Friday, May 30, 2025 Why agents are bad pair programmers LLM agents make bad pairs because they code faster than humans think. I'll admit, I've had a lot of fun using GitHub Copilot's agent mode in VS Code this month. It's invigorating to watch it effortlessly write a working method on the first try. It's a relief when the agent unblocks me by reaching for a framework API I didn't even know existed. It's motivating to pair with someone even more tirelessly committed to my goal than I am. In fact, pairing with top LLMs evokes many memories of pairing with top human programmers. The worst memories. Memories of my pair grabbing the keyboard and—in total and unhelpful silence—hammering out code faster than I could ever hope to read it. Memories of slowly, inevitably becoming disengaged after expending all my mental energy in a futile attempt to keep up. Memories of my pair hitting a roadblock and finally looking to me for help, only to catch me off guard and without a clue as to what had been going on in the preceding minutes, hours, or days. Memories of gradually realizing my pair had been building the wrong thing all along and then suddenly realizing the task now fell to me to remediate a boatload of incidental complexity in order to hit a deadline. So yes, pairing with an AI agent can be uncannily similar to pairing with an expert programmer. What should we do instead? Two things: The same thing I did with human pair programmers who wanted to take the ball and run with it: I let them have it. In a perfect world, pairing might lead to a better solution, but there's no point in forcing it when both parties aren't bought in. Instead, I'd break the work down into discrete sub-components for my colleague to build independently. I would then review those pieces as pull requests. Translating that advice to LLM-based tools: give up on editor-based agentic pairing in favor of asynchronous workflows like GitHub's new Coding Agent, whose work you can also review via pull reques...
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Last seen: 2025-06-10 19:24