long-running-agents

A detailed architectural guide for building long-running AI agents that handle changing user preferences over time by combining a vector store, graph DB, and temporal edges instead of overwriting data.

RecMem is a recurrence-based memory consolidation method for long-running LLM agents that reduces token consumption by up to 87% while improving accuracy, by only invoking LLMs when semantically similar interactions recur.

A new article outlines 5 agent design patterns for building long-running AI agents that can operate for days without interruption, leveraging Google's new Agent Platform.

Anthropic introduces a two-part solution using an initializer agent and a coding agent to enable the Claude Agent SDK to effectively handle long-running tasks across multiple context windows by maintaining a clean, incremental state.