From Allocation to Answerability: A Feedback Submission on the Model AI Governance Framework for Agentic AI (Version 1.5)

This working paper is a feedback submission to Singapore’s Infocomm Media Development Authority on the Model AI Governance Framework for Agentic AI (version 1.5). It argues that the framework is operationally strong, but that its aspiration to secure ‘meaningful accountability’ requires three further refinements. First, the paper distinguishes allocation from answerability: assigning a named human, team, or role does not by itself ensure that anyone can reconstruct, justify, and bear responsibility for a specific agentic-AI action. Secondly, it argues that composition is a distinct source of risk in multi-agent systems, because a duty may be satisfied at every individual node yet fail at the level of the composed system. Thirdly, it argues that binding constraints on high-stakes or irreversible agent actions must sit at an objective layer outside the agent’s generative reach, rather than merely inside prompts, behavioural instructions, or model-context safeguards. The paper also clarifies the distinction between autonomy and agency, recommending that responsibility-language be reserved for humans and institutions, while capability-language be used for artificial agents. The submission therefore develops a governance account in which traceability, allocation, oversight, and identity management are necessary but insufficient unless linked to institutional answerability and structurally enforceable constraints.