Agent-driven development in Copilot Applied Science
AI Summary
An AI researcher shares how they automated intellectual toil by developing 'eval-agents' to analyze large coding agent evaluation benchmarks. Using GitHub Copilot, the Copilot CLI, and the Copilot SDK with Claude Opus, the team adopted an agent-first development workflow. By emphasizing conversational prompting, rigorous documentation, continuous refactoring, and blameless processes rather than trusting-then-doubling, five team members built 11 new agents and 4 new skills in under three days.
Key Takeaways
- The project leveraged Copilot CLI, Copilot SDK, and Claude Opus within VS Code.
- A blameless process mindset treats coding agents like junior engineers, relying on guardrails, linters, and strict typing.
- The team successfully shipped 11 new agents and 4 new skills across 345 files in less than three days.
Why it matters
Demonstrates a practical blueprint for agent-driven development where coding agents act as primary contributors, showing how prioritizing clean architecture, robust testing, and clear documentation unlocks massive productivity gains.
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