For AI platform teams
Make agent decisions reproducible before they execute.
Start with the episodes on reproducibility, context graphs, and why RAG alone is not enough for autonomous workflows.
Start herePodcast
A seven-episode series for AI builders moving agents from demos to production. We unpack governance, traceability, accountability, context graphs, and the decision enforcement layer enterprise teams need before agents can act with trust.
Series guide
The Decision Layer frames the questions enterprise teams need to answer before AI agents execute real actions: can the decision be reproduced, is the context valid, who authorized the action, and can the outcome be audited later?
For AI platform teams
Start with the episodes on reproducibility, context graphs, and why RAG alone is not enough for autonomous workflows.
Start hereFor governance teams
Follow the episodes on authority, accountability, and the controls enterprises need before agents touch production systems.
Start hereFor product leaders
Use the finale and context-versus-enforcement conversation to frame the gap between POCs and deployed AI agents.
Start here95% 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.
Podcast FAQ
The Decision Layer is a Rippletide podcast about the infrastructure gap between AI agent demos and production-grade agents. It focuses on governance, traceability, reproducibility, accountability, context graphs, and pre-execution decision enforcement.
The series is for AI platform teams, CTOs, product leaders, and governance or risk teams that are moving autonomous AI agents from prototypes into enterprise workflows where every decision needs to be correct, explainable, and auditable.
The podcast explains the philosophy behind Rippletide's decision infrastructure: agents should not execute sensitive actions on LLM probability alone. Each episode maps to the need for deterministic validation, policy enforcement, and causal audit trails before agent actions reach production systems.
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.