All Permissions Granted: Agentic AI, Delegated Access, and the Governance of Semantic Footprints

This review examines how agentic AI reorganizes privacy, accountability and user agency by turning delegated access into operational context. Agentic AI is treated as a graded socio-technical configuration rather than a clean rupture with conversational AI: conversational interfaces may be coupled with contextual retrieval and memory, external tools and applications, organizational data, and execution or delegated action. The review synthesizes public documentation from OpenAI, Anthropic, Google, Microsoft, GitHub, DeepSeek and the Model Context Protocol ecosystem, alongside governance and cybersecurity frameworks and scholarship in human-AI interaction, privacy, infrastructure, accountability and usable security. Its contribution is to clarify how user content, communication and scheduling records, repository and browser context, and persistent traces such as logs, feedback, memories and local execution records can become semantic footprints when they reveal priorities, dependencies, intentions, vulnerabilities, roles or possible actions within agentic processes. The review develops the concepts of delegated temporal compression, capability amplification, semantic footprints, the agentic permission grey box and dynamic trust boundaries. Formal consent is insufficient when AI systems operate across fragmented informational environments. Users and organizations require capacity for intervention: the practical ability to understand, limit, audit, contest and withdraw delegated access. The Agentic Permission Transparency Standard is derived from the review synthesis as a design-facing framework for making agentic delegation visible, limited, auditable, revocable and contestable.