How to Automate Your Intellectual Toil with Agent-Driven Development and GitHub Copilot

Introduction

If you’re a software engineer or AI researcher, you know the drill: you spend hours poring over logs, debugging traces, and evaluation results. The work is repetitive, tedious, and—frankly—beneath your creative potential. But what if you could build an AI agent to do that for you? That’s exactly what I did on the Copilot Applied Science team. I automated the analysis of hundreds of thousands of lines of agent trajectory data using GitHub Copilot and a custom tool called eval-agents. This guide will show you how to replicate that process—step by step—so you can stop toiling and start innovating.

What You Need

• A GitHub Copilot subscription (Individual, Business, or Enterprise) with access to Copilot Chat and agent mode (if available).
• A code editor like Visual Studio Code or JetBrains with Copilot extension installed.
• Programming language knowledge (Python strongly recommended) — you’ll be writing scripts to process JSON logs.
• A dataset of agent trajectories — for example, JSON files from benchmark runs like SWE-bench or TerminalBench.
• Basic understanding of evaluation benchmarks — what a trajectory is and how agents interact with tasks.
• Version control (Git) to share your work and collaborate.

Step-by-Step Guide

Step 1: Identify Your Repetitive Intellectual Work

Before you automate anything, you need a clear picture of the task that’s wasting your brainpower. For me, it was reading JSON trajectories—each containing hundreds of lines describing an agent’s decision sequence across dozens of benchmark tasks. Ask yourself: What patterns do I keep looking for? What data do I compare over and over? Write it down. This becomes the scope of your first agent.

Step 2: Use Copilot to Explore Patterns in Your Data

Don’t start coding blind. Open a sample trajectory file in your editor. Use Copilot Chat to ask questions like “Summarize this trajectory” or “List all tool calls in this JSON.” Copilot will surface common structures—e.g., every trajectory has an action field, a thought field, and a result. This exploration reveals exactly what you need to extract. For deeper analysis, ask Copilot to write small scripts that count occurrences of actions, success rates, or failure patterns. This step turns thousand-line files into actionable insights.

Step 3: Design an Agent That Automates the Recognition

Now you have a pattern—say, “Agents that fail on task X often spend too long in the ‘think’ state.” Design an agent that scans all trajectories and flags that condition. I called mine eval-agents, but you can name yours anything. Sketch the flow: load trajectories → run pattern detector → output a summary. Keep it simple. Your agent should be a function or a class that takes a file path and returns a short report. Write the design in a markdown file or just talk it through with Copilot Chat.

Step 4: Implement the Agent with Copilot’s Help

Open a new script file. Start typing a comment like # Load all JSON files from a folder — Copilot will suggest the code. Use Copilot’s inline suggestions to fill in the detection logic. For example, if you need to parse nested JSON, let Copilot complete the dictionary traversals. If you’re unsure about data structures, ask Copilot Chat: “Write a function to extract the ‘thought’ array from my trajectory object.” This step moves fast because Copilot handles the boilerplate. By the end, you’ll have a working agent that runs on a single sample file.

Step 5: Test the Agent on Benchmark Runs

Run your agent against a full benchmark dataset (dozens of tasks, each with its own trajectory). Check the output. Does it catch the patterns you expected? If not, iterate: ask Copilot to refine the detection criteria. For instance, I found my first agent missed subtle action sequences, so I used Copilot to add a regex pattern matcher. Rerun on each new benchmark run to ensure consistency. This step validates that your automation saves real time.

Step 6: Share the Agent with Your Team

The magic of agent-driven development is collaboration. Commit your code to a shared repository. Write a README that explains how to use the agent (include examples from your testing). Enable teammates to run it on their own data. Because your agent is lightweight and modular, others can extend it—maybe adding a new pattern detector or integrating with a dashboard. Use GitHub Issues or Discussions to collect feedback.

Step 7: Iterate Based on Feedback

Your first agent won’t be perfect. Teammates will ask for new features: “Can it also export to CSV?” or “Can it compare two benchmark runs?” Use Copilot to evolve the agent. Open the existing script and describe the new requirement in a comment—Copilot will suggest the changes. Each iteration increases the value for everyone. Before long, your agent will handle peak analysis loads effortlessly, freeing your team to focus on research rather than drudgery.

Tips for Success

• Start small: Automate one pattern first. Once that works, add more. Don’t try to build the Swiss Army Knife of agents on day one.
• Use Copilot Chat as a thinking partner: Describe what you want to automate in plain English; let Copilot suggest approaches you might not have considered.
• Keep the code modular: Each pattern detector should be a separate function. This makes it easy to test, reuse, and share.
• Version everything: Save every iteration of your agent. You’ll often want to revert or compare behaviors.
• Encourage contributions: Make your repository welcoming with a CONTRIBUTING guide. Teammates will add their own patterns, turning your single tool into a suite.
• Treat trajectories as gold mines: They contain not just failures but also rare success patterns. Build agents that highlight both.

By following these steps, you can transform repetitive intellectual work into a self-running system—just as I did with eval-agents. You’ll not only speed up your own workflow but also empower your team to collaborate on solving harder problems. Now go automate something you used to think was too complex to delegate.