Love this. Context engineering will become similar to industrial and process engineering, in the sense that those latter disciplines emerged as there was a requisite and desire to optimise and get the most out of the machines that had taken over mechanical labour. Now, context engineering is emerging similarly because we want to optimise and get the most out of the machines that are taking over coding — the mechanical labour part of software engineering.
Thanks Christian! :) That's a good analogy! You've captured something really important about why this feels like a genuine discipline emerging rather than just rebranded prompt writing. Industrial engineering didn't just happen because machines existed - it emerged when systems became complex enough that intuition and trial-and-error weren't sufficient anymore.
You needed systematic measurement, process optimization, and rigorous methodology to get reliable outcomes from complex mechanical systems.
We're hitting that same inflection point with AI systems. Early on, clever prompting was enough for demos and simple use cases. But now we're building production systems where an AI agent might retrieve from multiple knowledge bases, coordinate with several tools, maintain conversation state, and handle edge cases - all while staying within token budgets and cost constraints.
I think we will start to see frameworks/best practices/companies that specialize in helping generate and format your business data into context for models (it's already happening). This Cognition article wasn't about context engineering per se but it discusses different strategies for generating context (like a small model generating an md file of suggested changes) that might be interesting: https://cognition.ai/blog/dont-build-multi-agents#principles-of-context-engineering
Love this. Context engineering will become similar to industrial and process engineering, in the sense that those latter disciplines emerged as there was a requisite and desire to optimise and get the most out of the machines that had taken over mechanical labour. Now, context engineering is emerging similarly because we want to optimise and get the most out of the machines that are taking over coding — the mechanical labour part of software engineering.
Thanks Christian! :) That's a good analogy! You've captured something really important about why this feels like a genuine discipline emerging rather than just rebranded prompt writing. Industrial engineering didn't just happen because machines existed - it emerged when systems became complex enough that intuition and trial-and-error weren't sufficient anymore.
You needed systematic measurement, process optimization, and rigorous methodology to get reliable outcomes from complex mechanical systems.
We're hitting that same inflection point with AI systems. Early on, clever prompting was enough for demos and simple use cases. But now we're building production systems where an AI agent might retrieve from multiple knowledge bases, coordinate with several tools, maintain conversation state, and handle edge cases - all while staying within token budgets and cost constraints.
I think we will start to see frameworks/best practices/companies that specialize in helping generate and format your business data into context for models (it's already happening). This Cognition article wasn't about context engineering per se but it discusses different strategies for generating context (like a small model generating an md file of suggested changes) that might be interesting: https://cognition.ai/blog/dont-build-multi-agents#principles-of-context-engineering