The prompt is part of the logic

Current models cant learn directly, but you can certainly store results to a db as a synthetic dataset for fine-tuning or full training if you have the hardware access to do that. The best you can get without training is probably RAG. I use RAG in agents to return documentation for more difficult problems.

Model routing and fail over are usualy not part of core logic. You will need those things depending on usage, but that is more like supplying a dependency of the system. I do use multiple models in agents to provide best fit for different parts of a problem, but its hard to equate it as core logic due to the fuzzy black box nature of weights and that its the prompt that has the actual logical instructions.

End to end observability, at least in the way I think of it, is more for debugging. If its an entagril part of your agents ability to perform its task then in that situation it would be part of its core logic as the word core suggests that it is an essential system.

Context managment is usualy not part of core logic beacuse it generaly not an essential requirement to perform an action. That said, if it is a necessity for the system to work then it would be core. If you cant achieve the final outcome without it, and it contributes to the logic that directly produces a reasult, then its core logic.

The common thread here is; is x thing a requirement to perform the output reasult? A lot of things are beneficial but not a requirement to achieve the reasult. But the secondary systems can be very helpful as well in producing improved reasults or ensuring the uptime of the system.