On this episode of Deployed, we sit down with Ajit Varma, VP of Product at Mozilla Firefox, and former product leader at Google, Meta, Square, and WhatsApp. 

Ajit brings a unique perspective: while venture-backed AI browsers race to build what increasingly look like walled gardens, Mozilla has spent months quietly shipping privacy-first AI features that put user choice above everything else.

It's a thoughtful conversation with candid insights into why Firefox offers multiple AI chat providers in their sidebar instead of forcing users into a single model (and how this philosophical stance shapes every technical decision they make), the reality that privacy-preserving AI requires completely rethinking architecture, and why their AI features prioritize on-device processing whenever possible. 

We also get into the technical challenges that come with balancing local and cloud AI (users want SOTA quality but also want their data to stay private), the business model constraints of being a nonprofit foundation competing against VC-funded competitors who can burn cash on flashy features, and why Mozilla believes keeping the web open matters more than winning the AI browser wars.

Whether you're interested in privacy and the open web or just trying to understand what it takes to integrate cutting-edge AI into existing products, Ajit's perspective offers practical lessons from someone navigating a challenging balancing act.

On this episode of Deployed, we sit down with Surojit Chatterjee, CEO and founder of Ema, and former VP of Product at Google and Chief Product Officer at Coinbase. Surojit brings a rare perspective: he started building enterprise AI agents in early 2023—well before "agentic AI" became a buzzword- and has spent two years getting them to production quality that companies like Hitachi actually trust. 

Surojit gives us refreshingly candid insights into why Ema calls their products "AI Employees" instead of agents (and how this completely changes their approach to feedback and evaluation), the reality that most enterprise AI projects fail because companies try to automate broken processes instead of redesigning them, and why multi-agent systems are essential for handling real enterprise complexity. We dive into the technical challenges that remain unsolved (complex dynamic planning with long tool chains is still hard), his forward-deployed "agentic clinics" approach to customer success, and why sustainable scale matters more than flashy demos. 

Whether you're building AI products for the enterprise or trying to understand what it really takes to transform critical business processes with AI, Surojit's battle-tested perspective offers practical lessons from someone who's been in the trenches longer than most.

In this episode of Deployed, we sit down with Ryan Carson, Builder in Residence at Amp (Sourcegraph's coding agent) and founder of Treehouse, which taught over a million people to code. Ryan brings a rare dual perspective: he's both building his own company with AI tools and helping create enterprise-grade AI coding infrastructure.

Ryan gives us refreshingly honest insights into the hyper-competitive coding agent landscape, why traditional evals don't work for open-ended coding tasks, and the "dirty secret" that most quality decisions still come down to "dev vibes." We dive into the technical reality of competing with well-funded teams, his systematic framework for building with AI agents (which has 5,000+ GitHub stars), and why success isn't just about model capabilities — it's about solving real developer problems through obsessive attention to user experience.

Whether you're building AI products or trying to understand what it really takes to compete in this space, Ryan's grounded perspective cuts through the hype with practical lessons from the trenches.

Retry

On this episode of Deployed, we talk with Kelly Schaefer, a Product Director at Google Labs and who’s been recognized as one of the Top 100 Women in AI. Kelly has led a portfolio of experimental AI products like NotebookLM and the Jules coding agent. The Google Labs team helps turn DeepMind research into real products that can work at Google scale.

She shares what actually works when shipping AI features, from how her teams use evals to drive product quality, to why speed matters more than perfection. We also talk about how PM and UX roles are evolving in the AI era, and why she hires “dot connectors” who can bridge across domains.

If you’re building AI products, managing product development teams, or just trying to stay ahead of how this field is evolving, this conversation offers a clear look into what actually works inside one of the world’s most influential AI product orgs.

To see more of Kelly and her team’s work, check out https://labs.google/

On this episode of Deployed we talk with Kwindla Hultman-Kramer, co-founder of Daily (daily.co) and creator of Pipecat (pipecat.ai), the most widely used open source framework for voice agent orchestration.

Kwin shares insights from building voice AI infrastructure since before it was cool, including why he thinks we've hit an inflection point now where voice agents are quickly moving from demos to real production deployments with real ROI. 

He breaks down the technical stack that actually works in production in July 2025, explains why most audio-specific evals are still "vibes" (and why that's okay if you get your text evals right!), and shares tactical advice that could save months of trial and error — like why you should use WebRTC instead of WebSockets, and why speech-to-speech models aren't quite ready for production yet.

Whether you're curious about voice AI or already building voice agents, this conversation offers practical guidance from someone who's seen hundreds of teams navigate the journey from prototype to production scale.

If you want to go deeper on this content, check out Kwin's Voice AI & Voice Agents book (https://voiceaiandvoiceagents.com/) and his popular Maven course (https://maven.com/pipecat/voice-ai-and-voice-agents-a-technical-deep-dive).

On this episode we talk with Nathan Sobo, co-founder and CEO of Zed, the high-performance code editor that's reimagining how AI agents can improve developer workflows. 

Nathan shares lessons from building one of the most natural agentic coding experiences available, including why investing up front to automate the feedback loop to improve the quality of AI systems is worth it. He also shares some great product design insights that go beyond the code editor -- including how they were able to weave an AI agent into the UX of their already-collaborative product (just like a human collaborator), and how their "subtle mode" for code completion has helped win over AI-skeptical developers. 

Whether you're building coding tools or another AI product, this conversation is a fun listen for anyone who cares about crafting great products and incorporating AI in smart ways.

On this episode we talk with Sarah Guo, founder of Conviction — a VC firm focused on early-stage AI investments. 

Sarah shares insights from her unique position at the intersection of AI research, startups, and enterprise adoption that are relevant to people building with AI. 

Topics like: How successful teams keep up with rapid changes in the AI landscape, how AI labs vs. enterprises approach problems differently, thoughts on why many AI initiatives struggle to move from prototype to production. She also shares some good advice for leaders on setting an ambitious roadmap and staying competitive in the current market. 

This conversation provides practical perspective on what's working with leading AI teams, and hopefully some advice others can learn from.

On this episode we talk with Tyler Phillips, AI Product Lead at Apollo.io, about what it really takes to build successful AI products in sales. Tyler shares surprisingly candid insights about the "unsexy" but critical work that great AI PMs do - from spending hours evaluating AI outputs to building systematic quality frameworks. Learn why it can help for domain experts should write prompts instead of engineers, how Apollo measures AI quality across their products, and the practical strategies they've developed after some early missteps. Whether you're an aspiring AI PM or an engineering leader trying to structure your team effectively, this conversation offers useful lessons on what actually drives AI product success. 

On this episode of Deployed: The AI Product Podcast, we sit down with Ben Kus, CTO of Box, to unpack how they built a secure, scalable AI platform within their enterprise content management system. Ben shares candid insights from Box's journey integrating AI capabilities while maintaining enterprise-grade security for sensitive customer data. Learn practical strategies for evaluating AI quality without accessing customer data, building internal platforms that engineering teams want to use, and designing architecture that can evolve with rapid AI advances. Whether you're integrating LLMs into an established product or building new AI features, this conversation offers valuable lessons on balancing innovation with enterprise requirements. Join Ian, Co-Founder and CEO of Freeplay, for this in-depth discussion on scaling AI responsibly in the enterprise.

In this episode, we sit down with Arya Asemanfar, Product and Engineering leader at Sierra, to explore the future of AI-powered enterprise agents. Arya breaks down how Sierra is transforming customer experiences by building agents that go beyond traditional chatbots—taking meaningful actions, embodying brand voice, and scaling reliably for unique business needs.

Learn how Sierra approaches agent development with their innovative “Agent OS,” solves challenges like tool hallucination, and co-designs solutions with customers. Arya also shares actionable insights for product and engineering leaders, from building better evaluation systems to designing intuitive feedback loops.

Topics covered in this episode:

What Are Enterprise Agents?

• The difference between traditional chatbots and AI-powered agents that take meaningful actions.
• Examples of how Sierra’s agents transform customer service interactions (e.g., returns, exchanges).

Building Scalable AI Agents

• The importance of robust architecture for reliability and performance.
• How Sierra’s “Agent OS” supports the full lifecycle of agent development—from design to monitoring.
• Addressing tool hallucination and ensuring agents only take actionable, reliable steps.
• Using evaluation frameworks to test and refine agent behavior at scale.

Collaborating with Customers

• Why each agent is treated as a unique product tailored to a customer’s brand, policies, and processes.
• The co-design process and how it ensures agents align with business needs.

Actionable Advice for Teams Building AI Agents

• Start with concrete customer examples to develop intuition and build better agents.
• Implement evaluation systems early to accelerate learning and iteration.
• Invest in tools and processes to streamline human feedback and testing.

The Future of AI and Product Design

• Emerging paradigms that will transform how users interact with software.
• Predictions for how AI will shape new user experiences beyond automation.