arXiv:2606.28739v1 Announce Type: new Abstract: Large language models increasingly act as agents: they call tools, move money, delete records, and send messages on a user's behalf. To keep them safe, practitioners imported the chatbot-era recipe (train the model to refuse unsafe inputs) into the agentic setting, and treat the resulting capability loss as a manageable ``alignment tax.'' We argue this is a \emph{category error}. Refusal is a primitive for \emph{content safety}, where the harm is in the model's output and is therefore a learnable function of it. Agentic harm is different in kind:
The proliferation of large language models transitioning from chatbots to active agents makes fundamental safety distinctions critical now.
A strategic reader must understand that agentic AI safety requires a different approach than content safety, impacting development, regulation, and deployment strategies.
The definition and methodology for AI safety are being re-evaluated for agentic systems, moving beyond simple content refusal to action alignment.
- · AI safety researchers
- · Agentic AI developers
- · Organizations deploying AI agents
- · Developers solely relying on chatbot safety paradigms
- · Companies with poorly aligned AI agents
- · Users impacted by misaligned agents
Increased focus and investment in agent-specific safety research and development.
New regulatory frameworks emerging for agentic AI that differentiate from content-based AI governance.
The acceleration or deceleration of AI agent deployment, depending on the success of action alignment methodologies.
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Read at arXiv cs.AI