Podcast
The infrastructure gap between AI demos and production-grade agents, explored in seven episodes. Governance, traceability, accountability, and the missing layers enterprise teams need to ship agents they can trust.
95% accuracy sounds great, until it compounds across a five-step workflow and drops to 60%. We unpack the four ways AI agents silently fail in production and why reproducibility is the first infrastructure problem every team must solve.
RAG works for chatbots because humans catch hallucinations. Autonomous agents don't have that luxury. We explore why retrieval-augmented generation breaks down for agentic workflows, and what context graphs offer that document lookups never will.
Every enterprise has a system of record. Every one stores what happened. None of them explain why it happened. We explore the context gap that makes AI agents unreliable and unauditable, and what it takes to close it.
Fewer than 15% of AI agent POCs reach production. The problem isn't intelligence. It's authority. Why autonomy without control is deferred legal risk, why prompt guardrails fail in the real world, and what infrastructure layer every agent stack is missing.
When an AI agent approves a mortgage, rejects an insurance claim, or reroutes a crew, who is accountable? The accountability gap is the biggest unaddressed risk in enterprise AI, and the EU AI Act is making it legally real.
The gap between a brilliant demo and a trusted production system isn't about code. In this season finale, we uncover what separates the companies that ship from the ones stuck behind the POC wall. Spoiler: it's governance as infrastructure.
Patrick Joubert (Rippletide) and Jason Cui (a16z) explore what happens when the context layer thesis meets the decision enforcement layer. A collaborative conversation about what enterprise AI agents actually need to be production-safe.
The Decision Layer isn't just a podcast. It's the philosophy behind Rippletide. Every episode maps to real infrastructure we're building for enterprise AI teams.