AI Podcast Weekly: The Secret Sauce to Scalable AI Agents | Agentic
By: Travis Fleisher
Welcome to the first installment of the TwinBrain AI Podcast Weekly, a weekly breakdown of the most insightful, tactical, and inspiring conversations happening at the frontier of artificial intelligence. Each week, we’ll distill key learnings from the best AI podcasts featuring founders, engineers, investors, and operators pushing the boundaries of what AI can do.
This week, we're diving into a powerful episode from the Agentic Podcast featuring the founding team behind Agency AI, the infrastructure startup helping developers and enterprises build the next billion AI agents.
In early 2023, most investors still rolled their eyes at the idea of AI agents. Now, in 2025, it’s all anyone in tech is talking about.
Few companies have had a better front-row seat to this evolution than Agency AI. Co-founded by Adam Silverman and Alex Reibman, Agency AI is building the infrastructure powering what they call the next one billion agents. Their primary offerings - AgentOps and AgentStack - are changing the way developers and enterprises approach agentic systems.
From Angry Customers to Infrastructure Pioneers
The origin story is classic startup lore: Adam and Alex were building AI tools for accountants, and the agents they developed worked... until they didn’t. Performance plateaued at 90%, bugs and loops plagued users, and the customer support inbox was flooded. Rather than giving up, they built internal tools to debug their own agents. Turns out, others needed them too.
Enter AgentOps: an observability and testing platform for AI agents. Developers use it to answer questions like: Why is my agent breaking? Which LLM is more cost-effective? Where is the bottleneck? In a noisy market full of orchestration frameworks (LangChain, AutoGen, CrewAI), Agency AI carved out a critical layer: making agent development fast, safe, and scalable.
Solving Fragmentation in the Agentic Landscape
One of the biggest pain points Agency AI is solving? The fragmented state of the agentic ecosystem.
In 2024, building an agent meant cobbling together multiple disconnected tools: orchestration frameworks like LangChain or Autogen, separate observability platforms (if any), and custom logic to string everything together. Each layer of the stack, from LLM to framework to tools to observability, was siloed. There was no standard. And for developers, the cost was high: time wasted switching platforms, unclear debugging paths, and no unified way to measure performance or iterate quickly.
Agency AI is fixing this by offering a unified experience.
With AgentStack, developers can scaffold a complete agentic architecture in minutes, choosing from their preferred LLMs, orchestration layers, and tools. Everything is pipelined into a common structure that flows into AgentOps, their observability engine that tracks every prompt, response, cost, and failure across agents—no matter which framework you're using.
They aren't trying to replace frameworks like Crew or LangGraph. Instead, they integrate with them, providing the connective tissue that makes the ecosystem cohesive.
This means:
Indie hackers can test ideas without getting lost in the weeds.
Enterprises can monitor and scale dozens of agents with one dashboard.
Developers can move from prototype to production in hours, not weeks.
The result is a dramatically lower barrier to entry, and a vastly improved development lifecycle. As Brillin Boon put it in the podcast, "We can now build an agent in five minutes that used to take teams weeks."
Sales Calls and Secret Live Demos: A Brilliant Move
One of the most impressive (and fun) moments in the episode is the story of how Braelyn Boynton, the founding engineer, regularly live-codes agent prototypes during sales calls. Yes, while a potential customer describes what kind of agent they want to build, Braelyn is quietly spinning up a working version behind the scenes.
By the end of the 30-minute call, the prospective client isn’t just hearing a pitch, they’re looking at a demo.
I found this absolutely brilliant. It turns the typical sales process on its head. Instead of talking in hypotheticals, you're proving value in real time.
It’s the kind of move that sticks with you, not just as a technical flex, but as a signal that the future of agent development is fast, dynamic, and astonishingly accessible.
Real-World Use Cases: From Airlines to Obituaries
So what are Agency AI users actually building today?
Travel Tech: One company built a customer support agent to help travelers update itineraries, request refunds, or change personal details. They used Microsoft’s AutoGen for multi-agent coordination and AgentOps to monitor performance.
B2B SaaS: A freemium software company deployed agents to automate 80% of support tickets for free-tier users, turning a cost center into a profit center.
Obituary Analysis: A startup used agents to track every obituary in the U.S. and identify next-of-kin contacts—a process previously handled by a full-time human research team.
From voice agents in insurance to VC scouts building robo-investors, the applications are endless.
Connecting the Dots: Agents, Automations, and Workflows
If you read our most recent blog, "Agents, Automations, and Workflows: The AI Unlock Hiding in Plain Sight", you know we explored the subtle but critical distinctions between agents, automations, and workflows. What Agency AI highlights so well is how observability and stack flexibility are essential when building true agents, the kind that operate with autonomy, tools, and task memory.
AgentOps fills the gap we previously discussed: moving beyond scripted automation into robust, self-correcting agents capable of learning from failures. Meanwhile, AgentStack offers the structural scaffolding to build and test agentic workflows at scale.
These aren’t just automations or one-off bots. They’re the early building blocks of organizational intelligence.
Lessons for Founders: Build Simple, Ship Fast, Talk to Customers
The Agency AI team has learned a lot from building in public, winning hackathons, and talking to customers daily. Some of their best advice:
Start simple: You don’t need a breakthrough idea to start. One of their early projects, a simple chat-with-PDF app, made $25K with one week of work.
Validate early: Talk to customers before you build. Then talk to them again.
Build in public: Share demos. Test interest on Twitter. Let the market guide your roadmap.
The Vision: A Proactive Agent on Every Desktop
Today’s agents are reactive. You build them, you prompt them, and they respond. But the Agency AI team sees a future where agents are proactive.
Imagine an agent that quietly watches how you work, then says: “Hey, I noticed you sent 12 similar emails this week. Want me to handle that next time?”
That’s the world they’re building toward—and if the momentum behind Agency AI is any sign, it might arrive sooner than we think.
Whether you're optimizing sponsorship strategy at a major league or streamlining fan engagement workflows inside a team’s front office, AI agents no longer require expensive dev teams or outsourced builds - they’re now accessible, fast, and practical. And Agency AI is building the infrastructure to help sports marketers and operators deploy them with speed, reliability, and scale.
This post is part of our weekly AI Podcast Review Series, where we break down the best insights from founders building the future of artificial intelligence, from agent infrastructure to creative AI to enterprise automation. Stay tuned each week as we spotlight new builders, tools, and use cases from the front lines of AI.
Learn more: https://www.agen.cy
Travis