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All digests tagged Context Engineering
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Context engineering is crucial for AI agents. Learn about context rot and four failure modes—Poisoning, Distraction, Confusion, and Clash—and how to improve agent reliability by deliberately pruning context.
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DDC is a methodology that uses AI agents to fail on real production incidents, systematically identifying and curating the minimum necessary knowledge context. This approach treats incident data as knowledge tests, moving beyond static runbooks to a 'living context.'
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AI is changing software development from manual coding to architectural orchestration. Learn the five concepts—Spec-Driven Development, Context Engineering, and Loop Engineering—needed to master AI agents and build robust systems.
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Bigger context windows won't save your AI agents. Learn why models lose information in the middle and master the four strategies of Context Engineering: Externalize, Select, Compress, and Isolate.
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AI agents are 'blind,' not stupid. The fix is context engineering at the data layer, requiring federation and logical views to unify data from sources like Kafka and Iceberg.
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Jev is a System 1 model that makes fast, structured decisions without generating text. Learn how to integrate it into agent harnesses for safety classification and model routing, significantly improving efficiency and reliability.
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The future of software development is moving from writing code to defining specs. The focus must shift to 'context engineering' and building robust 'software factories' to manage AI agents.
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AI agents need a 'context engine' to move beyond simple information retrieval. Learn how to fix the context gap, avoid the 'curated context trap,' and achieve massive token and time savings in your CI/CD pipelines.
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Learn how LinkedIn scaled AI agents to thousands of tools and playbooks using a Search/Get Schema/Execute meta-tool layer, enabling reliable, context-aware automation.
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Discover how modern software factories use AI agents, skills, and loops to achieve unprecedented development velocity. Learn why context engineering is the critical step before automation.
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Skills are transforming software development from revolving around code to intent. Experts argue that managing skill sprawl requires treating skills as code, focusing on context engineering over model size.
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Context engineering is now the main lever on what a coding agent can actually do. The three remaining constraints are human time, attention, and the context window.
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DeepWiki, a codebase documentation product for agents like Devin, evolved from orchestration-led v1 to an agentic v2. Learn about context engineering principles, scaling codebases, and building robust knowledge graphs.
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Context engineering is critical for LLM agents. The talk reveals that compaction techniques can be detrimental because they invalidate prompt caches, forcing full-price token usage. Full history retention often wins on recall.
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The web is no longer just a source of data; we can look at it as a source of context for knowledge work using AI agents. This leads to Context-as-a-Service (CaaS).
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AI coding tools rely on three parts: models (the brain), harnesses, and tools. The agent uses a feedback loop where the model generates an intention, which the harness executes.
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Context Engineering is the critical practice of structuring information passed to LLMs and AI agents. Learn how optimizing context, rather than just writing better prompts, improves reliability and reduces hallucinations in complex AI systems.
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AI agents are evolving from local 'Harnesses' to persistent, always-on services called 'Claws.' This shift requires adding initiative and external connectivity, fundamentally changing how we build developer tools.
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