The strongest work treats context handling as an engineering control. qMLX cuts repeated long-context prefill on one Mac Studio, while ContextOps checks payload structure before inference.
Agent teams can improve reliability at three concrete boundaries: preserving reusable inference state across turns, checking assembled context before each model call, and verifying files passed between agents.
Today’s strongest signal is operational: large language model (LLM) agents need hard gates for money, identity, and execution. Donobu, Kortex, and Aion show the spread across testing, local inference, and desktops.
Agent teams can add concrete operating controls at the places where failures become expensive: pre-call cost reservations for LLM requests, deterministic authorization before RAG chunks or tool calls reach a model, and…