Policy Algebra for Trust-Preserving Agentic AI Execution
A recent study published on arXiv (2608.16402) introduces a policy algebra designed to enhance the reliability of capabilities within large language model-based agentic frameworks. The researchers emphasize that effective enterprise execution necessitates not just capability but also reliable capability, which is characterized by the ability to perform tasks while adhering to constraints related to identity, profile, tools, data, memory, budget, artifacts, approval, and audits. The proposed policy algebra establishes a reliability envelope for exercising agent capabilities, integrating security profiles and runtime obligations through various operations. This work tackles issues such as unauthorized data access and unapproved side effects, aiming to maintain trust in agentic AI systems by imposing constraints on their actions. The paper can be accessed at arxiv.org/abs/2608.16402.
Key facts
- Paper ID: arXiv:2608.16402
- Announce type: new
- Proposes a policy algebra for agentic AI execution
- Defines reliable capability as a path property
- Constraints include identity, profile, tool, data, memory, budget, artifact, approval, and audit
- Security profiles and runtime obligations compose through joins, intersections, budget narrowing, approval inheritance, and evidence accumulation
- Addresses unauthorized data access, widened delegated authority, unapproved side effects, unrecoverable budget consumption, and incomplete evidence
- Published on arXiv
Entities
Institutions
- arXiv