AutoAgent: Evolving Cognition and Elastic Memory Orchestration for Adaptive Agents
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 knowle...
| Authors | Xiaoxing Wang, Ning Liao, Shikun Wei et al. |
|---|---|
| Primary category | cs.AI |
| Published | 10 March 2026 |
| Last updated at source | 10 March 2026 |
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