Agentic Context Engineering: Comprehensive Reference Specification
The unified specification synthesizing research from Google ADK, Anthropic, Manus, and Stanford into a single framework - why agents fail from memory architecture problems not model stupidity, and the four-layer architecture that fixes it
Comprehensive reference specification for Agentic Context Engineering synthesizing research from Google ADK, Anthropic, Manus, and Stanford/SambaNova - covering the compiler thesis (context as computed view over richer state), the four-layer memory architecture (Working Context, Sessions, Memory, Artifacts), domain memory patterns, nine scaling principles, nine failure modes, the ACE self-improvement framework, multi-agent orchestration patterns, tool design principles, and the platform guardrails that prevent common architectural failures.
Also connected to
The stateless paradox: how conversational AI maintains the illusion of memory on protocols designed to forget everything
The distributed systems problem hiding inside every multi-agent AI system: how do agents share what they know without drowning in what they don't need?
The unified framework that production-grade agent platforms use to make context work at scale
How to architect context that scales across hundreds of agents without degradation
The unified specification synthesizing research from Google ADK, Anthropic, Manus, and Stanford into a single framework - why agents fail from memory architecture problems not model stupidity, and the four-layer architecture that fixes it