I've hired 5 different AI engineers. And fired 3 of them. Here are their mistakes

1. No real business value delivered

Many engineers focus on building workflows that look impressive from a technical perspective. However, complexity alone doesn’t matter if it doesn’t translate into business value. As an agency, I sell outcomes and impact—not technical sophistication. This applies to any business, yet it’s something people often overlook.

2. Lack of production-level quality

Production-ready AI workflows and agents require far more depth than demos or freebies. Several engineers I worked with consistently overpromised but failed to deliver work that met production standards. Having been a software engineer my entire life, I can quickly assess code quality, architecture decisions, and long-term maintainability—and these gaps were clear.

3. Weak ownership and execution mindset

Beyond ideas and implementation, strong engineers take ownership of outcomes. Some struggled with accountability: tasks required excessive guidance, deadlines slipped, and problems were surfaced late instead of being proactively addressed. In an agency environment, execution, communication, and responsibility are just as critical as technical skills.

If you want to become an automation engineer, keep these in mind!

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