On 22 January 2026, Singapore’s Infocomm Media Development Authority (IMDA) released its Model AI Governance Framework for Agentic AI, a timely response to the next phase of AI deployment. As artificial intelligence systems evolve from content generators to autonomous agents capable of acting on behalf of humans, this framework fills a critical governance gap: how to safely and accountably manage AI that not only thinks, but does.
Agentic AI systems are already reshaping enterprise workflows, from coding assistants and customer service agents to multi-agent automation systems. However, their autonomy, access to external tools, and ability to coordinate across systems introduce new risks such as cascading failures, tool misuse, and challenges in tracing accountability when things go wrong. These developments raise the stakes for operational governance and institutional readiness.
A Practical Framework for Agentic Governance
Singapore’s framework outlines four pillars for responsible deployment of agentic AI, offering actionable guidance for public and private sector organisations alike:
- Assess and Bound Risks Upfront
The first step is to evaluate potential agent use cases by considering both the likelihood and impact of risk. Key factors include agent autonomy, access to sensitive systems, and reversibility of actions. To mitigate risk, organisations are advised to restrict tool access, use sandboxed environments, and establish fine-grained identity and permission systems. - Make People Meaningfully Accountable
Clear roles should be defined across the agent lifecycle, from product teams to executive oversight. Human-in-the-loop mechanisms, especially for high-stakes or irreversible actions, are critical, as is guarding against automation bias in supervisory roles - Implement Technical Controls and Processes
Agentic systems require new technical safeguards. The framework calls for testing not only output accuracy, but also tool usage, policy compliance, and workflow reliability. Post-deployment, agents should be rolled out gradually and continuously monitored to detect anomalies and unintended behaviours - Enable End-User Responsibility
Governance doesn’t stop with developers. End-users, whether internal staff or external stakeholders, must be equipped with training and transparency on what agents are permitted to do. Clear communication, oversight protocols, and user education help mitigate over-reliance and ensure users remain active stewards of AI systems
Building on Singapore’s AI Governance Leadership
This latest framework builds on Singapore’s 2019 Model AI Governance Framework, which focused on trusted AI principles such as transparency, fairness, and human-centricity. It reflects Singapore’s continued leadership in aligning innovation with accountability, offering clarity in ethics and implementation.
The release also contributes to a growing regional convergence. South Korea’s AI Basic Act (2026) and Taiwan’s AI Basic Act (2025) similarly address human oversight, data safeguards, and prohibitions against harmful or deceptive AI uses. Together, these frameworks mark a shift toward shared regulatory norms in Asia-Pacific built on proactive governance rather than reactive regulation.
As the agentic AI landscape matures, Singapore’s framework sits at an important midpoint between high-level principles and formal system-theoretic governance. Recent proposals such as the Stability-Assured Framework for Entities (SAFE) by AI Asia Pacific Institute’s board advisor, David Hardoon suggest applying control theory concepts, including observability, controllability, and feedback latency, to govern autonomous AI with the same precision used in critical infrastructure. While the IMDA framework does not yet formalize these dynamics, it introduces operational anchors such as sandboxing, monitoring, and escalation protocols that could evolve toward more measurable control guarantees.
This suggests a clear trajectory: as agentic systems scale, governance will need to incorporate not just human judgment, but quantitative measures of system stability and responsiveness. Singapore’s model creates space for such evolution; a practical baseline today, and a foundation for embedding deeper assurance frameworks like SAFE tomorrow.
Looking Ahead
Singapore’s agentic AI framework is a living document, intended to evolve with stakeholder feedback and real-world deployment experiences. As organisations integrate agentic systems into daily operations, the challenge will be scaling governance: embedding it not just in policy, but in design, deployment, and use.
The framework provides a strong starting point that as AI becomes more agentic, institutions must become more adaptive.
