AI Agents in Production (part 2)

AI Agents in Production (part 2)

From reactive chatbots to agents that plan, delegate, and think across extended timescales. We explore Deep Agents and the Recursive Language Models paradigm that's redefining AI in 2026. What's the difference between a chatbot and an agent? A chatbot responds, an agent acts. In this episode, we go deep on: * Deep Agents: systems that plan and delegate like project managers * Planning patterns: hierarchical decomposition, reactive planning, plan repair * Recursive Language Models: context folding that enables multi-day tasks without degradation * Production architectures: how Manus and Claude Code orchestrate complex agents * The future: autonomous, collaborative, and metacognitive agents The future of AI is in intelligent architecture around them. This episode includes AI-generated content.

AI Agents in Production (part 1)

Why does 60% of an AI agent's success have nothing to do with the model? In this episode, we explore context engineering: the hidden discipline that separates impressive demos from systems that actually work in production. Ever built an AI agent that was brilliant in testing but failed miserably in production? The problem isn't the model. It's the context. In this episode, we cover: * Context rot: why agents "forget" and degrade over time * Context blindness: when the agent has information but can't use it * Context hallucination: the danger of plausible inventions * Memory architecture: hot, warm, and cold memory for robust agents * Production patterns: what Manus and Claude Code do behind the scenes If you're building with AI, this episode will change your approach. This episode includes AI-generated content.

Context Rot

This episode of Rooting exposes the hidden enemy of modern agent architectures: context rot. We explore why long context windows aren’t a silver bullet, how attention budgets degrade over time, and the four ways rot shows up in real systems—poisoning, distraction, confusion, and clash. Listeners learn why million-token prompts still fail, why observability must extend into the model’s working memory, and how emerging strategies such as isolation, selective retrieval, compression, external memory, semantic chunking, and standards such as MCP are reshaping how robust agents are built. This is a practical, technical deep-dive for architects and developers who want their AI systems to survive contact with reality. This episode includes AI-generated content.

Formal Logic - pt. 02

We are moving past the limitations of probabilistic AI by embracing formal logic and verification. This approach allows us to mathematically prove that our AI systems will behave correctly under specific conditions, providing a new level of trust and reliability essential for critical applications in finance, healthcare, and beyond. This is the second and last part of the episode. This episode includes AI-generated content.

Formal Logic - pt. 01

We are moving past the limitations of probabilistic AI by embracing formal logic and verification. This approach allows us to mathematically prove that our AI systems will behave correctly under specific conditions, providing a new level of trust and reliability essential for critical applications in finance, healthcare, and beyond. This is the first part of a two-part podcast This episode includes AI-generated content.