Surely Jim is also using an agent. Jim can't be worth having a quick sync with if he's not using his own agent! So then why are these two agents emailing each other back and forth using bizarre, terse office jargon?

It's an integration adventure. This is why much AI is failing in the enterprise. MS Copilot is moderately interesting for data in MS Office, but forget about it accessing 90% of your data that's in other systems.

I guess "context engineering" is a more encompassing term than "prompt engineering", but at the end of the day it's the same thing - choosing the best LLM input (whether you call it context or a prompt) to elicit the response you are hoping for.

The concept of prompting - asking an Oracle a question - was always a bit limited since it means you're really leaning on the LLM itself - the trained weights - to provide all the context you didn't explicitly mention in the prompt, and relying on the LLM to be able to generate coherently based on the sliced and blended mix of StackOverflow and Reddit/etc it was trained on. If you are using an LLM for code generation then obviously you can expect a better result if you feed it the API docs you want it to use, your code base, your project documents, etc, etc (i.e "context engineering").

Another term that has recently been added to the LLM lexicon is "context rot", which is quite a useful concept. When you use the LLM to generate, it's output is of course appended to the initial input, and over extended bouts of attempted reasoning, with backtracking etc, the clarity of the context is going to suffer ("rot") and eventually the LLM will start to fail in GIGO fashion (garbage-in => garbage-out). Your best recourse at this point is to clear the context and start over.

Cool, but wait another year or two and context engineering will be obsolete as well. It still feels like tinkering with the machine, which is what AI is (supposed to be) moving us away from.

The dudes who ran the Oracle of Delphi must have had this problem too.

We do enough "context engineering" we'll be feeding these companies the training data they need for the AI to build it's own context.

If I need to do all this work (gather data, organize it, prepare it, etc), there are other AI solutions I might decide to use instead of an LLM.

i think context engineering as described is somewhat a subset of ‘environment engineering.’ the gold-standard is when an outcome reached with tools can be verified as correct and hillclimbed with RL. most of the engineering effort is from building the environment and verifier while the nuts and bolts of grpo/ppo training and open-weight tool-using models are commodities.

This article reads as if the author pulled this idea straight from the MGS2's ending regarding giving and creating context..

Of course the best prompts automatically included providing the best (not necessarily most) context to extract the right output.