ooh this is a dense and useful paper. i like that they took the time to apply it to a bunch of case studies and its all in 30 pages.

i think basically all of them involve reducing the "agency" of the agents though - which is a fine tradeoff - but i think one should be aware that the Big Model folks dont try to engineer any of these and just collect data to keep reducing injection risk. the tradeoff of capability maxxing vs efficiency/security often tends to be won by the capabilitymaxxers in terms of product adoption/marketing.

eg the SWE Agent case study recommends Dual LLM with strict data formatting - would like to see this benchmarked in terms of how much of a perfomance an agent like this would be, perhaps doable by forking openai codex and implementing the dual llm.