As models improve, they bring surprising challenges for those building apps and harnesses. How context engineering has changed this year, and where it’s headed.
Context Engineering | Meetup
"Context engineering is the delicate art and science of filling the context window with just the right information for the next step" - Andrej KarpathyLearn about tools, frameworks, and code that is in service of context engineering from practitioners in this space.Dive into the set of strategies fo
Context Engineering Guide – Nextra
A Comprehensive Overview of Prompt Engineering
Luke Wroblewski on Twitter / X
If context engineering is the new hotness for engineering, context management is the new UI hotness/challenge for designers. pic.twitter.com/iXNTYnVRIi— Luke Wroblewski (@LukeW) July 10, 2025
The New Skill in AI is Not Prompting, It's Context Engineering
Context Engineering is the new skill in AI. It is about providing the right information and tools, in the right format, at the right time.
Working with Contexts
The following article comes from two blog posts by Drew Breunig: “How Long Contexts Fail” and “How to Fix Your Contexts.”Managing Your Context is the Key
Context Engineering
Context engineering strategies for AI agents: write, select, compress, and isolate context to optimize performance and manage long-running tasks.
Context Constitution
Today we are releasing the Context Constitution: a set of principles governing how AI agents manage context to learn from experience.
Roadmap - Model Context Protocol
Our plans for evolving Model Context Protocol
Context engineering is sleeping on the humble hyperlink
The dream of hypermedia can come to fruition in a world where clients can not only parse, but navigate with intent.
yzfly/awesome-context-engineering
A curated collection of resources, papers, tools, and best practices for Context Engineering in AI agents and Large Language Models (LLMs).
The Architect of Constraints: Why Context Engineering Is the Human Skill That Matters
“If I had an hour to solve a problem, I’d spend 55 minutes defining the problem and 5 minutes solving it.” — Albert Einstein
Model Leaderboard | Letta
Context-Bench measures an agent's ability to perform context engineering with:
Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models
Large language model (LLM) applications such as agents and domain-specific reasoning increasingly rely on context adaptation: modifying inputs with instructions, strategies, or evidence, rather than weight updates. Prior approaches improve usability but often suffer from brevity bias, which drops domain insights for concise summaries, and from context collapse, where iterative rewriting erodes details over time. We introduce ACE (Agentic Context Engineering), a framework that treats contexts as evolving playbooks that accumulate, refine, and organize strategies through a modular process of generation, reflection, and curation. ACE prevents collapse with structured, incremental updates that preserve detailed knowledge and scale with long-context models. Across agent and domain-specific benchmarks, ACE optimizes contexts both offline (e.g., system prompts) and online (e.g., agent memory), consistently outperforming strong baselines: +10.6% on agents and +8.6% on finance, while significantly reducing adaptation latency and rollout cost. Notably, ACE could adapt effectively without labeled supervision and instead by leveraging natural execution feedback. On the AppWorld leaderboard, ACE matches the top-ranked production-level agent on the overall average and surpasses it on the harder test-challenge split, despite using a smaller open-source model. These results show that comprehensive, evolving contexts enable scalable, efficient, and self-improving LLM systems with low overhead.
The Myth of The Infrastructure Phase
We are not in an infrastructure phase, but rather in another turn of the apps-infrastructure cycle. First, apps inspire infrastructure. Then that infrastructure enables new apps…
Effective context engineering for AI agents
Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.
The Context Outside the Code
From the Zed Blog: How you can quickly add external context to language model conversations with Zed extensions built using the new Model Context Protocol.
