arXiv:2606.29225v1 Announce Type: new Abstract: LLM agents handle user requests on behalf of organizations through tool calls and must follow the company policies stated in their system prompts. Prior work approaches this as a safeguarding problem -- external checks that block non-compliant agent actions. We argue that policy adherence is a broader problem: real workflows unfold across many turns, require explicit user confirmation and prerequisite reads, and hinge on the content of the dialogue rather than on any single argument value. Meeting this bar requires (i) full conversation context,
The proliferation of LLM agents in enterprise settings necessitates robust policy adherence mechanisms to ensure ethical and compliant operations, moving beyond simple safeguarding to context-aware verification.
This work introduces a critical advancement in making LLM agents reliable and trustworthy for complex, multi-turn organizational workflows, enabling broader adoption and reducing operational risks.
The shift from external safeguarding to dialogue-grounded sub-agent verification allows for more nuanced policy adherence, considering full conversation context rather than isolated actions, enhancing agent autonomy and safety.
- · Enterprises deploying LLM agents
- · AI governance and compliance platforms
- · LLM agent developers
- · Companies with weak AI governance frameworks
- · Systems relying solely on superficial AI safeguarding
- · Providers of non-contextual policy enforcement tools
LLM agents become more reliably able to operate within stated organizational policies, reducing incidents of non-compliance.
Increased trust in LLM agents fosters their integration into more sensitive and regulated business processes.
The development of sophisticated policy adherence tools could become a new sub-sector within the AI industry, influencing standards for agent deployment.
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Read at arXiv cs.AI