This article examines how Claude Code constructs its system prompts by analyzing recently leaked source code. It highlights the dynamic nature of these prompts, which are built from various components based on conditional statements. The author also outlines additional elements that contribute to context, like tool definitions and conversation history.

This article discusses the importance of context engineering in AI coding, emphasizing how it differs from traditional prompt engineering. It explores how effective context can enhance AI's performance within teams and outlines strategies for creating better workflows.

The article discusses how Dash evolved from a basic search system to an agentic AI by implementing context engineering. It highlights strategies like limiting tool definitions, filtering relevant context, and introducing specialized agents to improve decision-making and performance.

This article explores advanced techniques in context engineering for AI agents, focusing on issues like context rot and pollution. It shares insights from industry experts on optimizing agent performance through context management, toolset reduction, and effective communication strategies among multi-agent systems.

This article argues that data teams should transition to context engineering, integrating data governance, engineering, and science to create reliable knowledge sources for AI agents. It highlights the need for a structured context stack to ensure accurate answers and effective performance from these agents.

This article discusses the need for product explainability in the age of AI, emphasizing that clear product information is crucial for effective customer interactions. It outlines how builder PMs can create a structured approach to ensure that product knowledge is accurate and accessible.

This article discusses the challenges of managing context in AI agent development, emphasizing the need for a structured approach called context engineering. It introduces the Google Agent Development Kit (ADK), which separates context storage from presentation and optimizes how context is compiled and utilized during agent interactions.

This article presents a collection of skills focused on context engineering for AI agents. It covers the principles of managing context, designing memory systems, and optimizing agent operations. The skills are platform-agnostic and include practical examples for implementation.

This article explores "Latent Space Engineering," a method for crafting prompts that influence how AI models respond by shaping their mindset. It emphasizes the importance of managing the agent's emotional state to enhance output quality, using techniques like competitive framing and style transfer.

This article discusses how Whatnot implemented an AI-powered Slack bot to streamline data inquiries for their data scientists. It highlights key lessons learned about balancing flexibility and trustworthiness, the importance of context engineering, and the ongoing role of data scientists in this evolving landscape.

This article outlines the development of the Azure SRE Agent, focusing on the importance of context engineering in improving reliability and efficiency. It discusses the transition from numerous specialized tools to a few broad tools and generalist agents, highlighting key insights gained throughout the process.

This article discusses the ease of creating LLM agents using the OpenAI API. It emphasizes hands-on experience with coding agents, explores context management, and critiques the reliance on complex frameworks like MCP.

This article outlines the development of a deep research agent that leverages AI to enhance information gathering and synthesis. It discusses the challenges faced in building an effective agent harness, the importance of context management, and the evolution of models and tools to improve research capabilities.

This article explains how Optimizely Opal differs from typical AI tools by acting as an orchestration platform for marketing teams. It emphasizes Opal's context engineering, specialized agents, and autonomous workflows, which enhance its ability to manage tasks and integrate with existing marketing tech stacks.

Wix improved its AI scheduling assistant by shifting from prompt engineering to context engineering. This change reduced costs by 23 times and increased speed by 46% by preprocessing data before it reached the AI model.

The article discusses the limitations of single-agent runs in coding and proposes using parallel agents to explore multiple solutions simultaneously. By comparing results from different agents, the author demonstrates how this approach can lead to better problem-solving and more reliable outcomes.

This article offers insights on using Claude Code 2.0, detailing the author's journey with various coding agents and how to maximize their potential. It covers features, workflow tips, and the importance of context engineering for better results.

The Context Window Architecture (CWA) is proposed as a disciplined framework for structuring prompts in large language models (LLMs), addressing their limitations such as statelessness and cognitive fallibility. By organizing context into 11 distinct layers, CWA aims to enhance prompt engineering, leading to more reliable and maintainable AI interactions. Feedback and collaboration on this concept are encouraged to refine its implementation in real-world scenarios.

The Manus project emphasizes the importance of context engineering for AI agents, highlighting lessons learned from building their agent framework. Key practices include optimizing KV-cache usage, avoiding dynamic tool modification during iterations, and utilizing the file system for efficient context management to maintain performance and reduce costs. The article shares insights and principles aimed at helping others develop effective AI agents more rapidly.

Context engineering is crucial for agents utilizing large language models (LLMs) to effectively manage their limited context windows. It involves strategies such as writing, selecting, compressing, and isolating context to ensure agents can perform tasks efficiently without overwhelming their processing capabilities. The article discusses common challenges and approaches in context management for long-running tasks and tool interactions.

Redis Released is a free all-day event in San Francisco focused on building and scaling AI applications using Redis technology. Attendees can expect hands-on workshops, expert panels discussing context engineering and agent memory, and practical guidance on deploying AI solutions, with insights from industry leaders and live demos of new Redis features.

The article discusses the emerging importance of context engineering as a pivotal skill for the future, particularly in 2025. It emphasizes the need for individuals to understand and manipulate contextual information effectively in various fields, driven by advancements in technology and data analysis.

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AI integration in design systems presents challenges due to its probabilistic nature conflicting with the deterministic requirements of high-quality outputs. While AI excels in divergent tasks like brainstorming and research, it struggles with convergent tasks that demand precision and reliability, leading to mistrust among design practitioners. Effective use of AI requires a focus on context engineering and rigorous testing to ensure adherence to design system standards.

Agents require effective context management to perform tasks efficiently, which is achieved through context engineering strategies like writing, selecting, compressing, and isolating context. This article explores these strategies, highlighting their importance and how tools like LangGraph support them in managing context for long-running tasks and complex interactions.

The article discusses the challenges faced when developing the Notebook Agent for analytics in Hex, highlighting the differences between coding agents and analytics agents in context management. It emphasizes that while code can be summarized effectively, data requires direct observation to identify patterns, leading to the need for innovative context engineering strategies that allow AI agents to navigate complex data environments efficiently.

Chris Silvestri shares a three-phase workflow for improving AI-assisted writing by emphasizing the importance of context engineering over basic prompt engineering. By providing rich, strategic materials, writers can guide AI outputs to be more than just grammatically correct, transforming them into engaging and meaningful content.