Automating Intellectual Toil: Q&A on Agent-Driven Development with Copilot
In the fast-paced world of AI research, repetitive tasks can drain creativity. One Copilot Applied Science researcher found a way to automate the analysis of coding agent trajectories using GitHub Copilot, creating a tool called eval-agents. This Q&A explores the motivation, challenges, and design decisions behind this innovation, and how it transformed team collaboration.
- What sparked the creation of eval-agents?
- What specific problem did eval-agents address?
- How does eval-agents leverage GitHub Copilot?
- What were the three core design goals for eval-agents?
- How does eval-agents improve collaboration across the team?
- What key lessons were learned about using GitHub Copilot effectively?
What sparked the creation of eval-agents?
The researcher’s daily work involves evaluating coding agent performance against standardized benchmarks like TerminalBench2 or SWEBench-Pro. Each evaluation produces a trajectory—a JSON file with hundreds of lines detailing the agent's thought process and actions. With dozens of tasks per benchmark and multiple runs per day, analyzing all these files manually was nearly impossible. The researcher often turned to GitHub Copilot to surface patterns, but realized the analysis loop itself was repetitive. The engineer's instinct to automate intellectual toil kicked in, leading to the idea of building specialized agents to handle the heavy lifting. This insight—that agents could automate not just manual tasks but also cognitive work—gave birth to the project now known as eval-agents.
What specific problem did eval-agents address?
The core problem was the overwhelming volume of data: hundreds of thousands of lines of JSON code from agent trajectories needed to be analyzed each day. The researcher had to identify patterns, anomalies, and performance trends across multiple benchmark runs. Without automation, this meant manually sifting through massive files, a process that was both time-consuming and error-prone. The tool eval-agents was designed to automate the analysis cycle: first, Copilot helped summarize the trajectories into a few hundred lines, then the researcher investigated those key insights. But even that loop became repetitive. By creating autonomous agents that could ingest, parse, and report on trajectory data, the researcher eliminated the need for manual analysis entirely. This freed up time for deeper investigative work and allowed the team to scale their evaluation efforts.
How does eval-agents leverage GitHub Copilot?
GitHub Copilot serves as the intelligence layer within eval-agents. When analyzing a new benchmark run, the system uses Copilot to automatically surface patterns in the trajectories—identifying common failure modes, successful strategies, or anomalous behaviors. This reduces the lines of code a human must read from hundreds of thousands to a manageable few hundred. But more importantly, Copilot enables the agents themselves to generate analytical reports, suggest next steps, and even propose code fixes. The researcher described this as a feedback loop: Copilot helps the agent understand the data, the agent executes actions (like patching code or writing summaries), and the results feed back into the next iteration. This symbiosis between human, Copilot, and autonomous agent accelerates development and debugging cycles dramatically.
What were the three core design goals for eval-agents?
The researcher outlined three guiding principles for eval-agents:
- Ease of sharing and use: Agents must be simple to share with teammates and run without complex setup. This builds on GitHub’s collaborative DNA.
- Easy authoring: Team members should be able to create new agents quickly, without deep expertise. The system was designed to lower the barrier to entry.
- Coding agents as primary contributions: Instead of writing lengthy documentation or manual scripts, the team’s main way to contribute improvements is through new or improved agents. This aligns with the culture of open source contribution and code-driven progress.
These goals ensured that eval-agents wouldn’t just be a personal tool, but a platform that empowers the entire Applied Science team to collaborate and innovate.
How does eval-agents improve collaboration across the team?
Eval-agents was built with the belief that engineering and science teams work better together. By making agents easy to share and author, the tool removed silos: a machine learning engineer could write an agent to analyze model behavior, while a software engineer could extend it with new data sources. The unified platform meant that everyone could contribute their expertise. The researcher noted that previously, analyzing trajectories was a solitary task; now, team members can pool their agents, compare results, and build on each other's work. The tool also includes mechanisms for peer review and version control, because agents are code. This shift turned the evaluation process into a collaborative, iterative science experiment, rather than a bottleneck for individual researchers.
What key lessons were learned about using GitHub Copilot effectively?
Two major lessons emerged from this project. First, Copilot excels at pattern recognition in large datasets when given clear context and examples. The researcher found that providing Copilot with a few annotated trajectories dramatically improved its suggestions. Second, agents should be designed as composable components. Instead of building one monolithic agent, the team created small, reusable agents for tasks like parsing JSON, summarizing logs, or generating visualizations. This made it easy to mix and match capabilities. Additionally, the importance of a tight feedback loop was highlighted: after Copilot generates an insight, the agent should act on it immediately, even if that means updating its own code. This dynamic self-improvement is what ultimately unlocked the fast development loop the researcher describes.