SINGAPORE – 20 MAY 2026
1. The Model AI Governance Framework for Agentic AI (“MGF for Agentic AI”) was launched at the World Economic Forum (WEF) in January this year. This first-of-its-kind framework for reliable and safe agentic AI deployment builds upon the governance foundations of MGF for AI, which was introduced in 2020. It provides guidance to organisations on how to deploy agents responsibly, recommending technical and non-technical measures to mitigate risks, while emphasising that humans are ultimately accountable. More information on the MGF for Agentic AI is available here.
2. Following the launch, IMDA had called for feedback to refine the framework and submission of case studies to demonstrate how agentic AI can be responsibly deployed. Today, IMDA has updated the framework to include real-world case studies and new best practices. These new additions will help more organisations by providing them with examples of how their counterparts have operationalised the MGF for Agentic AI recommendations for their systems, so that they can do the same.
3. The updated MGF for Agentic AI incorporates industry feedback from over 60 organisations, such as AWS, DBS, Google, and Salesforce, including how to manage risks from multi-agent systems and third-party agents, and address automation bias. It also features more than ten case studies of real-world agentic deployments that demonstrate how to operationalise different dimensions of the framework. Case studies were contributed by Singaporean and international companies such as Ant International, City Developments Limited (CDL), Cyber Sierra, Dayos, Google, Knovel, OCBC, PwC, Stability Solutions, Tencent, Terminal 3, Workday, and X0PA, as well government agencies including Government Technology Agency of Singapore (GovTech Singapore).
4. Some examples of case studies include:
| MGF dimension | Organisation | Agentic use case | How it operationalises the MGF |
|---|---|---|---|
| 1: Assess and bound the risks |
Dayos An enterprise AI automation company headquartered in Singapore with operations in the US. |
Dayos built an AI-powered ticketing agent which handles every internal IT request that comes in, and either resolves it automatically or routes it to a human. |
Dayos used tiered risk levels to guide and bound the actions taken by its agent. Every type of IT ticket was assessed for severity of impact, reversibility and feasibility of human oversight. Depending on this, the agent would have a different autonomy level. For example:
|
| 2: Enabling meaningful human accountability |
Tencent Multinational technology company headquartered in China, responsible for gaming, messaging app WeChat and AI models such as Hy. |
CodeBuddy is an agentic AI coding system developed by Tencent Cloud and used by its engineers. It can autonomously plan, write, test and deploy code through natural language instructions, with access to filesystems, terminal commands, external APIs and MCP tools. |
To enable meaningful human oversight, CodeBuddy employs a mix of preset secure defaults and configurable permissions to allow for meaningful human oversight without overly fatiguing the user.
|
| 3: Implement technical controls and processes |
GovTech A statutory board in Singapore that develops digital government services and drives public sector transformation. |
GovTech Singapore rolled out agentic coding assistants within the government. |
Govtech Singapore’s phased approach to rollouts of coding assistants allowed them to incrementally monitor risks while preparing controls for new features. First phase – limited to internal employees, no external tools, low-risk systems: In the first phase, the tool was limited to GovTech Singapore’s employees, external tools (MCP servers) were not allowed, and only for low-risk systems. This kept the potential damage small if anything went wrong. During this time, GovTech Singapore was able to build the necessary safeguards for a wider rollout – such as central logging, monitoring, and a framework to safely connect to approved external tools. They were also able to test the system against potential attacks to ensure effectiveness of guardrails. Lessons from phase one also helped improve the rollout – for example, fixing early technical issues, reducing cognitive load of human approvers, and making the overall setup easier to adopt. |
| 4. Enable end-user responsibility |
Workday Global enterprise AI platform for managing people, money and AI agents. |
Workday implemented AI agents to streamline its internal financial and human resources operations. The use cases for HR agents include:
|
To enable end-user responsibility, Workday informed users of:
|
3. Initiatives such as the MGF for Agentic AI are key to helping enterprises develop, deploy and use AI responsibly so that more can benefit. This is in line with Singapore’s practical and balanced approach to AI Governance, where guardrails are put in place, while providing space for innovation.
4. For more details, please refer to the update MGF for Agentic AI (1.78MB).