Builder Stories is an official podcast of Agent.ai, where we spotlight the creators behind the agents. Each episode shares the journey of a different builderm, many of whom aren't traditional developers, showing how people from all backgrounds are using AI to solve problems, launch tools, and build their way into the future. If you're curious about what’s possible with AI agents, this is the place to get inspired.
Build AI Agents in 30 Minutes (Cursor, Codex, or Claude Code) - PROMPTED Ep 44
In this episode, we break down how to build a working AI agent in about 30 minutes using modern AI coding tools like Cursor, Codex, or Claude Code.
Matthew Stein walks through a real, end-to-end example of building an agent from scratch. No fluff. No theory. Just a practical workflow you can follow and adapt to your own ideas.
Resources & Links
👉 Agent AI Build Kit (GitHub): https://github.com/Chefmattrock/agent-ai-build-with-cursor (https://github.com/Chefmattrock/agent-ai-build-with-cursor)
👉 Agent.ai Community (get help, share what you build): https://community.agent.ai/ (https://community.agent.ai/)
You will see how to go from a rough idea to a functional agent using a simple but powerful process:
Define the problem and user
Generate a PRD (Product Requirements Doc)
Use AI coding tools to build the agent logic
Debug and refine outputs
Ship a working version fast
This is not just about Cursor. The same approach works across today’s AI coding tools and models. If you have ever wanted to build your own agents but did not know where to start, this episode gives you a repeatable system.
This is best suited for intermediate users, but beginners can follow along by pairing this with your favorite LLM to guide setup and implementation.
If you are in GTM, product, marketing, or operations, this is one of the fastest ways to start turning AI into real workflows.
What You’ll Learn
How to build an AI agent from scratch in ~30 minutes
Why everything starts with a simple PRD
How to use AI coding tools to generate agent workflows
How to debug and iterate when things break
How to turn ideas into usable agents quickly
A repeatable framework you can apply to your own use cases
Building AI agents is no longer slow or complex.
If you can clearly define the problem, AI can help you build the solution faster than ever.
Start simple. Ship fast. Improve from there.
24 Mar 2026
She Entered an AI Hackathon With No Technical Background… and Won - Ep 43
In this episode of PROMPTED, Kyle James sits down with go-to-market strategist Kat Hill Contag to talk about the experience that completely changed how she thinks about AI and building products.
Kat is a GTM consultant who works with SaaS companies on product launches and market expansion. Like many operators, she was curious about AI but did not come from a technical background.
Then she decided to try something new.
Kat entered a women’s AI hackathon using the platform Lovable, gave herself two days to figure it out, and built a tool called AI Recess, a Duolingo-style platform designed to help teams learn practical AI workflows at work.
What happened next surprised even her.
In this conversation, Kyle and Kat explore:
What it is really like to participate in an AI hackathon
How non-technical professionals can start building with AI
Why operators may become the next generation of product builders
The mindset shift required to experiment with AI tools
Why more women need to be part of the AI conversation
If you have ever wondered whether you could actually build something with AI, this episode might change how you think about it.
Connect with Kat
LinkedIn - https://www.linkedin.com/in/khillcontag/ (https://www.linkedin.com/in/khillcontag/)
TikTok - https://www.tiktok.com/@kathillcontag (https://www.tiktok.com/@kathillcontag)
Personal Website - https://kathillcontag.com/ (https://kathillcontag.com/)
Try AI Recess - https://ai-recess.com/ (https://ai-recess.com/)
17 Mar 2026
AI Won’t Fix Your Go-To-Market. It Will Amplify It. | Dale Zwizinski Ep 42
Most companies think their go-to-market is solid. It’s not.
In this episode, Dale Zwizinski of Revenue Reimagined breaks down why AI will not fix a broken GTM foundation. It will expose it or amplify it.
We dive deep into why startups and growth-stage companies skip stabilization, rush toward scale, and avoid pressure-testing their ICP, value proposition, and sales motion. Dale explains the four stages of the Go-to-Market Gap framework, why repeatability cannot happen without foundations, and how AI should be used to eliminate blank-page work, not replace strategic thinking.
You’ll learn:
Why founders overestimate their GTM maturity
The difference between stabilization, foundation, repeatability, and scale
How AI can accelerate GTM work without creating chaos
Why most companies never revisit their ICP or value prop
How to build a human + agent collaboration model for 2026
If you care about building repeatable revenue in a rapidly shifting market, this conversation is essential.
🔗 Connect with Dale on LinkedIn: https://www.linkedin.com/in/dalezwizinski/ (https://www.linkedin.com/in/dalezwizinski/)
🔗 Visit Revenue Reimagined: https://www.revenue-reimagined.com/ (https://www.revenue-reimagined.com/)
🔗 Run the GTM Gap Report for your company: https://www.revenue-reimagined.com/gtm-gap (https://www.revenue-reimagined.com/gtm-gap)
🎙 Subscribe for more conversations on AI, go-to-market strategy, and building modern revenue engines.
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