Why I’m Learning Python, Prompting, and Agentic AI

Most people know me as someone who places high-level talent in hospitality. I’ve helped build leadership teams at resorts, clubs, and high-touch properties around the country. That work isn’t going away, but lately I’ve been asking a different kind of question.

What happens to leadership when AI starts shaping the teams?

Agentic AI is already shifting how work gets done. These are systems that don’t just respond to questions like chatbots do. They act on goals. They search, plan, make decisions, and complete tasks without being micromanaged. And they’re not theoretical. Microsoft, LangChain, CrewAI, and dozens of startups are rolling them out right now. Tools like Gumloop.com let anyone create AI agents that can search, write, analyze, and make decisions across tasks, no engineering team required.

The common thread across all of them is prompting. But not the kind you do in ChatGPT for fun. I’m talking about prompt engineering. It’s structured language that guides AI behavior across multi-step workflows. McKinsey recently called it one of the fastest-growing skillsets in the market. Companies want people who can work with AI, design prompts, and understand how autonomous agents operate. And they’re not just looking for engineers.

So I’m learning Python. I’m diving into multi-agent frameworks. Not because I plan to write production code, but because I want to speak the same language as the teams building this stuff. If I’m going to represent talent in this space or work with founders shaping the next wave of AI companies, I need to understand how it works under the hood.

That doesn’t mean walking away from hospitality. It means adding range. It means becoming a recruiter who can sit in a meeting with a technical founder or a product lead and understand what they need beyond a job description.

AI isn’t just a wave. It’s a restructuring. And I’m making sure I’m ready for the next decade of hiring, not the last one.

Are you planning for the work of the future, or still using yesterday’s playbook?