Enterprise IAM assumes a static actor: one human, one credential, one stable role. AI agents break this — they're provisioned dynamically, inherit permissions from invokers, and spawn sub-agents that delegate further, with no attribution chain or credential lifecycle model to match. On-device deployment makes this worse: there's no network authority in the loop to authenticate or log the agent's actions in real time.

This talk uses EdgeMind — an on-device agentic system running Phi-3 Mini via ExecuTorch on Snapdragon HTP, resolving most queries in under 300ms with zero cloud egress — as a case study. We walk through its five-agent EdgeForge pipeline to show how identity and authorization can be scoped per agent stage, using PyTorch/ExecuTorch-level techniques (export/quantization provenance, runtime integrity checks, constrained action surfaces) as substitutes for network identity controls when offline. Attendees leave with a concrete architecture pattern, not just the problem statement.