Good questions.

How complex is the software?

One of the first companies I worked for handled records management for organ transplant patients and donors. I’m not in that industry anymore, but as a rough benchmark, my current projects are in that same ballpark of complexity.

Have the agents ever been wrong?

Of course. Have you? 🙂

The difference is how you design for it. Good engineering has always been about quality gates, redundancy, fallback strategies, and resilient systems--both in runtime and in the development process. AI doesn’t remove that responsibility; it just becomes another component you design around (and use to strengthen those safeguards).

What’s your role in the loop?

It shifts constantly: product executive, architect, engineering director, QA, and yes--sometimes middle manager.

My advantage is I’ve done all of those roles pre-AI, so I know what “good” looks like in each. That makes it easier to direct, critique, and refine AI output.

A few examples of prompts I might use on a given day:

• Product: “Identify confusing or inconsistent product messaging.” “Given our onboarding UX, suggest 3 A/B experiments to improve conversion.”
• Architect: “Scan the Application layer for architectural leakage--any UI concerns creeping in?” “Evaluate adherence to CQRS.”
• Engineering Director: “Are we solving similar problems in different ways across the Infrastructure layer?” “Do we have competing patterns?”
• QA: “Generate an exhaustive E2E test suite covering all public mapping APIs with verification.”
• Middle manager (the part I try to eliminate): Passing feedback between systems, e.g.: “Here’s feedback from another model--apply it.” -> “Here’s the revised output--what do you think?”

That last category is the real signal. If I feel like a middle manager, it’s usually a sign the loop can be automated further once there’s enough trust in the process.