Creating vertical AI agents isn’t as straightforward as it seems. As I work on my fourth vertical AI agent, I’ve realized it’s tougher than most people expect.

While building a proof of concept is quick—thanks to tools like LangGraph, publicly available foundation models and the zero shot learning paradigm—getting to production is a whole different game.

You’ll face typical product related challenges like

• What should the agent be capable of?
• How will users interact with it?
• What’s the ideal UI / UX?

On the technical side you’ll face challenges like

• How do you measure performance effectively?
• When and how should you update the data / model?
• How do you monitor your solution in production?
• What safeguards can prevent harmful actions by the agent?
• How to prevent result inconsistency

Productionizing AI agents demand the same rigor as traditional SaaS or software projects. For non-technical stakeholders, it’s crucial to stay realistic—building a polished, reliable solution takes time, iteration, and a lot of problem-solving.

I’d love to hear your thoughts or lessons learned!