✨ AI Agent Prototyping at $20? 5 Steps From Idea to Production

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

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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 ogo@procoders.tech or connect with me on LinkedIn.

Transcript

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If you're a software founder building an AI agent, watch this. I'll walk you through 5 simple steps so by the end of this video you'll know exactly how to build a custom AI agent for your software product. Spoiler, I've designed a framework for AI agent development that you can grab in the description below. It's absolutely free and it's so simple that even if you're a non-technical founder, you'll get it. Before I start, make sure you subscribe to get more videos like this. I share useful content for software founders every two weeks on this channel. Let's go. Here's what's possible when you nail this. Your users stop clicking through multiple pages to do basic tasks. They type what they want in plain English and it's done. Your support tickets drop by 30% because your product just got smarter. And here's the kicker. You're not guessing anymore. Now you've got a well-tested, working AI agent that your power users are already asking for more of. That's the finish line. But most founders, they never get there. Here's what I keep hearing. George, how can we build our AI agent? And I get it, agents are everywhere right now. Nearly half of Y Combinator last sprint batch were building AI agents. Everyone's jumping on this train. But here's the real problem. Your team has been building product features for years, but nobody's built an AI agent before. So when you try to build one with a team that doesn't understand how agents work, you're setting yourself to waste 10, 20, maybe 30 grand on something that is slow, clunky, and hallucinates responses. I've watched founders blow through their budget building an agent that their users try once and never touch again. That's the nightmare scenario. You spend the money, you ship the feature, and it just sits there collecting dust while your competitors figure it out first. So instead of learning the hard way, let's walk through a simple five-step plan that helps you define, prototype, and build an AI agent that actually works. Step number one. Define. First question. Where do your users spend 80% of their time? What's the one thing they do every single day over and over again? Let me give you some real examples. Imagine Airbnb or Booking.com. The users are constantly filtering searches. They want properties with different amenities for a date range in a specific location. Another example, Salesforce or HubSpot CRM. Their users are living in deals, tasks, and contacts, updating the pipeline, sending follow-ups, and trying to move deals forward. One more example. Slack Messenger. Their users are drowning in threads, trying to catch up on what five people chatted about for an hour while they grab lunch. Once you answer the first question, you'll have two things crystal clear. First, product area. For instance, product search, CRM deals, messenger threads, etc. Second, single action to automate. Find a property, update a deal, summarize a thread, and so on. This is your North Star. Now you know exactly where your AI agent will save time and deliver instant value. Miss this step, and you'll build an agent that solves a problem that nobody has. Step number two, input output. Alright, now that we know what, let's nail down how. This is straightforward. You need an example of what goes in and what comes out. Most agents use chat or search bar, so let's work on that. Example 1. Search bar. Input Two bedroom villa in Miami, July 20 to 26, 4 people, need the pool. Output. Property component with images, price and cancellation policy. Example 2. CRM chat agent. Input. Move the company X deal to 1, update budget to 15K, add one reason as discount. Output. Agent updates the deal and confirms. Deal with company X updated to one status. Budget set to 15K. Reason discount. The agent does the heavy lifting under the hood and just tells you it's done. Simple response in the chat. Example 3. Action button. Input. User clicks summarize thread. Output clean summary message. Nothing fancy. See how it works? You're describing in plain text how it looks and behaves before you design a single thing. And honestly, you can ask Cloud or GPT to help you figure out what format fits your product best. Skip this step, and your developers will build what they think you want, not what your users actually need. Step number three, prototype. Here's where it gets fun. Build a proof of concept in Cloud or GPT before you write a single line of code. I remember when clients first asked me to build AI agents and they weren't sure they should invest. I'd say, let's build a prototype at a cost of a monthly subscription and test if it even makes sense. That's exactly how I help clients validate their concepts before spending real money. Here's the simple flywheel. Number one, build a master prompt. Describe how the agent behaves, show input-output examples, define communication style, tell it what it can do and what it can't. Don't write it from scratch. GPT, Cloud, or Gemini will help you. Number two, set up MyGPT or Cloud project. Both lets you add your master prompt and upload files. Here's the killer move. You can upload a slice of your actual database with products or deal samples so the agent can work with the real data. Number three, test and refine. Write queries, see how it responds, tweak your master prompt until the output is sharp. That's how you test each tool. And by tool, I mean one action that agent can perform. Update a CRM record, find a product, whatever it is. Take this prototype to your power users. Ask them, would you actually use this? Why or why not? What will make it better? This is where you separate the winners from losers. Winners says cheap and fast. Losers skip this and go straight to building. Step number four, document. Once you've nailed down the prototype, build a spec for your engineers. This is for your internal team or an external developer if you don't have AI chops in house. Take your master prompt, the input-output examples, then ask Claude, GPT, or Gemini to generate a spec. I personally prefer Claude Opus 4.5, but check which is the flagship model if you're watching this later than Q1 2026. You need functional and non-functional requirements, PII instructions for what goes to the LLM and what doesn't, specific tools the agent has and how they interact. You don't need a product manager to write the PRD, but you absolutely need one to review and approve it before anyone starts building. This PRD becomes your insurance policy. When things go sideways, and they will, you got a clear reference point for what you actually agreed to build. Step number 5. Investment. Let's talk money. Because we are running a business here, not a charity, your developers can tell you how many LLM tokens each action burns. You need to calculate this before you commit. Say generating a proposal costs you a dollar per request. One user does it 50 times a day and you've got 20 users doing this all week. That's a grand a week on one feature. That's insane. But if upgrading a CRM deal costs you a penny per action and users do it a hundred times a day, you're looking at a dollar a day or about 22 bucks a month. And if that saves your users 150 hours, that's a no-brainer. Your job is simple. You need to understand what you'll be paying OpenAI or Anthropic to run the thing. Look, here's what just happened. You went from I have no idea where to start to having a clear 5-step roadmap. You know how to design the AI agent, how to test it, and how to make sure it won't bankrupt you. And by the way, grab the framework in the description. It's got this entire 5-step plan laid out so you can actually execute on this. It's free, it's simple, and it will save you from wasting months spinning your wheels. Building AI agents doesn't have to be this massive, scary projects where you blow your budget and get nothing. You define what matters, prototype it cheap, test with real users, and know your costs. You're now ahead of 90% of founders who are still guessing. And when your users start asking can the AI agent also do this? That's when you know you nailed it. If you found this video helpful, hit that like button and subscribe for more frameworks like this. I'll see you in the next one.