Three Things That Will Go Wrong When You Introduce AI Agents

Here are three things that are guaranteed to go wrong if you introduce AI agents into your software development right now:

1. Alignment: Every one of your engineers brings different experiences and opinions on AI to the table. As long as skeptics and enthusiasts don’t engage in a constructive dialogue, you can’t build a solid foundation.
2. Single point of failure: AI agent infrastructure is evolving at a breakneck pace. Even the big players (Anthropic, OpenAI) make some hair-raising mistakes along the way. Bet everything on one horse and you’ll have very unproductive days.
3. Resource management: You don’t want to set up a process that cognitively exhausts your team. You want efficient workflows that reliably deliver good results over the years — and that your people enjoy working with.

You might be thinking now: “Well, then AI agents aren’t as great as everyone says they are.”

Fair enough, but my experience shows: with the right introduction, AI agents make your team noticeably faster, maintain or improve the quality of your product — and your team comes to enjoy them.

You might be wondering now: “Okay, so what is the right introduction?” Certainly not the one that chases the hype.

The right introduction:

• meets everyone where they are, whether skeptic or enthusiast,
• conveys the essential AI agent fundamentals every engineer needs to know,
• shows examples where AI agents can cut implementation effort in half or even by two thirds, and ones where AI agents are the wrong tool,
• makes you independent of any single vendor,
• and provides methods and frameworks so engineers can work effectively with AI agents long-term, without cognitive exhaustion.

Anything else, in my view, makes no sense. That’s exactly what I offer.

If you want to take this seriously and strengthen your development with AI agents sustainably, I can help: Explore the AI Agent Engineering Workshop