Raffles Consulting Services
Design agentic AI user transparency for identity, capabilities, limits, human escalation, feedback, records and outcome review.
Agentic AI user transparency should help a person understand when an agent is acting, what it can do, where its limits lie and how to reach a human. A generic AI label does not explain whether the system may search records, use tools, make recommendations or take actions. The explanation needs to follow the user’s journey and the consequence of the task.
Tell users what the agent is and can do
At the first meaningful interaction, identify the system as AI and state its role in plain language. Explain whether it provides information, drafts content, recommends decisions or takes actions through connected tools. Add the important limits, such as unavailable records, uncertain outputs, eligibility decisions reserved for an authority or actions that require human approval.
The updated IMDA framework for agentic AI includes user transparency and appropriate training alongside bounded risks, human accountability and technical controls. The IMDA artificial-intelligence resource page provides the wider national governance context. Apply the framework proportionately to the deployed system and sector obligations.
Explain actions, uncertainty and human escalation
Before a consequential action, show what the agent proposes, which account or record is affected, the material source information and what will happen next. Distinguish a verified fact from an inference, recommendation or estimate. Use a short summary with access to detail so the person can make a real choice without reading an unexplained system trace.
Offer a visible route to pause, correct, complain or ask for human review. State the expected response path and preserve the user’s context so the human does not begin from nothing. For higher-risk work, link to the existing human-approval checkpoint guide and keep this article focused on what the user must be told.
Test notices, feedback and outcome records
Run user tests for ordinary work, uncertainty, a failed tool, an unavailable record, a rejected request and a harmful or mistaken outcome. Ask users to explain what the agent did, what it could not do and how they would reach a person. Revise the notice where users infer more authority or reliability than the system has.
Record the notice version, user feedback, escalation reason, human response and resulting correction without retaining unnecessary personal data. Review recurring confusion and complaints as product-control evidence. Reassess transparency when tools, data, prompts, models, user groups, languages or business authority change.
Turn the decision into a controlled file
Use a dated decision record that identifies the applicant, business need, current official source, chosen route, responsible owner, costs, milestones and evidence. Separate facts already verified from forecasts and assumptions. A catalogue listing, supplier statement, consultant recommendation or portal acknowledgement does not establish approval. The company remains responsible for its declarations and commercial decision.
Give each quotation, contract, invoice, payment and deliverable a stable reference. Preserve earlier versions and explain material changes instead of silently replacing them. Where the authority, tax adviser, auditor or another professional must decide a point, record the open question and obtain the required view. Do not convert uncertainty into a confident eligibility statement.
Review before commitment and again before closeout
Run four reviews: suitability, authority, implementation and closeout. At suitability, test the entity, activity and business outcome. Before commitment, freeze the proposed scope, vendor and financial assumptions. During implementation, compare actual work with approved terms. At closeout, reconcile deliverables, accepted results, invoices, full payment, operating evidence and obligations that continue.
Name the person who may approve each stage and the records that person must see. Procurement should not treat a draft application as permission to contract. A project team should not treat supplier completion as company acceptance. Finance should not treat a submitted claim as cash received. Clear status words prevent accidental commitments and weak reporting.
Handle exceptions without rewriting history
Maintain an exception log for changed facts, delayed milestones, supplier variations, cost movements, missing records and failed tests. State the effect on scope, eligibility, timing and expected value. If an authority decision or formal variation is required, use the published route before relying on the change. Silence and helpdesk receipt are not approval.
If a material defect appears, pause the affected acceptance, filing or claim item. Keep original evidence, assign corrective action and set a review date. Do not backdate records, move costs between categories or adjust a baseline after seeing the result. A candid exception with an owned resolution is more credible than a clean-looking file that cannot explain what happened.
Plan handovers before the original project team disperses. The handover should identify the current version, source locations, system access owner, next review date, open exceptions and any condition that continues after payment or filing. Restrict personal, financial and commercially sensitive records to authorised users, while giving each responsible owner enough information to perform the assigned control. Record the external event that should trigger an earlier review, such as a changed programme page, revised authority letter, supplier failure or material business change.
Verify the final outcome independently
Ask someone who did not prepare the final pack to compare the decision, current official source, approved terms, deliverables, payments and operating evidence. Recalculate totals and sample source records. The reviewer should be able to reproduce the conclusion without relying on the preparer’s memory. Record unresolved points with named owners and dates.
Use the Singapore business support decision guide for the wider decision and keep this article as the narrower operating guide. After submission, preserve the receipt and reconcile questions, adjustments and the final registered or paid outcome. Continue any reporting, retention, asset-use or other condition that survives closeout.
Practical review checklist
- AI identity disclosed
- Capabilities and limits explained
- Consequential actions described
- Human escalation visible
- Feedback and outcomes reviewed
Agentic AI user transparency is effective when people can identify the agent, understand its relevant powers and limits, review important actions and reach a human with the necessary context.