Context Engineering: Designing the data that feeds the AI

About this episode

Context Engineering: Designing the data that feeds the AI is a deep-dive podcast series exploring the architectural control plane that powers modern agentic AI systems. Moving far beyond basic prompt engineering, this show untangles how data engineers and AI developers design, structure, and govern the runtime payloads that models consume to execute reliable, real-world workflows.If this podcast were created directly from your notebook's sources, its episode or series description would highlight the following core themes:

• The Shift Beyond RAG: While Retrieval-Augmented Generation (RAG) solved basic grounding problems, mature AI systems require much more. Listeners will explore why retrieval alone is insufficient for autonomous agents that must coordinate APIs, track conversation history, preserve user preferences, and execute multi-step decisions safely.
• The Physics of LLM Working Memory: The show tackles the hard physical and economic constraints of language models—including token window limitations, latency, and the quadratic cost of context bloat. Episodes will break down the infamous "Lost in the Middle" phenomenon, detailing how models suffer severe performance drops and fail to retrieve critical information when it is buried in the center of long prompts.
• The Context Stack Framework: A guide through the five operational layers that define production-grade context: Retrieval (grounding), Memory (continuity), Tools (live APIs), Orchestration (handoffs), and Governance (access control and cost tracing).
• Cutting-Edge Implementations: Real-world architectural blueprints are put under the microscope. The show reviews frameworks like Cisco’s HYVE (Hybrid Views), which uses request-scoped SQL datastores to dynamically generate space-saving columnar and row-oriented views, alongside AIGNE's Agentic File System (AFS), which treats memory, tools, and human-in-the-loop overrides as structured files mounted onto a virtual file system.
• A New Engineering Discipline: Why prompt design is ultimately downstream of context design. The series details why the future of AI economics and reliability depends not on the largest models, but on the teams that engineer the highest-signal, lowest-noise data pipelines.

This podcast serves as an essential guide for any developer, data architect, or tech leader looking to transition their AI applications from fragile, prompt-padded prototypes into robust, governed, and highly efficient digital collaborators.