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

• DoubleO.ai

• Craft.co

• Ignition

• Ocar.io

• Ragas eval framework

• CrewAI

• AutoGen

• LangGraph

• Granola (AI call recorder)

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