Agent Stack Masters

Scalestack.ai
  • 5.0 (1)
  • Business

Elio Narciso sits down with the operators and builders putting agents into production, to dig into what actually works: the data underneath, the governance, and the stack that makes agents reliable. Real conversations with people who have run these systems and seen exactly where they crack. Agent Stack Masters, by Scalestack.

  1. Oct 19

    Build vs. Buy in the Age of AI - Maggie Lam

    Everyone can build with AI now, but in many cases, that's become the problem. Maggie Lam of Mazarin Consulting sits down to talk RevOps governance, GTM systems, clean data foundations, tool sprawl, build vs. buy, and why companies that invest in RevOps scale faster. Everyone can build with AI now, but that is often the root of the problem. Maggie Lam explains why tool sprawl is killing your GTM efficiency. 🎙️ Maggie Lam of Mazarin Consulting joins the podcast to break down why the build vs. buy debate has shifted in the era of artificial intelligence. Instead of rushing to launch new custom solutions, she outlines the importance of establishing RevOps governance first. When you prioritize clean data foundations before adding more tech, you create a system that actually scales rather than one that creates technical debt. We discuss the hidden costs of fragmented infrastructure and how to determine if your company is truly ready to build a proprietary system or if buying a standardized tool is the smarter path. By focusing on the right GTM systems, you can ensure your team spends time on revenue generation instead of maintenance. Subscribe for weekly breakdowns on the systems that power revenue growth, and let us know in the comments: are you currently building or buying your next revenue tool? 0:00 Introduction & Background0:32 The Shift to Remote Work and Global Teams2:08 Consulting and the Role of Senior Expertise3:12 The Evolution of Service Companies in the AI Era6:35 Finding and Developing Talent in Consulting9:30 The Future of CRM and Customization12:26 Trends in Data Management and Tool Utilization15:21 Aligning Processes for Success18:24 Identifying Successful Patterns in Revenue Operations21:16 RevOps: The Underwater Struggle24:52 Building a Strong RevOps Foundation32:10 Build vs. Buy: Strategic Decisions in RevOps41:06 Governance in the Age of AI

  2. Sep 28

    The CRO Is a System Builder, Not a Closer — Brian Krause

    A Chief Revenue Officer's real job is building the system that makes deals repeatable, where sales, marketing, and RevOps move as one integrated motion instead of three disconnected teams. That's the thesis Brian Krause, CRO at Aware, brings from a career built across AnyVision, Veriff, and Incode, where he more than doubled North America's ARR. Brian believes that revenue growth follows structure, not heroics, and that structure is what separates a CRO from a VP of Sales with a bigger title. Throughout his years of experience one thing keeps resonating wherever he goes: financial fluency is the foundation underneath that structure. Reading a P&L the way most people read a deck lets a CRO build ROI models tied to what a customer's business actually needs.  Which is why he challenges himself and his team every day to reach their potential. Borrowing a line from coach Nick Saban, he knows that average is a human condition, but he’s convinced that great leaders push their teams to be extraordinary, showing them they're capable of more, then stay shoulder to shoulder while they prove it. In sales, information is the real currency, and AI has become the tool that shortens the distance between a rep and the right data necessary to take action and boost productivity. He knows it can be tempting for RevOps teams to spin up different tools and systems while handling sensitive information, so it’s important for teams to prioritize security and the CISO get involved early enough to keep customer data out of the wrong model.

  3. Jul 8

    "If Your Data Is Wrong, Your Agent Is Wrong" — Yoni Tserruya

    Data is commoditizing. More providers, more alternatives, more coverage. On paper, that should make the data layer less interesting. But Yoni Tserruya, CEO and co-founder at Lusha thinks the opposite is true. When data was consumed by a human, a bad record meant a wasted call. When data is consumed by an AI agent making autonomous decisions at scale, a bad record compounds. The agent routes wrong, scores wrong, prioritizes wrong. And it does it a thousand times before anyone notices. The problem isn't scale. It's that agents don't catch their own mistakes. That's what pushed Lusha to make a deliberate call: move away from the all-in-one platform, go deeper into the data layer. API-first. Open by design. Usage-based pricing. No seats required. Because the next consumer of their data isn't a salesperson opening a browser tab. It's an agent calling an API at scale, with no patience for stale records. Lusha turns ten this year. It started as a side project, a bootstrapped tool for pulling contact data mostly for recruiters, until Google called because it wanted to buy licenses. That was a decade ago. And the way Yoni tells it, the next decade doesn't look anything like the last one. Because the consumer of data is completely changing.

  4. Jun 1

    Your Edge Isn't the Model Anymore, with Dr. Anthony Annunziata

    Dr. Anthony Annunziata started at IBM building better memory and transistors out of magnetic materials. The work was good, but he kept feeling the same itch: get the thing out of the lab and into the world where it actually matters. That instinct shaped everything after, through quantum computing, AI for science, and now IBM's open-source AI strategy. The physicist's habit is first-principles thinking, stripping something down to what truly makes it work. But research and business part ways at the definition of done. In research, you discover, publish, and move on. In business, a demo is nothing. It doesn't work until someone else uses it, trusts it, and pays for it. He sees the same gap with agents today: demoing one is trivial, shipping one to production is the hard part. The throughline is where your edge actually comes from. Frontier models are extraordinary, but progress on them has slowed while the cost of pushing further climbs, so the model itself stops being the differentiator. What's left is yours to build: your data, an eval strategy grounded in your real use cases, and the choice to run smaller models on your own infrastructure rather than handing your proprietary edge to a tech company.

  5. Apr 8

    After the SaaSpocalypse, with Wade Foster

    In 2011, three co-founders from Missouri walked into Y Combinator with a simple observation: every SaaS help forum had the same unanswered thread. "When will you integrate with X?" Nobody was building the infrastructure to close that loop. They did. Paul Graham told Wade and every YC batch the same thing: be a cockroach. Don't raise money. Survive. Adapt to whatever comes next. Almost nobody listened. Zapier did. Fourteen years, $1.3M raised, bootstrapped to a $5B valuation. Every time a funding opportunity came up, they asked one question: what is actually holding us back right now? The answer was never the balance sheet. Then the SaaSpocalypse arrived. SaaS stocks cratered. Every software company started asking: what part of what we built actually survives this? Wade's answer is specific. Not the 8,500 integrations. The governance layer, the auth infrastructure, the background automation reliability stack. And then Wade, in this episode of Revenue Engine Masters podcast, says something incredible. They built their entire business on drag and drop. It made them. And they're already moving past it. Natural language builders outperform it on activation. They measured it. They called it "yap to zap" and didn't look back. For Wade, this adaptability applies to humans too, not just companies. The best operators today aren't the ones prompting AI and moving on. They're the ones staying in the loop, iterating on context, pushing until the output is actually good. That's the human role now. In the outer loop around it. Paul Graham's advice was about never optimizing for the conditions of today at the expense of your ability to adapt to whatever comes after. Zapier understood that in 2011. They still do.

  6. Mar 18

    "AI Hype Keeps Me Up at Night (and I'm the AI Guy)" —Barry Dauber

    Barry Dauber's job running GenAI GTM at Databricks is to convince enterprises to move faster on AI. So when he sat down at a VC dinner last year and everyone went around the table sharing what kept them up at night, the room got a surprise when his answer was "AI hype". Barry is not a skeptic. He believes AI is transforming everything, and he has the customer stories to back it up. But he has also watched enough enterprise pilots stall, enough Monday morning messages come in saying the data was not ready, to know that the gap between what AI promises and what organizations can actually deliver is real and wide. In this episode, Barry and Elio work through what that gap looks like from the inside: why most companies are not as ready as they think, what the early GTM lessons from MosaicML taught him about building before the market understood what you were selling, and what it actually takes to move from experiment to production at scale.

  7. Feb 8

    Why Selling Will Always Be Human

    Remy Piazza, Principal & CRO at Powerhouse Consulting, started his career at Xerox, the godfather of solution selling. In this episode, we explore why despite AI and evolving tech stacks, selling remains fundamentally human. From mastering discovery to building trust, Remy shares why curiosity and genuine connection will always outperform automation.

  8. Jan 16

    The Art and Science of Revenue Operations - SK Ramakuru

    Elio interviews SK Ramakuru about what RevOps really is: the “engine” behind selling that turns chaotic people/systems/data into repeatable motion. SK shares his path from Hyderabad, India—where early exposure to big tech sparked his ambitions—into consulting, then a Master’s program in the U.S. that deepened his stats and data skills. An experience of temporarily stepping into a selling role, and struggling badly, cemented a core belief: selling is art, not science. Data can point you toward opportunity, but performance depends on preparation, context, relationships, and understanding customer pain. SK explains his analytical approach: in RevOps, the goal isn’t perfect data—it’s decision-guiding data that drives behavior and feels fair, especially in territory planning. His mental model is: start by trusting nothing, validate assumptions, slice data from multiple angles, and build a clear story that leaders and the field can understand. As MongoDB scaled from ~600 to ~2,500 sellers, he emphasizes designing territories that match the right accounts to the right reps, remain equitable, require minimal additional headcount, and are explainable and auditable. SK and Elio also talk about how AI is changing both how RevOps works and what signals matter. Traditional indicators like website visits and content downloads are less reliable as buyers use answer engines (ChatGPT/Gemini) instead. That forces new time- and revenue-based indicators and stronger foundational data orchestration. SK argues the market is flooded with “AI wrappers”; the winners will orchestrate messy data across systems, not just generate answers.

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About

Elio Narciso sits down with the operators and builders putting agents into production, to dig into what actually works: the data underneath, the governance, and the stack that makes agents reliable. Real conversations with people who have run these systems and seen exactly where they crack. Agent Stack Masters, by Scalestack.

Information

  • Creator
    Scalestack.ai
  • Years Active
    2024 - 2026
  • Episodes
    27
  • Rating
    Clean
  • Copyright
    © Scalestack.ai
  • Show Website
    Agent Stack Masters