3 years ago I gave a talk at the first @aiDotEngineer conference on "Advanced RAG" techniques in order to work around the limitations of naive RAG.
It's insane how much the world has changed since then, and the world has evolved into standardized, higher-level abstractions around agent harnesses and context.
Some general patterns:
Retrieval complexity can be encoded at the agent layer. This means that you can give relatively simple but performant search tools to an agent (e.g. really fast bm25, vector search), and let the agent reasoning enter the right queries to find the right results.
To some extent this is still evolving, but I do think we will increasingly care less about "hacking" the context window and more about deciding what business context is relevant in the first place.
The way we build agents has fundamentally changed from defining code, to defining runbooks, to defining goals.
Big congrats to @swyx and the entire AI Engineer team for continuing to put out awesome conferences every year.