Tech Agent Guardrail

Tech — Agentic AI Governance Check (IMDA)

A four-dimension governance assessment for any AI agent deployment. Runs IMDA's Model AI Governance Framework (Agentic AI, 2026) iteratively across the agent lifecycle.

The framework is iterative — not a one-time gate. If anomalies appear in monitoring (Dimension 3), re-run Dimensions 1 and 2.

Verdict vocabulary (stable output contract): Deploy / Deploy-with-conditions / Do-not-deploy-until-gaps-addressed. This governs the agent's autonomy and accountability; where the underlying data may legally run is tech-data-deployment.

---

Dimension 1: Assess and Bound the Risks Upfront

Core question: Is this use case suitable for an agent, and how do we limit its blast radius by design?

SUITABILITY CHECK:

• What is the intended scope of the agent's actions? (Tasks it can take, data it can access, systems it can touch)
• What is the blast radius if the agent takes an unintended action? (Internal-only / operational / customer-facing / regulatory)
• Is this use case suitable for autonomous operation, or does it require a human decision-maker in the loop?

RISK SCORING: For each of the following risk dimensions, rate LOW / MEDIUM / HIGH:

• Impact — what is the worst-case consequence of an agent error?
• Likelihood — how often could errors occur given current controls?
• Recovery — how quickly and completely can an error be reversed?

STRUCTURAL BOUNDS: Agents must be bounded by design, not by prompt. Identify:

• Which tools does this agent have access to? (Should it have access to all of them, or should access be scoped narrower?)
• What data can it read? What data can it modify?
• Does it operate with a single all-powerful agent pattern? (Red flag — avoid; use scoped agents instead)
• What authorizations and identity credentials are issued for this agent?

Output: SUITABILITY | RISK SCORES (impact/likelihood/recovery) | STRUCTURAL BOUNDS | AGENT IDENTITY STATUS

---

Dimension 2: Make Humans Meaningfully Accountable

Core question: Who is responsible for what, and how do we ensure human oversight remains effective at scale?

RESPONSIBILITY ALLOCATION across the agentic value chain:

• Model developer: has this foundation model been assessed for the intended use case?
• Platform/tooling provider: what guarantees does the platform provide on safety and performance?
• Deployer (this organization): who specifically owns the deployment decision? Who is the named accountable individual?
• Users: what is expected of users who interact with or oversee the agent?

WITHIN-ORGANIZATION ACCOUNTABILITY:

• Who made the deployment decision? (Named person — not a team)
• Who monitors agent behavior post-deployment?
• Who has authority to pause or shut down the agent?
• Who reviews override rates and escalation patterns?

HUMAN-IN-LOOP DESIGN: For each consequential action type the agent can take, state whether:

• Human approval is required before the action (pre-approval)
• Human review is required after the action (post-review)
• The action is fully autonomous with monitoring only (name the monitoring mechanism)

AUTOMATION BIAS CHECK: If humans are in the loop, are they actually reviewing — or rubber-stamping? Name the mechanism for auditing override rates and response times.

Output: RESPONSIBILITY MAP | NAMED ACCOUNTABLE INDIVIDUAL | HUMAN-IN-LOOP DESIGN | AUTOMATION BIAS RISK

---

Dimension 3: Technical Controls and Processes

Core question: How do we operationalize safety across the lifecycle?

PRE-DEPLOYMENT TESTING — confirm each is complete:

• [ ] Task execution testing (does the agent do what it's supposed to?)
• [ ] Policy adherence testing (does it stay within its authorized scope?)
• [ ] Tool-calling correctness (are external tool calls accurate and bounded?)
• [ ] Robustness testing (does it handle edge cases, adversarial inputs, unexpected states?)
• [ ] Multi-agent interaction testing (if it operates within a multi-agent system: what are the interaction failure modes?)

STRUCTURAL CONTROLS (not prompt-layer only):

• Are guardrails structural (architecture, authorization, API scope) or prompt-based only?
• Prompt-layer controls alone are insufficient — flag if structural controls are absent.
• Are permissive defaults tightened? (Default should be minimum-necessary access, not maximum access)

POST-DEPLOYMENT OPERATIONS:

• Gradual rollout plan (start narrow, monitor, expand — not big-bang deployment)
• Continuous monitoring: what anomaly detection exists?
• All agent actions logged? (Yes/No — No is a red flag)
• Change management process for agent updates

Output: PRE-DEPLOYMENT TEST STATUS | STRUCTURAL CONTROLS ASSESSMENT | POST-DEPLOYMENT OPERATIONS PLAN | LOGGING STATUS

---

Dimension 4: End-User Responsibility

Core question: How do we equip the people who use or oversee agents to do so safely?

USER TRANSPARENCY: Does the end user know:

• What capabilities this agent has?
• What range of actions it can take?
• How to escalate to a human?
• How their data is handled?

TRAINING AND EDUCATION:

• Have users and oversight personnel been trained on autonomous-agent risks?
• Is the training specific to THIS agent's capabilities and failure modes, or generic?

TRADECRAFT PROTECTION:

• Does this agent automate tasks that require foundational skills to be maintained by human operators?
• If yes: what is the plan to protect those skills from erosion?

(Example: if an agent automates document review, do human reviewers still maintain the ability to review without the agent?)

Output: USER TRANSPARENCY STATUS | TRAINING COMPLETENESS | TRADECRAFT RISK

---

Synthesis: Governance Readiness Assessment

Consolidate Dimensions 1–4.

READINESS VERDICT: [Deploy / Deploy-with-conditions / Do-not-deploy-until-gaps-addressed]

For each gap found:

• State the gap in one sentence
• State the required action (specific, not general)
• State the accountable owner for the action

CRITICAL GAPS (any one of these = Do-not-deploy):

• No named accountable individual (Dimension 2)
• No structural controls — prompt-layer only (Dimension 3)
• No agent action logging (Dimension 3)
• Agent operates in customer-facing or regulatory-consequence context without human-in-loop (Dimension 2)
• Pre-deployment testing incomplete for consequential action types (Dimension 3)

REGULATORY CONTEXT:

• EU AI Act (Art. 4): if deploying in EU context, AI literacy obligation applies to all users
• EU AI Act (Art. 26): if this is a high-risk system, named human oversight is legally required
• NIST RMF MANAGE: every AI system needs a documented "off" path (MANAGE 2.4)

(Consult eu-ai-act-essentials.md and nist-rmf-functions.md for detailed obligations.)

Output: READINESS VERDICT | GAPS LIST | CRITICAL GAPS | REGULATORY OBLIGATIONS TRIGGERED

---

References

All files below live in references/ at the plugin root (${CLAUDE_PLUGIN_ROOT}/references/ when installed as a plugin).

• imda-4-dimensions-agentic.md — the four-dimension governance framework (IMDA 2026)
• eu-ai-act-essentials.md — Art. 4 literacy obligation; Art. 26 deployer obligations; risk tiers
• nist-rmf-functions.md — GOVERN, MANAGE functions; MANAGE 2.4 (off-path requirement)
• pwc-roi-2026-governance.md — 1.7× AI leadership advantage for documented RAI strategy

Reference files are bundled with this skill — Claude resolves them by filename regardless of install layout (single-skill or plugin).

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: geledek
• Source: geledek/enterprise-ai-transformation-skills
• License: MIT

Install and usage instructions live in the source repository linked above.