#JAZ

A minimal prompt-and-loop design outperforming heavyweight memory infrastructure suggests agent scaffolding is largely redundant overhead, which changes how teams should budget for agent architectures. Developers building long-horizon agents can study the JAZ repo and test whether dynamic scoping replaces their vector-store and meta-optimisation layers. Benchmarks come with explicit dollar costs, making direct comparison with Letta (MemGPT) and ACE easy to replicate.

Gorden Sun@Gorden_SunSource

Memory and self-improvement are usually bolted on as separate subsystems; JAZ shows both can just be code the agent writes inside its own loop, with hooks enforcing constraints and monitoring. Builders designing agent harnesses can use this to cut complexity and cost instead of adding memory layers. The paper is arXiv:2609.26891, with an explore guide at academy.dair.ai.

elvis@omarsar0Source