Standard / soft law
Every AI agent should have a distinct identity and its actions should be traceable to a supervising human
Each AI agent that can act autonomously must have its own distinct, verifiable identity (not a shared service account or a human's credentials), and every action it takes must be logged in a way that ties it back to that agent and to the human or team accountable for it. Without distinct identity and a tamper-resistant action log, you cannot answer 'which agent did this, under whose authority, and why' — which is the first question asked after anything goes wrong.
imda-agentic.agent-identity-and-action-traceability · IMDA MGF for Agentic AI (v1.5) — Section 2.1.2, Agent identity and authorisation · official source · jurisdictions: SG, INTL
Standard / soft law
AI agents should require human approval before high-impact or irreversible actions
When an AI agent can take actions on its own (calling tools, hitting APIs, moving money, changing records, sending communications), any action that is high-impact or hard to undo must pass through a human approval checkpoint before it executes. The agent proposes; a human with authority approves, modifies, or rejects; only then does the action run. Without this gate, an agent can lock in financial, legal, or operational harm at machine speed before anyone notices.
imda-agentic.human-approval-gate-high-impact-action · IMDA MGF for Agentic AI (v1.5) — Section 2.2.2, Design for meaningful human oversight · official source · jurisdictions: SG, INTL
Standard / soft law
Human involvement in AI decisions should be calibrated to harm; decisions should be explainable or repeatable (Singapore MGF)
Per Singapore's Model AI Governance Framework (2nd ed.), organisations should determine the degree of human involvement in AI-augmented decision-making — human-in-the-loop, human-over-the-loop (supervisory), or human-out-of-the-loop — calibrated via a matrix of harm severity × probability, so that higher-impact decisions retain meaningful human oversight/override. The decision-making process should be explainable, transparent, and fair; where explainability cannot practicably be achieved, organisations can consider documenting the repeatability of results. Detect a high-harm AI-augmented decision path running out-of-the-loop with no human oversight and no explainability/repeatability provision.
imda-agentic.mgf-human-involvement-explainability · Singapore Model AI Governance Framework (2nd ed.) — paras. 2.7(a), 3.13-3.15 (human involvement), 3.30 (repeatability) · official source · jurisdictions: SG