, simplify it.
• Document as you go — Use Copilot to generate docstrings and comments. Future you (and your teammates) will thank you.
• Don’t be afraid to automate yourself — The goal isn’t job elimination; it’s job evolution. You’ll become a maintainer and enabler, not a code monkey.
• Leverage Copilot’s agent capabilities — Copilot is not just a code completion tool; it can now generate entire functions from natural language descriptions. Use it to prototype agents quickly.
• Test with real data early — Simulated trajectories won’t catch edge cases. Use production-like datasets to validate your agent’s robustness.

By following these five steps, you can transform your own intellectual toil into an automated, collaborative system. You’ll not only save hours of manual analysis—you’ll empower your entire team to do the same. The result? More time for creative problem-solving and innovation.