arXiv:2607.03510v1 Announce Type: cross Abstract: Enterprise artificial intelligence is moving from experimentation into operational workflows. Early programs focused on model access and retrieval-augmented generation, but enterprises are now beginning to deploy agents that plan, retrieve, remember, call tools, update systems, and coordinate work across applications. This changes the evaluation problem. Leaders are no longer asking only whether an answer is accurate or fluent. They need to know who authorized an action, which policy applied, whether evidence was current, whether memory was val
Enterprises are moving beyond experimental AI models to deploy agentic systems that directly interact with operational workflows, necessitating a new evaluation framework for control, assurance, and governance.
The shift to enterprise agentic AI introduces complex challenges for accountability, policy adherence, and evidence validation, directly impacting regulatory compliance and operational security.
Evaluation metrics are evolving beyond accuracy and fluency to include sophisticated assessments of authorization, policy application, data currency, and memory integrity for autonomous AI agents.
- · AI governance solution providers
- · Cybersecurity firms
- · Enterprise AI platform developers
- · Regulatory bodies
- · Companies with weak AI governance strategies
- · Legacy compliance frameworks
- · Organizations without robust data audit trails
Enterprises require new tools and processes to manage the risks associated with deployable agentic AI systems.
New standards and regulations for AI assurance will emerge, driving demand for specialized expertise and technologies.
The proliferation of agentic AI could lead to a redefinition of organizational liability and accountability in automated decision-making.
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Read at arXiv cs.AI