▶️ Watch the full video on YouTube: https://youtube.com/@georgehelgesen

👉 Let's connect on LinkedIn — send me a request and mention the podcast.

In episode #13 of "Backstage Tech", we're walking through a simple 5-step framework to build a custom AI agent for your software product — without blowing your budget on something your users will never touch again.

Pulling from 8+ years working shoulder-to-shoulder with software founders, here's what separates the teams that ship AI agents that work from the ones that waste $20-30K and ship nothing:

1. Start with the right problem — not every workflow deserves an agent. Ask yourself: where do your users spend 80% of their time? That's your starting point
2. Define input and output before anyone opens a code editor — a plain-text description of what goes in and what comes out is worth more than 3 weeks of dev time spent guessing • Validate for the price of a coffee subscription — a Claude Project or MyGPT with your real data can tell you if the idea holds water before you spend a dime on engineering
3. Turn your prototype into a spec — the master prompt you tested becomes the foundation of your PRD. It's not glamorous, but it's the thing that keeps your build from going sideways
4. Understand what you're actually paying for — LLM costs can be invisible until they're not. Knowing your token burn per action is the difference between a smart feature and a financial surprise

If you own, fund, or lead a software product, this episode gives you the exact playbook to go from "I have no idea where to start" to a tested, documented, cost-aware AI agent.

👉 Want to talk through how to implement this for your product? Email [email protected] or connect with me on LinkedIn.