Continual Learning for Enterprise AI Agents

Existing research in continual learning has primarily focused on model-level adaptation, including updating model parameters and representations as new data becomes available. However, modern AI systems are rarely deployed as standalone models, particularly in enterprise AI applications. Instead, they are increasingly realized as intelligent agents that operate in complex environments, interact with external tools, and coordinate across multiple system-level components. This shift calls for a broader perspective on continual learning, which extends beyond model adaptation to encompass agent-level adaptation, including tool use, workflow optimization, orchestration strategies, safety mechanisms, and feedback integration. To address this emerging challenge, we propose a workshop on continual learning for AI agents, with a particular emphasis on enterprise settings. Enterprise AI agents are already being deployed across domains such as IT operations, customer support, software engineering, and business process automation. These applications provide a compelling and realistic setting for studying continual learning because they involve evolving tasks, changing system infrastructures, rich interaction logs, diverse feedback signals, and complex multi-tool workflows. At the same time, enterprise settings offer valuable artifacts for developing realistic benchmarks (e.g., ITBench, SWE-bench, GDPval) and evaluation protocols for continual learning in agentic systems.

By bringing together researchers in continual learning, reinforcement learning and agent systems, and industrial practitioners building production-grade agents and harnesses, this workshop aims to advance the methodological foundations and practical deployment strategies of continual learning for enterprise AI agents.