Agentic AI use-case screen: bound autonomy before build

An agentic AI use-case screen should decide how much autonomy is acceptable before a pilot is approved. The most important questions are what the agent can do, which tools and data it can reach, who may be affected and where a human must approve or stop the action. IMDA’s framework places responsibility on organisations and recommends assessing and bounding risk, making humans meaningfully accountable, controlling the lifecycle and enabling users to act responsibly.

Describe the action, not the label

Write the proposed task from trigger to outcome. State what starts the agent, what information it reads, which decisions it makes, which systems it can change and what result reaches a customer, employee or third party. Avoid descriptions such as smart assistant or autonomous workflow because they hide the actual power being granted.

Separate advice, drafting, recommendation and execution. An agent that suggests a reply has a different impact from one that sends it, changes a record or commits money. Map every external side effect. Where several agents interact, include the handoffs and shared memory because risk can arise from the combined process rather than one component.

Rate the possible harm

Consider financial loss, legal or regulatory impact, privacy, discrimination, safety, service interruption, fraud and reputational harm. Assess the number and vulnerability of people affected, reversibility, detectability and speed. A rare event can still be material when the agent can act quickly across many records.

Use evidence, not a single unexplained score. Record scenarios and reasons. Ask the business owner, technology team, security, data protection, risk and the people who handle exceptions. Different functions often see different failure modes. Keep uncertainty visible and select a conservative boundary where evidence is weak.

Bound tools and data

The IMDA Model AI Governance Framework for Agentic AI recommends limits on autonomy and access. List every tool, API, database and file store. Give the agent only the permissions required for the bounded task. Use allowlists, transaction limits, separate environments and short-lived access where appropriate.

Classify the data the agent can read, infer, store and disclose. Check personal data, confidential business information and credentials. Do not place secrets in prompts or logs. Define what context may persist between sessions and how it will be deleted or corrected. A pilot should not inherit broad production access simply because setup is easier.

Place meaningful human checkpoints

Choose approvals before high-impact actions, not after the outcome is hard to reverse. Identify the reviewer, information shown, response time and authority to reject or modify. A person who clicks approve without time, context or competence is not meaningful oversight. Measure overrides and review quality during the pilot.

Design escalation for low confidence, conflicting instructions, unusual volume, policy exceptions and attempts to reach unauthorised tools. Give operators a clear pause and shutdown route. The existing human approval checkpoint guide provides a deeper operating design for accepted use cases.

Test before live action

Create normal, edge, adversarial and recovery tests. Include incomplete data, ambiguous requests, prompt injection, unavailable tools, model changes and repeated actions. Record expected behaviour and actual results. Test audit logs and rollback, not only output quality. A successful demonstration does not prove safe performance under operating pressure.

The updated IMDA framework factsheet adds case studies and practices for multi-agent systems, third-party agents and automation bias. Use them as implementation examples, not as a certification. Vendors remain one source of evidence; the deploying organisation keeps accountability for its own use case.

Approve a bounded pilot

State the allowed users, volume, duration, tools, data, human approvals, monitoring and stop conditions. Assign owners for business outcome, model performance, security, privacy and incident response. Approve expansion only after measured evidence shows the controls work and the benefit justifies the remaining risk.

Connect the pilot to the agentic AI approval guide and the AI data-readiness register. The use-case screen should remain a living record whenever autonomy, tools, data or affected people change.

Apply the screen to an accounts-payable agent

Suppose an agent reads invoices, matches purchase orders and suggests payments. Reading and matching can affect confidential data, but automatic payment creates a much larger financial consequence. The initial pilot could limit the agent to a sandbox, approved suppliers and recommendations below a low threshold while a finance employee reviews every exception and payment instruction.

The screen should test duplicate invoices, changed bank details, mismatched currency, missing purchase orders, prompt injection inside attachments and attempts to use an unapproved payment tool. Logs should show the input, retrieved records, proposed action, approval and final outcome. A reviewer must be able to stop a batch and explain why the agent reached the recommendation.

Expansion should depend on measured accuracy, exception handling, reviewer workload and control performance, not only faster processing. If reviewers routinely approve without checking, human oversight is failing even when no loss has occurred. The team may need better evidence presentation, lower volume, stronger training or a narrower action before granting more autonomy.

Record the approval outcome in language operators can use. State permitted and prohibited actions, data limits, required human decisions, monitoring and incident contacts. Put these rules into system configuration, user instructions and tests. Review them after vendor or model changes. A risk screen has little value if its conclusions remain in a meeting note while the deployed agent receives broader access or different instructions.

A useful screen produces a smaller, clearer pilot with explicit limits and accountable people. If the team cannot explain the agent’s actions, permissions, human approvals and stop conditions, the use case is not ready for live execution.