Karthik Suresh has seen the software industry from all sides — high-frequency trading, Meta's product teams, early-stage startup trenches, and now the frontier of AI agents. As co-founder of Double-O, he's building a platform that helps non-technical teams automate complex business processes using AI agents described in plain English.
In this episode, Karthik shares hard-won lessons about what it really takes to make agentic workflows work in production — and why most companies are still getting it wrong. We talk about the structural failures hiding inside most AI proof-of-concepts, the overlooked importance of eval frameworks, and why enterprise leaders may be unintentionally blocking their own GenAI progress by clinging to control instead of creating trust.
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Inside the episode...
Why most AI agent workflows break outside the demo environment
How to architect agents like a real team, with roles and review loops
What eval frameworks are and how they protect optionality
Common failure patterns when swapping models or changing prompts
How to tell when you've actually hit product-market fit
Why pivot decisions are rarely data-first and often emotionally fraught
How startups maintain speed by designing for change, not certainty
What big companies get wrong about AI adoption and control
Lessons from a $400M trading system failure Karthik lived through
The future of agent-to-agent communication and orchestration protocols
Mentioned in this episode
AutoGen
LangGraph
Granola (AI call recorder)
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