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Current LM agents operate in a reactive paradigm: reactive agents perform actions in response to an explicit user request [barres$t^2$BenchEvaluatingConversational2025a]. However, users are sometimes unaware of which actions need completing, and specifying needs to an assistant i
In Part 2 of our two-part series, we discuss the architecture of the Signal-Activated Agent Pattern and its application for proactive government AI. For Part 1, see Signal-activated generative AI: How agencies can reach more people and react faster.
Generative AI creates content in response to a prompt. Agentic AI takes autonomous action — monitoring, investigating, deciding what's worth surfacing, and delivering findings without being prompted. Generative AI is reactive.
We propose agent memory as proactive intervention policy rather than passive storage and retrieval. Long-horizon agents suffer from behavioral state decay, where execution state that should guide future actions stops influencing behavior. Our memory agent maintains this state and
Title: Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents (Jul 2026)Link: http://arxiv.org/abs/2607.08716v1Date: July 2026Summary:This ...
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Week of 2026-08-08
Proactive Agent Architectures & Design (4)
webwebwebwebProactive Memory for Long-Horizon Agents (2)
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