| title | IMDA — Four Dimensions of Agentic AI Governance |
|---|---|
| source-id | imda-agentic-ai-2026 |
| wiki-page | (02_References/enterprise-ai/wiki/frameworks/four-dimensions-of-agentic-ai-governance.md) |
| last-synced | 2026-06-07 |
IMDA's Model AI Governance Framework (Agentic AI extension), 2026. The central governance model for responsible agentic AI deployment: four dimensions applied iteratively across the agent lifecycle.
| # | Dimension | Core question |
|---|---|---|
| 1 | Assess and bound the risks upfront | Is this use case suitable for an agent, and how do we limit its blast radius by design? |
| 2 | Make humans meaningfully accountable | Who is responsible for what, and how do we ensure human oversight remains effective at scale? |
| 3 | Implement technical controls and processes | How do we operationalize safety across the lifecycle? |
| 4 | Enable end-user responsibility | How do we equip the people who use or oversee agents to do so safely? |
Source: IMDA Agentic AI Framework 2026, p.13.
Dimension 1 — Assess and bound:
- Score use case on risk factors (impact × likelihood)
- Set structural limits on tools, data, autonomy, and area of impact
- Issue agent identities and authorizations before deployment
- Avoid single all-powerful agents; restrict device installation in mission-critical contexts
Dimension 2 — Human accountability:
- Allocate responsibility across the agentic value chain (model devs, platform, deployer, users)
- Design human-approval checkpoints
- Audit override rates and response times (automation bias is a measurable risk)
Dimension 3 — Technical controls:
- Pre-deployment: structural controls (not prompt-layer only); agent-specific testing (task execution, policy adherence, tool calling, robustness)
- Post-deployment: gradual rollout, continuous monitoring, change management
- Tighten permissive defaults; dedicated agent credentials; log all actions
Dimension 4 — End-user responsibility:
- Transparency to users (capabilities, actions, escalation paths, data handling)
- Training on autonomous-agent risks
- Protect foundational skills and tradecraft from agent erosion
"These four dimensions should be viewed as an iterative process. If an anomaly is detected during monitoring, organisations should re-evaluate the earlier dimensions." — IMDA 2026, p.13
- For skill
tech-agent-guardrail: this IS the core content — the skill operationalizes all four dimensions as a structured assessment. - For skill
tech-stack-diagnostic: Dimension 3 (technical controls) maps to the oversight and ops layers of the stack. - For skill
people-readiness-conversation: Dimension 2 (human accountability) surfaces the governance-ownership gap in leadership teams.
IMDA Model AI Governance Framework (Agentic AI), 2026, pp.13–47. The four-dimension model, iterative principle, and worked examples including the OpenClaw case.