Governance frameworks for real-world deployment of agentic AI

Develop governance frameworks for real-world deployment of large language model-based agent systems, explicitly addressing long-horizon behaviors, planning-time failures, and multi-agent dynamics that are underexplored in existing benchmarks.

Background

The paper argues that embedding LLM agents into long, irreversible FAIR data management workflows introduces fail-open failure modes that require architectural governance. In surveying related work on agent frameworks and memory systems, the authors highlight that least-privilege access, immutable audit logs, and deterministic validation are often missing and that governance remains insufficiently addressed.

They explicitly cite a recent survey that frames the lack of governance frameworks for real-world deployment as a central open problem, especially given the limitations of current benchmarks which emphasize short-horizon behaviors over planning-time failures and multi-agent dynamics.