Rooting - The Podcast

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 managersPlanning patterns: hierarchical decomposition, reactive planning, plan repairRecursive Language Models: context folding that enables multi-day tasks without degradationProduction architectures: how Manus and Claude Code orchestrate complex agentsThe future: autonomous, collaborative, and metacognitive agentsThe future of AI is in intelligent architecture around them.This episode includes AI-generated content.

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 timeContext blindness: when the agent has information but can't use itContext hallucination: the danger of plausible inventionsMemory architecture: hot, warm, and cold memory for robust agentsProduction patterns: what Manus and Claude Code do behind the scenesIf you're building with AI, this episode will change your approach.This episode includes AI-generated content.

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.

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.

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

We go beyond prompt engineering to focus on context engineering, a systematic approach to building production-grade AI. By treating the agent's context as a structured, observable pipeline, we enable teams to create robust, cost-effective, and scalable AI systems that deliver real business value.

What separates a forgetful chatbot from an AI agent that feels truly smart?In this episode, Root unpacks the power of memory in AI—from basic sliding windows to advanced retrieval-augmented and memory-augmented strategies inspired by neuroscience.Discover how the right memory architecture enables personalization, learning, and adaptability in your agents.Whether you’re building customer support bots, coding copilots, or next-gen assistants, you’ll get practical examples, production tips, and a glimpse into the future of multi-modal, real-time memory in AI.Tune in and learn how to make every interaction count!

Dive into the evolving world of AI context windows with Rooting. This episode unpacks the surprising paradox of 'more memory' in AI models: exploring the immense benefits of larger context, the hidden limitations like reasoning degradation, and the cutting-edge engineering techniques—from positional encoding tricks to novel reasoning architectures—that are shaping how AI truly understands and remembers. Essential listening for solution architects, software developers, and data scientists navigating the complexities of large language models.

How much do artificial intelligence systems really resemble the human mind? In this episode of Rooting, The Podcast, host Root takes you on a deep dive into the emerging metaphor that’s reshaping our understanding of AI: the idea that modern large language models and multi-agent systems are designed with architectures that parallel human cognition.Explore how features like chat interfaces act as digital sensory systems, context windows mimic our short-term memory, and Retrieval-Augmented Generation (RAG) gives AI its own form of long-term recall. We’ll examine the practical implications of these analogies for architects, developers, and data scientists, and discuss how understanding these human-AI parallels can drive better system design, interface engineering, and collaboration between people and intelligent machines.

Welcome to Rooting, a podcast about AI, made by AI with a human touch!Meet Root, your AI host with a mission: cutting through the noise and hype to help technical leaders make sense of modern AI. In this special intro episode, Root pulls back the curtain and shares how an AI, trained on the latest research and guided by top human experts, is ready to become your go-to guide in the fast-changing world of artificial intelligence. Find out what makes this hybrid human-AI approach unique, why you’ll hear both clarity and candor, and how Rooting plans to take you from buzzwords to breakthroughs—one episode at a time. Tune in, because when it comes to AI, sometimes the smartest guide really is the one who never stops learning.