Recorded on
with Aishwarya Srinivasan, Arvind Narayanamurthy
Teams are no longer debating whether agents matter; they are deciding which kind to build, where value shows up, and what breaks in production. Sam's taxonomy gives builders a practical map across customer-facing, internal, and developer-platform agents, plus the cost, accuracy, rollout, and platform lessons that separate prototypes from shipped systems.
Learn the three production-agent buckets: customer-facing, internal, and developer-platform agents.
See how B2B and B2C agents use context, proprietary data, and chat to create better product experiences.
Plan for cost, accuracy, evals, early access, and pricing before broader rollout.
Use frameworks and internal platforms to avoid rebuilding primitives and move from prototype to production.
Teams are no longer debating whether agents matter; they are deciding which kind to build, where value shows up, and what breaks in production. Sam's taxonomy gives builders a practical map across customer-facing, internal, and developer-platform agents, plus the cost, accuracy, rollout, and platform lessons that separate prototypes from shipped systems.
Co-founder and CEO @ Mastra · Mastra
Sam Bhagwat is co-founder and CEO of Mastra, the TypeScript agent framework. He previously co-founded Gatsby, the popular React web framework, and has worked as an engineer at startups across Silicon Valley.
Hosted by Aishwarya Srinivasan and Arvind Narayanamurthy.
AI Advisor / AI Educator
Aishwarya Srinivasan is the Co-Founder of The Gen Academy and a Developer Relations Lead at Nebius. Her experience spans Fireworks AI, Microsoft for Startups, Google Cloud and IBM, where she worked with developers and businesses on practical AI and machine-learning solutions. She holds a postgraduate degree in Data Science from Columbia University and teaches AI engineering, applied machine learning, generative AI and LLMOps.
Founder @ The Gen Academy | AI Solutions Architect | Ex - Adobe, Microsoft, IBM
Arvind brings a cross-company perspective on AI at scale, built across Microsoft, IBM, and Adobe, where he led data science teams and developed production-grade machine-learning solutions. He has built and deployed enterprise AI systems and focuses on translating AI concepts into practical, production-ready solutions. He holds a Master's from Carnegie Mellon University.
