The Developer's Guide to Working With AI Agents

Set the scope before you set the agent loose

The single biggest predictor of a good outcome is how precisely the task was scoped going in, not the sophistication of the model or the tool. A task with a clear definition of done, relevant files identified, and constraints stated up front consistently outperforms a vague instruction handed to the most capable agent available.

Scoping well is a skill that transfers across tools, unlike prompt tricks specific to one product, which makes it the better place to invest your own learning time as the underlying tools keep changing under you.

Build a working relationship with feedback loops

Agents are only as good as the signal they get back while working, such as a fast test suite, clear error messages, and a working lint and type-check setup. Time spent improving those systems pays off on every future task you delegate, not just the current one, which makes it a better investment than time spent tuning prompts.

When an agent produces a bad result, the more useful diagnosis is usually what context or feedback it was missing rather than assuming the model isn't good enough. The latter is rarely actionable, and the former usually points to a fixable gap in your setup.

• Scope tasks precisely: clear done-criteria, relevant files, explicit constraints
• Invest in fast, reliable tests over clever prompting
• Review every result, sized to the risk of the change
• Keep humans accountable for anything irreversible or high-stakes
• Build institutional memory of what a given tool handles well on your codebase

Where to hold the line

Regardless of how capable agents get, keep a human in the loop for decisions that are hard to reverse, security-sensitive, or dependent on context that lives outside the repository, not because the tool cannot help there, but because accountability for those decisions should stay with a person who can be asked why.

The developers getting the most out of these tools are not the ones with the best prompts; they are the ones who have built codebases and workflows where good feedback is cheap and fast, and who have a clear, consistent sense of which decisions they are willing to delegate and which they are not.