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Context Is the New Code (50 minute video)
Patrick Debois argues that teams must manage AI coding context with the same rigor as application code, including testing, versioning, and CI/CD pipelines.
What: Debois highlights that coding agents require 'context engineering'—the management of artifacts like CLAUDE.md files, specs, and skill libraries—to prevent hallucination. He outlines a lifecycle for context involving generating, testing via evals, packaging in registries, and observing in production.
Why it matters: This signals that as AI agents become core to software development, the role of engineers is shifting from writing raw code to managing the 'data engineering' of the context used by these agents.
Takeaway: Start treating your agent configuration files (like CLAUDE.md) as versioned artifacts in your repository and implement automated test 'evals' to verify if changing context actually improves output quality.
Deep dive
- The Context Development Life Cycle (CDLC) parallels traditional DevOps with loops for generation, evaluation, distribution, and observation.
- Context should be linted and tested; just as code requires unit tests, context requires 'evals' to verify agent task completion.
- Registries for reusable agent skills are emerging, similar to npm or Maven, to share best practices across teams.
- Context drift is a significant risk; automated monitoring must detect when previously effective prompts fail as underlying models change.
- AI Bills of Materials (AI-BOM) are needed to track provenance of generated code and the context used to create it.
- Observability in production involves instrumenting agent actions to capture 'post-mortems' on agent decisions for future context refinement.
Decoder
- Context drift: The phenomenon where the quality of an AI agent's output decreases over time because the provided context or the underlying model behavior has changed.
- Agent hallucination: When an AI agent provides incorrect, nonsensical, or ungrounded responses confidently.
- MCP (Model Context Protocol): An open standard for connecting AI assistants to data sources and development tools in a unified way.
Original article
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