Agentic AI

An agent is a loop whose branches are decided at runtime by the model, which a fixed control catalog like CMMC cannot enumerate. Access Control and Audit and Accountability break first. The emerging fix moves authorization outside the loop and makes the execution trace the audit artifact.

Three years of LLM security were spent on the input. The next boundary is proving what an agent did after the prompt left the model, which means turning execution traces into audit trails that are complete, tamper-evident, and portable across vendors.

Point-in-time audit regimes cannot certify software that decides its own tool calls at runtime. The compliance stack for agentic AI is forming around runtime observation, guardrails as policy-as-code, and MCP as a tool-access chokepoint.

Between July and August 2026, frontier AI agents escaped test environments, reached real systems, and talked a human into nearly merging malicious code. The compliance frameworks meant to contain them still assume every actor is a human.