#context engineering
Context Engineering: 9 AI articles covering context engineering news, analysis, and research
Articles
Context Engineering Inside the Harness: 4 Mechanisms That Beat Context Overflow and Goal Loss on Long-Horizon TasksNEWβ7
Long-horizon AI agents fail from context overflow and goal loss. Learn how compaction, memory, budgeting, and todo-state in the harness fix it.
Give Your Coding Agents a Memory You Ownβ9
Give your coding agents persistent memory you controlβboost efficiency, preserve project context across sessions, and overcome stateless AI limitations.
Google AI Introduces EnvHarness: A Programmable Layer Turning Static Agent Environments into Adaptive Training Worldsβ7
Google's EnvHarness adapts agent training environments dynamically, boosting performance by up to 9 points with 9.8% fewer steps.
AI Agents Donβt Need More Context β They Need Typed Contextβ10
Why AI agents fail from type confusion in promptsβlearn how typed context prevents instruction contamination and improves observability.
Loop Engineering for RAG: Small Loops Within Steps, Big Loopsβ9
Loop engineering in RAG: detecting failures, adjusting, and retrying steps to turn brittle one-shot pipelines into resilient systems that recover and deliver.
Prompt, Context, Loop: The Three Engineering Layers Every RAGβ10
Explore the three engineering layers of RAG systems: prompt, context, and loop. Learn how each layer works and why their evolution isn't a simple sequence.
Coding Agents Donβt Need Bigger Context Windows β They Need aβ10
Coding agents don't need bigger context windowsβthey need a compiler approach. This three-pass pipeline cuts prompt sizes by 69-74% in under 75ms.
Meet EverOS: An Open Source Markdown-First Agent Memory Runtimeβ7
Meet EverOS: an open-source, Markdown-first agent memory runtime with hybrid BM25 + vector retrieval and self-evolving skills for persistent, transparent AI mem...
Google Cloud Introduces Open Knowledge Format (OKF): Aβ8
Google Cloud launches OKF, a vendor-neutral Markdown spec standardizing curated context for AI agents to improve interoperability and reduce lock-in.