Context Engineering is the discipline of deciding what an AI system sees at inference time — selecting, compressing, ordering, and refreshing the contents of the context window — so the model has exactly what it needs and little else.
It is broader than Prompt Engineering: it spans retrieval, Agent Memory, tool outputs, and history management, treating the window as a scarce budget to curate rather than fill. What you leave out matters as much as what you put in.
For builders it is the main lever on agent reliability and cost: irrelevant or contradictory context degrades reasoning and inflates token spend. The common failure is dumping everything available into the prompt, which buries the signal and triggers the lost-in-the-middle effect on Long Context inputs.
Topic: LLMs Related: Long Context Prompt Engineering Agent Memory RAG Agents
Recent Updates
- 2026-09-07: Ask HN: How to Manage Skills Files for Claude Code and Coding Agents (breakingnewsofficial) · news.ycombinator.com — Anthropic · OpenAI · GitHub · Vercel · Astro mattpocock mcollina trailsofbit
- 2026-09-07: KVMem: Virtualizing Million-Token Agent Workspaces on a Consumer GPU (breakingnewsofficial) · arxiv.org — Qwen3.8-27B · Qwen3.6-27B
- 2026-09-07: Rhythms of Work: Multi-Scale Interpretation of Human Behavioral Traces for Workplace Agents (breakingnewsofficial) · arxiv.org
- 2026-09-07: Iris: Climbing to the Search Frontier (breakingnewsofficial) · arxiv.org
- 2026-09-07: Engrim: Local-first SQLite memory engine for cross-model AI coding environments (breakingnewsofficial) · github.com — Google · Anthropic · OpenAI Gemini 3.8 · Claude 3.7 Sonnet · GPT-4o
- 2026-09-07: Hidden state bridge enables 4B mobile model to match larger cloud model on ARC-AGI 3 (breakingnewsofficial) · qbitai.com — Mostik Alibaba · Zhipu · Anthropic · OpenAI Stanislav Smirnov Sasha Malysheva · Qwen-3.5 · Qwen 3 · GLM-5.2 · GPT-6 Astra Claude-3.5-Haiku
- 2026-09-08: Mercury 2.5: Production-Ready Diffusion LLM for Search, Voice, and Coding Agents (breakingnewsofficial) · inceptionlabs.ai — Inception NVIDIA · OpenAI · Google · Anthropic OpenCall Augment Code · Baseten · OpenRouter Shruti Koparkar Oliver Silverstein Mercury 2.5 · Mercury 2 Mercury Voice GPT-5.6 Luna (Low) · Gemini 3.5 Flash-Lite · Claude Haiku 4.5
- 2026-09-09: What the Window Does Not Contain: Auditing Provenance in a Document-Grounded Instability Benchmark (breakingnewsofficial) · arxiv.org
- 2026-09-09: Multi-turn LLM Degradation: How Assistant-Generated History Shapes Downstream Behavior (breakingnewsofficial) · arxiv.org
- 2026-09-09: When Agent Governance Helps (breakingnewsofficial) · arxiv.org
- 2026-09-09: Using AI agents to win consumer disputes: seven months, zero losses, twelve thousand dollars recovered (breakingnewsofficial) · sudomoin.com — Annie’s Costco Google UnitedHealth Kaiser Family Foundation · Reddit FedEx · Claude
- 2026-09-10: ConvMem: Hierarchical Convolution for Long-Context Reasoning in LLMs (breakingnewsofficial) · arxiv.org
- 2026-09-10: Beyond Surface Imitation: Contrastive Modeling for Multimodal In-Context Learning (breakingnewsofficial) · arxiv.org
- 2026-09-10: Kernel-Managed Shared Memory for Multi-Agent System Personalization (breakingnewsofficial) · arxiv.org — OpenAI · Meta · Alibaba · Mem0 · GPT-4o · Llama-3.1:8B · Qwen 2.5 7B
- 2026-09-10: Positional Task Conditioning for Scalable Defect Detection in Large Product Catalogs (breakingnewsofficial) · arxiv.org
- 2026-09-10: PRAGMA: Evaluating Personalized Guidance with Memory Alignment in Lifelong Conversations (breakingnewsofficial) · arxiv.org
- 2026-09-10: From Fixed Keys to Readable Schemas: Small Language Models for Vehicle Agent Function Calls (breakingnewsofficial) · arxiv.org
- 2026-09-10: Multi-Agent Collaboration for Graph Reasoning with Permutation-Invariant Structural Signatures (breakingnewsofficial) · arxiv.org
- 2026-09-10: Subagents vs Agent Skills: Executing Reusable Knowledge for Long-Horizon Agentic Tasks (breakingnewsofficial) · arxiv.org
- 2026-09-10: Do LLMs Make More Mistakes If They Do Not Believe the Input Data? (breakingnewsofficial) · arxiv.org — Kimi K3
- 2026-09-10: Sizing RAM and vCPU for Local Language Models: Calculating Startup Infrastructure Requirements (breakingnewsofficial) · habr.com
- 2026-09-11: REVA: Reusable Evidence View Aggregation for Context-Efficient RAG Serving (breakingnewsofficial) · arxiv.org
- 2026-09-11: SWRouter: Similarity-Contractive Window Routing for Multi-Turn Large Language Model Conversations (breakingnewsofficial) · arxiv.org
- 2026-09-11: FlexComp: One Model for Every Ratio in Context Compression (breakingnewsofficial) · arxiv.org
- 2026-09-11: Larger Context Window, Fewer Overcorrections: Optimizing Prompts and Batching for Minimal-Edit Grammatical Error Correction (breakingnewsofficial) · arxiv.org — Google Staruch · Gemini-3.1-Pro
FAQ
What is Context Engineering?
Context Engineering is the discipline of selecting, compressing, ordering, and refreshing the information an AI system sees at inference time. GROUNDING tracks context windows, memory, tools, retrieval, and prompt architecture.
Which topic does Context Engineering belong to?
On the GROUNDING radar, Context Engineering is grouped under the LLMs topic.
Which concepts are related to Context Engineering?
Related concepts tracked by the radar include Long Context, Prompt Engineering, Agent Memory, RAG, Agents.