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This paper introduces the concept of memory depth for long-running language agents, distinguishing it from retrieval-based memory access, and proposes EVAF, a selective parametric consolidation mechanism using surprise- and valence-gated LoRA updates. Experiments across multiple models show EVAF improves goal persistence after context unload with minimal parametric writes.
A recorded discussion about effectively running autonomous long-running coding agents, including insights on goal setting, model selection, and best practices, made freely available.
Agent libOS introduces a library-OS-inspired runtime substrate for LLM agents, treating agents as schedulable processes with explicit capabilities, lifecycle management, audit records, and human approval queues. The design shifts the trust boundary from tool dispatch to runtime primitives, enabling long-running agents to be scheduled, authorized, resumed, and audited safely.
A tutorial from Google on building long-running AI agents that can pause for days, survive restarts, and resume without losing context using the Agent Development Kit (ADK), with code and step-by-step guidance for enterprise workflows like new hire onboarding.
A practitioner shares real-world failure modes of context window management strategies (summarization, RAG, truncation) in AI agents running continuously for 6+ hours, noting that each method degrades decision quality in ways that only become apparent at extended runtime.
Anthropic's applied AI team shared in a workshop how to build agents that can run for hours, with the core being context management, planning and self-validation, as well as the co-evolution of models and supporting tools.
ActiveGraph introduces a continuity layer for long-running AI agents, building on BabyAGI's concept of persistent state to maintain coherent, evolving models of beliefs, dependencies, and actions over time.
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.
The article discusses how traditional observability methods fail as AI agents run for days and hundreds of steps, proposes trajectory-based Observability-Driven Development (ODD) to monitor and debug long-running agents, and introduces related practices on the Honeycomb 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.