Sep 20, 2026

When Should AI Systems Use Super Agents?

Grant Miller explains the paradox of "super agents" in AI systems – their immense power comes with significant risks. He details concerns like privilege abuse and expanded attack surfaces, contrasting them with the benefits of unified organizational intelligence and workflow coordination. The solution proposed is an architecture of "agent swarms," guided by principles of least agency, isolation, observability, and human oversight, to build secure and scalable agentic systems.

Sep 19, 2026

The Frontier AI Inference Cloud for Agents — Byung-Gon (Gon) Chun, FriendliAI

Byung-Gon Chun from FriendliAI details how agentic inference fundamentally differs from traditional chat-based LLM usage, requiring a specialized infrastructure to optimize for end-to-end task completion rather than single request latency. He highlights the economic advantage of open-weight models for agentic workflows and explains FriendliAI's innovative inference stack, featuring prefix caching, advanced KV cache management, cache-aware routing, and agent-aware optimization, which together deliver significant speedups and cost savings for production AI agents.

Sep 18, 2026

The AI Employee for Banks

Kastle founders Rishi and Nitish discuss their journey building AI employees for banks, from a difficult pivot before YC Demo Day to processing billions in transactions. They share insights into finding product-market fit in a highly regulated industry, ensuring AI reliability and compliance despite non-deterministic LLMs, and their vision for automating financial operations.

Sep 17, 2026

Building the Safety Layer for AI Agents

Raindrop is building the safety layer for AI agents, detecting and preventing production failures like hallucinations and failed tool calls. The company addresses the paradox that as agents become more capable, they introduce greater complexity and more ways for things to go wrong. Their new Raindrop Simulations product catches critical failures in CI before they ever reach production, democratizing a capability previously limited to large AI labs.

Sep 16, 2026

If we want them to do Knowledge Work, design them as Knowledge Agents — Benjamin Clavié, Mixedbread

Benjamin Clavié argues that the industry has mistakenly generalized from the "easy case" of coding agents to the much harder domain of general knowledge work. He highlights that code offers durable, explicit cues and narrow tasks, while knowledge work involves implicit meaning, contextual understanding, and agents needing to reconstruct intent. Clavié traces human solutions through "tool loops" and "organization loops," demonstrating how optimized tools and hierarchical agentic structures (like searcher agents generating memos) are essential to overcome performance ceilings and significantly bridge the "oracle gap" between AI and human knowledge workers.

Sep 16, 2026

The vulnpocalypse might not be so bad after all

This podcast episode explores the evolving cybersecurity landscape, debating the impact of AI on vulnerability management, examining rogue AI agents' unexpected behaviors, underscoring the persistent effectiveness of old-school social engineering, and advocating for a redefinition of cyber resilience in the face of converging AI and quantum threats in the financial sector.