As an AI researcher, I recently discovered a new way to automate my intellectual toil using GitHub Copilot. This journey led me to build a tool called eval-agents, which now empowers my entire team. Here are 10 essential lessons I learned along the way about creating and collaborating with coding agents.
1. The Engineer's Pattern: Automate the Tedious to Free Creativity
Software engineers often build automation systems out of inspiration, frustration, or even laziness. The goal is always the same: remove repetitive toil so we can focus on more creative, high-value work. This pattern is so ingrained that many of us end up maintaining those systems ourselves. But the payoff is immense—what starts as a simple script can become a team-wide productivity multiplier. In my case, automating the analysis of agent trajectories transformed how I approach research data.
2. From Manual Analysis to Agent-Powered Automation
My daily work involves evaluating coding agent performance against benchmarks like TerminalBench2 or SWE-bench Pro. Each task produces a trajectory—a JSON file listing the agent's thoughts and actions. Multiply that by dozens of tasks and multiple benchmark runs, and you're looking at hundreds of thousands of lines of code. Reading all that manually is impossible, so I relied on GitHub Copilot to surface patterns. But I soon realized I was repeating the same analysis loop over and over.
3. The Birth of eval-agents: Automating Intellectual Work
That repetitive loop became the impetus for eval-agents. I decided to leverage agents to automate the intellectual grunt work. Using GitHub Copilot's capabilities, I built a system that can ingest trajectories, identify patterns, and highlight anomalies—all without me poring through endless JSON files. The engineer in me saw a problem and said, "I want to automate that." And agents made it possible.
4. Three Design Goals for Collaborative Agent Tools
When designing eval-agents, I set three core goals: make agents easy to share and use, easy to author, and make coding agents the primary vehicle for contributions. The first two align with GitHub's DNA—sharing and collaboration are in our blood. The third goal ensures that any team member can extend the system without deep specialization. This framework turned a personal automation into a platform for collective intelligence.
5. Leveraging GitHub Copilot as Your Development Partner
Throughout this project, GitHub Copilot acted as more than a code completer—it became a development partner. I used it to draft agent logic, refactor repetitive code, and even suggest new analysis strategies. The key is to treat Copilot as a junior collaborator: provide clear context, review its suggestions, and iterate. This pattern unlocked an incredibly fast feedback loop, enabling me to prototype and test agents in minutes rather than hours.
6. Trajectory Analysis: Turning Noise into Signal
Trajectories are rich with data, but raw JSON files are overwhelming. eval-agents uses Copilot-powered summarization to extract key actions, decision points, and errors from each trajectory. Then it aggregates across runs to highlight trends. For example, I could quickly see that agents often fail on specific command sequences. This turned a firehose of data into actionable insights, directly improving benchmark interpretation.
7. Enabling Peers Through Shared Agent Workflows
Once eval-agents was stable, I showed my teammates how to author their own analysis agents. Because the system uses GitHub Copilot as the core inference engine, anyone comfortable with natural language can describe what they want to analyze. Within a week, three colleagues had built custom agents for different benchmark comparisons. Shared automation amplified our research bandwidth tenfold.
8. The Feedback Loop: Agents That Improve Themselves
One unexpected insight was that eval-agents could analyze their own performance. By feeding agent trajectories back into the system, we identified where agents misinterpreted tasks or generated inefficient sequences. This created a virtuous cycle: agent outputs became inputs for improvement. GitHub Copilot's ability to refactor based on these logs made self-improvement practical.
9. Scaling Science with Agent Orchestration
We now run multiple evaluation benchmarks daily, each spawning dozens of agents. Orchestrating them—scheduling, versioning, and aggregating results—required a lightweight layer atop eval-agents. I built a simple coordinator that uses Copilot to generate orchestration scripts from natural language requests. For example, "Run SWE-bench on the latest model and compare with last week" becomes an automated pipeline. This scales our research without scaling the team.
10. The Future: Agents as First-Class Team Members
This experience convinced me that coding agents are not just tools—they're becoming collaborative team members. As agents become easier to author and share, they'll handle more intellectual tasks, freeing humans to focus on creativity and strategy. The next frontier is agents that collaborate with each other, effectively forming a self-organizing research team. GitHub Copilot is the backbone making this possible today.
Conclusion
Automating my intellectual toil with GitHub Copilot and eval-agents transformed how I work and how my team collaborates. The lessons above show that agent-driven development is about more than efficiency—it's about unlocking creativity, sharing insights, and building systems that improve themselves. If you're a researcher or engineer, consider how you can apply these principles to your own repetitive analysis loops. The future of development is agent-driven, and it starts with a single Copilot suggestion.