LinkedIn·Wednesday, 19 August 2026·7d ago

Most companies think their AI adoption problem is a usage problem while it isn't. People are using the tools. The real problem is that the…

ML6

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Most companies think their AI adoption problem is a usage problem while it isn't. People are using the tools. The real problem is that the knowledge they build up while using them disappears. An engineer spends three weeks figuring out exactly how to get an AI agent to work well on a specific codebase; which context to feed it, which workflows actually hold up, what not to let it touch. That knowledge is genuinely valuable. And then the chat session ends, the engineer moves to another project, and all of it evaporates. The next person starts from zero. We call this context evaporation, and in our experience it's the actual bottleneck behind uneven AI adoption, not tooling or training. In our latest blog, Senior Data Engineer Georges Lorré lays out the fix: 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗮𝘀 𝗰𝗼𝗱𝗲. Instead of letting AI knowledge live in Slack threads and chat history, it goes into versioned markdown files that sit in the repo alongside the code itself — an AGENTS.md that tells any agent what a project is and what it should never touch, reusable SKILL.md workflows for recurring tasks, AI review instructions that evolve with the codebase, and the specs and ADRs most teams already have but haven't pointed an agent at yet. The blog also gets into the harder, more structural question: how do you make this scale across an entire organization rather than living project by project? That's where our internal tool Nimbus comes in - a "company floor" of standards that every new project inherits by default, kept current through a mechanism called reinit, and pushed toward staying fresh (rather than going stale like most documentation) through something we're calling context harvesting. It's a genuinely practical read for any engineering org that has noticed its AI adoption feels uneven - strong in pockets, invisible everywhere else - and wants to understand why. Read the full article: https://lnkd.in/esdvGKkJ #AIEngineering #AIAdoption #ContextEngineering #AgenticAI #AICoding