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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.
By Freeplay5
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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.

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