Tag
The article discusses the shift from prompt engineering to context engineering as the next critical AI hiring challenge, emphasizing the need for professionals who can design the environment and data context around AI models and agents.
An essay arguing that prompt-architecture is emerging as a new literary form designed for both LLMs and humans, treating text as an executable substrate rather than passive prose.
Twin is an open-source research project that aims to give AI systems continuous, cumulative understanding by correlating events and building reusable situation models, rather than reconstructing context from scratch each conversation. A demonstration shows Claude Sonnet 4.6 using Twin's MCP server to answer questions about a project without any custom memory or local files.
This post explains the layered relationship between prompt, context, harness, loop, and graph engineering, emphasizing that each layer builds on the previous one rather than replacing it, and how to identify which layer to debug.
Explains the differences between AGENTS.md, SKILL.md, and CLAUDE.md for AI coding agents, and offers practical guidance on using them without wasting tokens.
A reflective post arguing that swapping AI models rarely fixes poor output; instead, the quality of context provided to the model is the main driver, covering facts, examples, and corrections.
LangChain releases Deep Agents v0.7, a leaner agent harness that cuts base input tokens by 65% through removing the default system prompt, trimming tool descriptions, and making TodoListMiddleware opt-in, with comparable performance validated across multiple models.
Anthropic details new rules for context engineering with Claude 5, emphasizing letting the model use judgment over rigid rules, designing interfaces instead of giving examples, using progressive disclosure, and simplifying tool descriptions.
Anthropic shares lessons from removing 80% of the Claude Code system prompt for new Claude models, advising users to let the models use judgment over strict rules and to update context engineering practices.
A 28-page PDF introduces Context Engineering 2.0, reframing human-AI interactions beyond traditional prompt engineering.
This paper introduces ProofAgent-Harness, an open-source evaluation tool that measures context engineering quality for AI agents across seven criteria, validating it as a leading indicator of agent reliability.
Gergely Orosz tweets about a podcast with Dex Horthy on LLM context engineering, covering lessons like the dangers of shipping unread code and recognizing when an LLM session is trajectory-poisoned.
A tweet thread promoting the book 'Context Engineering for Multi-Agent Systems' by Denis Rothman, which teaches how to build transparent, modular multi-agent systems with context engineering, dual RAG, and other techniques.
Recommending Harrison Chase's blog post on multi-agent system design. The key insight is that multi-agent systems split into 'reading' and 'writing' tasks; reading is easy to parallelize while writing leads to conflicts. It also summarizes suitable and unsuitable scenarios and engineering prerequisites.
The article outlines four foundational elements of AI architecture—data quality, context engineering, governance, and human expertise—that IT leaders should prioritize to scale AI systems reliably as models evolve.
This blog post by SuJinyan6 examines the evolution of AI agents from simple LLM+tool use to context engineering and long-running harnesses, citing Anthropic's recent work and discussing how agent capability is now a system-level property involving multiple components.
A thread summarizing key Day 1 takeaways from the @aiDotEngineer World’s Fair, highlighting that trust is now an engineering discipline, context engineering has evolved beyond RAG, and the back office of AI (observability, authority, governance) is emerging as a new focus.
The article discusses in a Q&A format how to enable AI Agents to perform system design and coding in a multi-microservice scenario, focusing on practical experiences with context quality (via monorepo, layered documentation) and validation loops (via contract testing, mock servers).
This paper defines agentic abstention, the problem of deciding when an LLM agent should stop acting under uncertainty, and evaluates it across web shopping, terminal environments, and question answering. It introduces convolve, a context engineering method that improves timely abstention without updating model parameters.
A detailed guide on building a production-grade agent harness for multi-agent LLM systems, covering components like orchestrator, subagents, skills, backend state management, and context engineering.