Learn more and connect with Mike Redbord:

• Mike on LinkedIn - https://www.linkedin.com/in/mredbord/
• AI Workshop Signup - https://content.agent.ai/ai-skills-workshop
• Session Ran at Inbound - https://www.inbound.com/sessions/recQMNBsJpB6ZLhIk

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Welcome to another episode of Prompted: Builder Stories by Agent.ai.

In this conversation, we sit down with Mike Redbord, known as the “Human Helper” at Agent.ai. Mike has taught thousands of people to build their first AI agent through Agent.ai’s 101 workshops. He’s seen firsthand what sparks confidence, creativity, and real adoption.

In this episode, Mike shares:

• Why starting small is the key to success in agent building
• How play and experimentation lead to breakthrough ideas
• The role of community and co-learning in building momentum
• Insights from INBOUND 2025 and the shift from curiosity to imperative adoption
• Why an iteration mindset is more powerful than aiming for perfection

Whether you’re just getting started or already experimenting with agents, Mike’s story is full of practical takeaways to help you move from tinkering to traction.

👉 Subscribe for more builder stories and join the Agent.ai community: https://community.agent.ai/

👉 Try building your first agent today: https://agent.ai/

Learn more and connect with Logan Rivenes:

• Logan’s LinkedIn - https://www.linkedin.com/in/loganrivenes/
• Red Bike Marketing - https://redbikemarketing.com/
• Demand Gen Confidential Podcast - https://redbikemarketing.com/demand-gen-confidential/
• HR Bench Pulse Podcast - http://www.hrbench.com/resource/pulse

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“I’m of the opinion that out of the box is for amateurs.” – Logan Rivenes

When layoffs and market shifts put pressure on job seekers, Logan Rivenes, demand generation veteran and host of Demand Gen Confidential, turned to AI agents not just as a side project but as a lifeline. With 15 years of experience scaling pipeline for B2B companies, Logan knew how to work systems. What he did not expect was that building agents would land him his next big role at HRBench.

Logan describes himself as a sales rep turned marketer with a love for tinkering. That do-it-yourself streak led him to push past “out of the box” defaults in both marketing automation and AI. Rather than rely on pre-built templates, he stitched together free tools like Clay, Apollo, and Google Sheets with Agent.ai webhooks to create a fully customized job-hunting workflow.

“Every post is basically a thousand applications… How am I gonna get through the noise?”

Logan’s system started with firmographic filters (HR tech, company size) in Apollo, cross-checked in Clay, then piped into Google Sheets. From there:

• Agent.ai webhook calls scanned job boards and company sites directly.
• A validation agent enriched company profiles with funding and context.
• Manual plus AI-assisted cover letter drafting layered personalization on top.

The scale was impressive:

“The total company list was 5,000… whittled down to 10 or 15 good jobs I could apply for.”

This was not a spray-and-pray approach. It was targeted, systematic, and repeatable.

The process worked.

“I landed a gig through this AI builder.”

Logan joined HRBench, an HR data company, and immediately began applying lessons from agent building into his marketing workflows. For him, the takeaway was clear: agents are not abstract toys. They are practical leverage.

Logan’s story highlights several principles that builders can carry into their own projects. First, agent design is about workflows, not lines of code. He compares building with Agent.ai to designing a HubSpot workflow or drawing a flowchart in Miro. Once you know the outcome you want, you can break the process into steps and link them together. That mindset lowers the barrier to entry for anyone who has ever worked with automation.

Another lesson is the importance of resourcefulness. Logan deliberately avoided expensive SaaS tiers by piecing together free versions of Clay, Apollo, and Google Sheets, then connecting them through simple webhook calls. It required more manual effort, but it also made the solution more accessible and replicable. He encourages builders to focus first on solving the problem with whatever is at hand, rather than waiting for perfect conditions or full-featured subscriptions.

Finally, Logan...

Learn more and connect with Matthew Stein:

• Matthew’s LinkedIn Profile - https://www.linkedin.com/in/steinmatthew/
• Matthew’s Agent AI Profile - https://agent.ai/human/Chefmattrock

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*We wanted to take advantage of the recent momentum of the Inbound 2025 conference and are putting together a few episodes that dive into those insights with conversations from select insiders. Matthew here is the first of those conversations.

At Inbound 2025, one thing was undeniable: AI agents were everywhere.

As Matthew Stein, executive producer of Prompted Builder Stories and part of the Agent.ai team, put it:

“AI is definitely front and center stage, everywhere you look.”

This year’s conference wasn’t just about buzzwords. It was about building. The Agent.ai booth gave attendees space to post ideas on a Bright Ideas Board and then walk over to the Builder Support Station, where Matthew and the team sat down with screens open and started turning those ideas into prototypes — often in under an hour.

What stood out wasn’t just how much attention AI agents drew, but how engaged people were in translating curiosity into action. Some attendees came with vague notions (“can AI help my sales team?”), while others brought specific, costly problems. In both cases, builders could help define the problem and spin up working solutions.

As Matthew shared:

“Sometimes they’re incredibly vague… Other people came in with an extremely discreet idea… and we solved some of those building pretty cool prototypes in, you know, under an hour.”

The energy was hands-on, collaborative, and optimistic. The takeaway? Builders aren’t just theorizing about agents anymore — they’re making them real.

Across dozens of conversations, Matthew noticed clear patterns:

• Ops leads the way. Beyond sales and marketing, operations teams brought some of the most compelling use cases — where agents can eliminate repetitive workflows and unlock scale.
• Clear problem definitions win. Good agent ideas start with specific, bounded problems. Bad ones are fuzzy (“find my spiritual center”). The best? Precise tasks with measurable outcomes.
• The payoff is real. Time saved, money saved, errors reduced — those were the consistent benefits.

One standout story came from a translation company that still hired interns to manually count words in non-Latin scripts so they could price projects:

“That’s a perfect idea of something that helps save money, saves time, and reduces a bunch of grunt work.”

Within 45 minutes, the team had a prototype that handled Arabic and Greek documents with 95% accuracy — freeing interns from mind-numbing work and helping the company scale faster.

Matthew boiled it down simply:

“Basically we think of an agent as something that takes input, does a multi-step process that leverages tools, … and then returns output to you.”

And the litmus test for value?

“They need to solve painful problems, save meaningful time and money, have a thoughtful user experience, and create genuine value.”

Inbound 2025 showed how quickly builders are moving from ideas to working prototypes. And Matthew sees a clear trajectory:

“You’re going to see people jumping between applications less and less as these agents do a better job of stitching together the different places...