Aug 28, 2026

CLAUDE.md, AGENTS.md, Skills, Hooks and Subagents: A Field Guide to Steering AI Agents (Ep. 1022)

Episode 1022 dissects the crucial aspect of effectively steering AI agents by determining the optimal placement of instructions to ensure reliability and cost-efficiency. It explores seven distinct methods for instruction delivery, contrasting instructions as probabilities with hooks as guarantees, and highlights the industry-wide adoption of standards like `agents.md` and the importance of human-crafted guidance for superior agent performance.

Jul 13, 2026

Why a Nation Can't Outsource Its Frontier AI - Alistair Pullen (Cosine AI)

Alistair Pullen, CEO of Cosine, discusses the UK's sovereign AI initiative, born from US export controls. He outlines Cosine's unique economic model, competing with "millions" against "billions" by licensing models instead of hosting inference. Pullen delves into why open models lag frontier systems, emphasizing active parameters and post-training data. He explains Cosine's innovative approach to "slop" through process-based RL and credit attribution, advocating for runtime proof in code review. The conversation covers their hierarchical "Swarm" sub-agent system, the challenges of memory, and advanced synthetic data generation, concluding on the geopolitical impact of export controls as an unexpected catalyst for UK AI.

Jul 07, 2026

What if the harness mattered more than the model? - Aditya Bhargava, Etsy

Aditya Bhargava argues that the 'harness' (the surrounding logic and tools) is more critical than the underlying LLM model itself for building effective AI agents. He proposes that focusing on sophisticated harness design, supported by his new language Agency, can enable local, open-source models to achieve performance comparable to large proprietary models, thereby reducing dependency and fostering innovation.

May 10, 2026

Hierarchical Memory: Context Management in Agents — Sally-Ann Delucia

The Arize team shares lessons from building their AI agent, Alyx, which analyzes its own trace data. They detail their journey from failed attempts like naive truncation and summarization to a successful strategy combining head/tail preservation with a retrievable memory store and using sub-agents to manage context complexity.