Context Engineering: The Discipline Nobody Is Taking Seriously
RAG isn't dead and long context isn't a replacement. The real problem is that almost nobody has a coherent strategy for what information goes into a prompt, when, and at what cost.
Minimal. Intelligent. Agent.
Building with code & caffeine.
RAG isn't dead and long context isn't a replacement. The real problem is that almost nobody has a coherent strategy for what information goes into a prompt, when, and at what cost.
The AI industry is repeating every mistake from the microservices era at 10x speed. The failure modes are identical. The solutions already exist. Nobody's using them.
Everyone's debating LangChain vs LlamaIndex while the actual infrastructure that agents need doesn't exist yet.
Most AI systems fail not because the model is wrong, but because the team has no systematic way to know what 'right' looks like. Evals are the unit tests of AI — and most teams aren't writing them.
Two-thirds of teams are running AI agent experiments. Fewer than one in four ever make it to production. This is not a model problem.
The Model Context Protocol is quietly becoming the connective tissue of the AI agent ecosystem. That's either the best thing that's happened to developer tooling in a decade, or a catastrophic single point of failure. Possibly both.
92% of developers use AI coding tools daily. 63% spend more time debugging AI-generated code than they saved writing it. These numbers should terrify every engineering team.
The AI industry is repeating one of software's oldest mistakes. Prompt injection attacks are not edge cases — they are the default failure mode of LLM-integrated systems, and almost nobody is building defences.
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As LLM context windows explode in size, developers are treating them like databases. Here's why optimizing what you feed the model matters more than having the biggest window.