TL;DR: Effectively directing AI agents isn't about memorizing magic prompt templates; it is about applying engineering management skills. LLMs behave like enthusiastic junior developers who confidently present half-baked solutions. To get production-grade results, you must learn to ask gently probing questions, challenge their limits, and establish clear boundary tests.
If you’ve ever managed people, you know there is a specific, quiet skill that takes years to hone. It’s the art of the gently probing question.
Imagine a junior engineer has just built a new checkout flow for an e-commerce app. They’re incredibly enthusiastic. "It’s done! Users can order t-shirts now!" they tell you. You don’t just take their word for it and push to production. Instead, you start digging. You ask: "This looks great, but how are we piping checkout analytics to the data warehouse? What happens if the payment gateway times out?"
You aren't trying to catch them out; you’re verifying that the system actually works the way you expect. Recently, I’ve realized that this exact management framework is the secret to working with AI agents. LLMs are, fundamentally, highly enthusiastic early-career engineers. They will confidently hand you a solution, and your primary job isn't just to write the initial prompt—it's to manage their output.
How do you manage an AI agent like a junior developer?
You manage an AI agent by shifting your mindset from a "spec writer" to an "engineering manager" who systematically questions assumptions. Instead of expecting a perfect first-time output, you must treat the initial generation as a draft that requires active, critical review.
When you prompt an AI to write a script or build a component, it will generate code with immense confidence. But just like a junior engineer, it often misses the broader system context. It might forget error handling, skip logging, or ignore edge cases. The skill lies in knowing when and how to ask those investigative questions to push the agent toward a robust solution.
| Behavioral Trait | How it Manifests | The Probing Question to Ask |
|---|---|---|
| Premature completion | The agent delivers code that works in a happy path but lacks resilience. | "How does this system handle network dropouts or API rate limits?" |
| False ceilings | The agent claims a task is impossible or that performance cannot be improved. | "What architectural patterns or caching strategies did you ignore to reach that conclusion?" |
| Context blindness | The agent writes clean code that doesn't align with your existing system design. | "How does this component integrate with our central error boundary?" |
Why do AI agents hallucinate boundaries, and how do you push past them?
AI agents often declare a problem unsolvable or optimization complete because they default to statistically likely, low-effort answers. You can push past these artificial limits by flatly challenging their assertions and forcing them to explore alternative problem-solving paths.
If you tell an agent, "This isn't a hard ceiling; you just haven't investigated enough alternative strategies," the model’s internal context shifts. It immediately pivots from defending its first-pass solution to searching for out-of-the-box approaches. It's the digital equivalent of telling a junior dev, "I know you think this database query can't be faster, but go look at how we handle indexing elsewhere and try again."
How do you balance manual verification with automated testing in AI workflows?
You balance this by relying on deterministic tests to verify logical correctness, allowing you to focus your human energy on high-level design and edge cases. Let your test runner do the repetitive work of verifying syntax and math so you can act as the architect.
If you have a solid suite of unit tests, you don't need to manually read every line of code the AI outputs to verify basic functionality. The tests will tell you if the code is broken. This frees up your cognitive bandwidth to ask the structural, non-deterministic questions: Is this code maintainable? Are we introducing security vulnerabilities? Are we over-complicating the architecture?
FAQ
How do you write prompts that force LLMs to think outside the box?
Instead of asking for a single solution, prompt the LLM to provide three distinct architectural approaches with their respective trade-offs before writing any code. This prevents the model from latching onto the easiest, most generic pattern.
Should you write unit tests for AI-generated code?
Yes, you should write tests for AI-generated code, and you can even have the AI help write them. However, you must verify the test cases yourself to ensure the agent didn't write tautological tests that pass simply because they don't assert anything meaningful.
What is the best way to handle an AI agent that insists a bug is unfixable?
Challenge the model's constraints by systematically stripping away assumptions. Ask the agent to explain why it is unfixable, then suggest a hypothetical architecture or a different library, forcing it to look at the problem from an entirely new angle.
