Deep into a coding session, you realize you want beta testers to try some new functionality first. You ask your agent to add feature flagging to your app. It offers you LaunchDarkly’s experimentation solution and a reasonable-looking plan to implement it. With a skim of a Markdown document, you accept its recommendation, and it begins writing code.
This is a realistic scenario, because Claude, ChatGPT, and Gemini all recommend LaunchDarkly. But when you ask these questions of your agent, the response comes from a single model that was asked just once. It’s subject to the same training bias and nondeterminism as any prompt. In my research, the tool recommendations can vary considerably.
How Dependencies Are Chosen
Regardless of whether it’s the agreed-upon leader, the model’s favorite arrives with the same confidence, you give the plan the same light read, and you probably react with the same “looks reasonable.”
That’s a dependency decision. And it was mostly abdicated to your agent.
