How to Automate Your Code Analysis with GitHub Copilot Agents

Introduction

Imagine spending hours sifting through thousands of JSON files, each representing an agent's decision-making process during a coding task. That's the reality for many AI researchers and engineers working with evaluation benchmarks like TerminalBench2 or SWEBench-Pro. But what if you could turn this repetitive intellectual toil into an automated workflow? Using GitHub Copilot, you can build a system that not only surfaces patterns in your data but also enables your entire team to contribute and benefit from these automations. This guide walks you through the process of creating a reusable agent-based analysis pipeline, inspired by the approach used by a Copilot Applied Science researcher who automated their own job—and then shared the tool with their peers.

What You Need

• GitHub Copilot – Installed and configured in your development environment (VS Code, JetBrains, etc.).
• Basic programming skills – Familiarity with Python or JavaScript, and experience with JSON data structures.
• A set of evaluation trajectories – JSON files containing agent steps for multiple tasks (e.g., from a standard benchmark).
• Git – For version control and collaboration.
• Node.js or Python environment – Depending on your choice for agent runtime.
• Curiosity and patience – Building custom agents requires iterative testing.

Step-by-Step Guide

Step 1: Identify Repetitive Analytical Tasks

Start by examining your daily workflow. Look for tasks where you repeatedly:

• Open the same types of files (e.g., agent trajectories).
• Ask the same questions (e.g., “Which actions caused the agent to fail?”).
• Run similar searches or pattern-matching exercises.

In the original case, the researcher found themselves constantly using Copilot to surface trends in trajectory data, then manually investigating. That repetition was the signal for automation. Write down a concrete, repeatable analysis you perform. For example: “I want to extract all instances where an agent attempted an ‘edit’ action that resulted in an error.” This becomes your first agent’s goal.

Step 2: Design a Shareable Agent Framework

Your framework should let you and your team easily author and run agents. Follow these design principles:

• Modularity: Break the agent into small, composable functions (e.g., “load trajectory,” “filter by action type,” “report results”).
• Standardized input/output: Use consistent JSON schemas so agents can share data.
• Ease of sharing: Use a GitHub repository to store your agent collection, with clear documentation and examples.

Create a base class or template that other agents can inherit from. In the original project (dubbed “eval-agents”), the researcher ensured the tool was easy to install and run with minimal configuration. Your framework should require only a few commands to set up.

Step 3: Author New Agents with Copilot’s Help

Now write your first agent. Use GitHub Copilot to accelerate development:

• Start with a comment describing the agent’s purpose: // Agent: find all “edit” actions that throw an exception.
• Let Copilot suggest code for loading JSON files, traversing the trajectory structure, and applying filters.
• Use Copilot Chat to ask for explanations of the trajectory format or to refactor code for clarity.

Example snippet (in Python):

def load_trajectories(folder_path):
    # Copilot may suggest os.listdir and json.load
    ...

def find_failed_edits(trajectories):
    # Copilot can help iterate over steps and check conditions
    ...

Test your agent on a small subset of trajectories. Refine the logic until it produces accurate results. Remember, the goal is to reduce the lines of code you manually review—your agent should output a concise summary, not another huge file.

Step 4: Implement a Contribution Model

Your team will want to build their own agents. Make this easy:

• Create a simple directory structure: agents/agent_name/main.py.
• Write a registry (e.g., a JSON config) where agents are listed with their purpose.
• Add a CLI command like run-agent --name failed-edits that executes any registered agent.
• Encourage pull requests: require basic tests and documentation for new agents.

The original researcher prioritized “making coding agents the primary vehicle for contributions.” This means you treat agent scripts like they are features of your analysis toolchain—version-controlled, reviewed, and continuously improved.

Step 5: Test and Iterate with Your Team

Deploy the framework and invite colleagues to try it. Gather feedback:

• Is it easy to run an existing agent?
• Do new users struggle to write their own agents? If so, improve documentation or add Copilot-powered starter templates.
• Are agents slow or inaccurate? Profile performance and refine filtering logic.

Schedule a regular “agent hackathon” where everyone builds one new agent. This builds a shared library of analytical tools and fosters collaboration. Over time, you’ll have an ecosystem that automates 80% of your repetitive analysis, freeing you to focus on higher-level insights.

Tips for Success

• Start small: Pick one analysis that you do daily. Automate that before expanding.
• Leverage Copilot Chat: Use it to explain your agent’s logic to colleagues. It’s like having a pair programmer who never sleeps.
• Document edge cases: JSON trajectories can vary. Add notes about unexpected structures.
• Measure impact: Track how many lines of code (or hours) your agents save each week. Share that data to motivate further contributions.
• Maintain as a side project: The original creator ended up “maintaining this tool to enable all peers.” Accept that automation requires ongoing care—but the payoff is exponential.
• Don’t over-automate: Some intellectual work, like creative hypothesis generation, is better left to humans. Use agents for the pattern-searching drudgery.

By following these steps, you can transform your own repetitive analysis into a collaborative, automated workflow—just as the Copilot Applied Science team did. Your tools will evolve, and your job might shift from manual reviewer to agent architect. That’s not automation replacing you; it’s automation amplifying your team’s capabilities.