エピソード

  • Inside Inbound 2025 with Matthew Stein: AI Agent Buzz and Practical Builder Takeaways - Ep 17
    2025/09/18

    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

    --

    *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.

    Theme 1: The Vibe at Inbound 2025

    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.

    Theme 2: Practical Lessons from Builder Conversations

    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.

    What Makes a Good Agent?

    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.”

    Looking Ahead

    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...

    続きを読む 一部表示
    39 分
  • How Erol Aykan Built ObjectionOwl to Scale Sales Coaching with AI - Ep 16
    2025/09/16

    Learn more and connect with Erol:

    • Erol’s LinkedIn - https://www.linkedin.com/in/erolaykan/
    • ObjectionOwl Agent (Award Winner) - https://agent.ai/profile/objectionowl
    • Sales Discovery Coach Agent - https://agent.ai/profile/discovery-coach

    --

    “What’s really important to me is that I build something that makes a change in someone’s life.”Erol Aykan

    When Erol Aykan, a Sales Manager at HubSpot, first started tinkering with AI, he wasn’t chasing awards. He was trying to solve a problem for his team. A tedious process that used to take five hours a week could now be done in just 20 minutes, thanks to a simple Chrome extension he built by prompting an LLM. That lightbulb moment kicked off his builder journey.

    From there, Erol leaned into his strengths as a sales leader: frameworks, repeatable processes, and a relentless focus on coaching. The result? Two standout agents, ObjectionOwl and Discovery Coach, that transform raw sales call transcripts into structured insights and actionable coaching feedback.

    Erol describes his philosophy this way:

    “Make it scalable, make it simple, and build on a framework.”

    For him, Agent.ai wasn’t just about fancy output. It was about building tools that sales reps and managers could actually use to improve every single day. ObjectionOwl surfaces and categorizes deal-blocking objections in seconds. Discovery Coach turns discovery call transcripts into a coaching scorecard, breaking down strengths and areas for improvement.

    What makes Erol’s story powerful for the builder community isn’t just the award-winning output, it’s his mindset. He didn’t try to build “software.” He started with a small, painful use case and scaled it into something useful. And he’s quick to point out that anyone can do the same:

    “Don’t overthink it. Start small, and progress it into something applicable and useful for you.”

    Erol’s journey is a reminder that the most impactful agents often come from people closest to the problem. You don’t need deep technical skills to start, you need curiosity, a willingness to ask good questions, and the drive to make a difference.

    続きを読む 一部表示
    42 分
  • Julia Turnbull: From Global Development to Award-Winning Agent Builder - Ep 15
    2025/09/09

    Learn more and connect with Julia Turnbull:

    • Julia’s LinkedIn - https://www.linkedin.com/in/julia-turnbull/
    • Award Winning Agent - https://agent.ai/profile/grantfunding
    • Bonbillo - https://www.bonbillo.com/

    --

    When you look at Julia Turnbull’s journey, it’s easy to see why she became one of our Agent AI Community Award Winners. Her career has spanned grassroots entrepreneurship in Mexico, impact finance across Latin America and Africa, and leadership at MIT Sloan Executive Education. The throughline? Helping founders find the resources they need to build and scale.

    Now she’s taken that experience and turned it into something new: an AI agent that helps startups discover grant opportunities.

    “I started kind of learning more about what we now call or think of as entrepreneurship before it necessarily became the discipline that it is now.”

    That early curiosity has carried through her work with founders around the world. She saw a recurring pattern: entrepreneurs are big on ideas but often struggle with structure, especially when it comes to financing.

    “We need ideas. But I think really giving people a structure—frameworks, goals, things to work towards—is critical. The only thing worse than not having any funding is having the wrong kind of funding.”

    Turning Expertise into an Agent

    Julia partnered with the BonBillo team to translate years of tacit knowledge into an accessible, scalable tool. The result: an agent that helps entrepreneurs quickly surface grant opportunities aligned with their industry, stage, and geography.

    For Julia, the act of building was just as valuable as the end product:

    “It’s one thing to explain knowledge to other people all day—it’s another to explain it back into an agent. Building it forced us to decide what was really most important.”

    This is a lesson for all builders: encoding expertise requires ruthless prioritization. Agents aren’t just about replicating your knowledge; they’re about distilling it down to what’s most useful for others.

    A Tool for Focused Discovery

    The grant discovery agent doesn’t try to do everything. Instead, it gives founders a clear starting point in a notoriously messy landscape.

    “Startup founders are busy… this is a tool for people to take off the blinders and see what else is available. It’s really a research tool, giving early-stage teams a high-level overview of what’s out there.”

    The practical takeaway for builders: the best agents succeed by removing friction and freeing up time for their users.

    Agents as Team Members and Bridges

    Julia also sees agents not just as productivity tools, but as enablers of new networks and opportunities.

    “Think of AI as a tool and as a bridge. These agents can become team members and do some of the thinking or the work for you, but they’re also an entrée into new networks and opportunities.”

    For builders, this is an important shift in perspective. The real power of an agent isn’t just in the tasks it performs, it’s in the connections it creates.

    Wisdom to Build By

    As the conversation wrapped, Julia shared a piece of advice she’s...

    続きを読む 一部表示
    27 分
  • Award-Winning Builder Monique Howard on Meetings That Shoulda Worked Better - Ep 14
    2025/09/02

    Learn more and connect with Monique Howard:

    • Monique’s LinkedIn - https://www.linkedin.com/in/monique-h-82b2722/
    • Shoulda Been An Email Agent - https://agent.ai/profile/shoulda-been-an-email
    • Smarticles Website - http://www.mysmarticles.com/

    --

    "What's really important to me is that I build something that makes a change in someone's life." – Monique Howard

    That’s the heart behind Monique’s work, and it’s also why she was recognized as one of our very first Community Choice Award winners for her agent, Shoulda Been an Email.

    At first glance, the name is funny (and instantly relatable). But dig a little deeper, and you see the serious builder’s mindset behind it. Monique saw meeting transcripts not just as records, but as raw material:

    “Meeting transcripts in the world of AI are gold because you can do everything with a meeting transcript.”

    Her agent uses transcripts to evaluate meetings against their agenda, then delivers witty, practical feedback on how to facilitate better. Instead of a dry checklist, you get a voice with personality. Her agent helps people not just run meetings, but learn to run them better.

    And Monique’s motivation goes even deeper. Her journey into agents started years earlier when she built tools for her children on the autism spectrum. From experimenting with Alexa to creating Color Together, she’s always been driven by one question: How can this technology make a real difference in someone’s life?

    That’s what makes her story resonate so strongly in this community. Yes, agents can save time and cut repetitive tasks. But they can also be designed to teach, to coach, to include, and to change lives.

    Builder takeaway: When you’re creating agents, don’t stop at efficiency. Ask yourself: What lasting impact could this agent have on the way people learn, connect, or grow?

    Big thanks to Monique for showing us what it looks like to build with both craft and heart.

    --

    続きを読む 一部表示
    44 分
  • What Paul Schmidt Learned Building AI Agents That Drove $1M in Efficiency at SmartBug - Ep 10
    2025/08/30

    Learn more and connect with Paul Schmidt:

    • Agent.ai Profile: https://agent.ai/human/paulschmidt
    • LinkedIn: https://www.linkedin.com/in/drumming/
    • X (Twitter): https://x.com/drumming

    Public Agents by Paul:

    1. Manufacturing sell sheet creator - https://agent.ai/profile/manufacturing-sell-sheet-creator - Instantly generate professional, one-page sell sheets for your manufacturing products.. Used this on an agent AI workshop with prospects, where we all worked on this agent together and everyone walked away with their own customized version of this agent.
    2. Agentic Idea Generator - https://agent.ai/profile/agentic-ai-idea-generator - Helps companies brainstorm agents that align with a consumption based pricing model
    3. AI Dataset Recommendation Agent - https://agent.ai/profile/AI-Dataset-Recommendation-Agent - Good AI outputs requires good data input. This agent helps departments think through the types of datasets they need to create for effective AI output.
    4. GiftSage: The Corporate Gifting Strategist - https://agent.ai/profile/corporate-gifting-ideas - Purpose of this agent is to provide the top gift ideas that would resonate most with your customer or prospect or partner.
    5. Non-boring Event Swag Idea Agent - https://agent.ai/profile/event-swag-generator - Purpose of this agent is to provide the top swag ideas that would resonate most with an audience of an event.

    --

    “We think about almost a million dollars in efficiency that we've created just by launching this agent for us.”

    — Paul Schmidt, VP of Marketing & Innovation, SmartBug Media

    How do you prep your sales team with deep insights before a first call, without making them do hours of manual research?

    For Paul Schmidt and the team at SmartBug Media, the answer came through a homegrown AI agent that transformed their CRM into a powerful prospect intelligence tool.

    By integrating HubSpot with Agent.ai, they built a research agent that auto-generates company background, technographic data, case study matches, discovery questions, and even custom follow-up emails. Sales reps now go into calls sounding like insiders, with no manual digging through spreadsheets or old decks required.

    Paul’s estimate? Nearly $1M in efficiency gains by reducing the time spent on prep across their sales pipeline.

    And none of this happened all at once.

    It began with something much smaller: an agent designed to stop the noisy Slack messages like “Has anyone worked with a manufacturing client before?” That early win helped them organize historical case study data, which became a key input for their more advanced sales research agent.

    The core takeaway?

    “Start small. Build for the future.”

    And get your data in order.

    Once the foundation was in place, the SmartBug team started layering on more complexity. They began

    続きを読む 一部表示
    31 分