AutoAgent: Evolving Cognition and Elastic Memory Orchestration for Adaptive Agents

• URL: http://arxiv.org/abs/2603.09716v1
• Date: Tue, 10 Mar 2026 14:23:49 GMT
• Title: AutoAgent: Evolving Cognition and Elastic Memory Orchestration for Adaptive Agents
• Abstract summary: AutoAgent is a self-evolving multi-agent framework built on evolving cognition, on-the-fly contextual decision-making, and elastic memory orchestration.<n>Each agent maintains structured prompt-level cognition over tools, self-capabilities, peer expertise, and task knowledge.<n>AutoAgent consistently improves task success, tool-use efficiency, and collaborative robustness over static and memory-augmented baselines.
• Score: 26.380991138110925
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Autonomous agent frameworks still struggle to reconcile long-term experiential learning with real-time, context-sensitive decision-making. In practice, this gap appears as static cognition, rigid workflow dependence, and inefficient context usage, which jointly limit adaptability in open-ended and non-stationary environments. To address these limitations, we present AutoAgent, a self-evolving multi-agent framework built on three tightly coupled components: evolving cognition, on-the-fly contextual decision-making, and elastic memory orchestration. At the core of AutoAgent, each agent maintains structured prompt-level cognition over tools, self-capabilities, peer expertise, and task knowledge. During execution, this cognition is combined with live task context to select actions from a unified space that includes tool calls, LLM-based generation, and inter-agent requests. To support efficient long-horizon reasoning, an Elastic Memory Orchestrator dynamically organizes interaction history by preserving raw records, compressing redundant trajectories, and constructing reusable episodic abstractions, thereby reducing token overhead while retaining decision-critical evidence. These components are integrated through a closed-loop cognitive evolution process that aligns intended actions with observed outcomes to continuously update cognition and expand reusable skills, without external retraining. Empirical results across retrieval-augmented reasoning, tool-augmented agent benchmarks, and embodied task environments show that AutoAgent consistently improves task success, tool-use efficiency, and collaborative robustness over static and memory-augmented baselines. Overall, AutoAgent provides a unified and practical foundation for adaptive autonomous agents that must learn from experience while making reliable context-aware decisions in dynamic environments.

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