UAE Government 4.0: Turning Agentic AI Ambition into Trusted Delivery at Scale

The UAE Has Entered a New Stage of Government Transformation

The UAE has repeatedly used technology to redefine the experience and performance of government. From eGovernment and smart government to mobile services, integrated identity infrastructure and proactive public-service design, the country has built a record of applying digital capability to improve how people, businesses and institutions interact with government.

Agentic AI now represents the next step in that evolution.

In April 2026, the UAE announced a government framework aiming to transition 50% of government sectors, services and operations to Agentic AI models within two years. In May 2026, the UAE Cabinet approved an implementation framework for ministries and federal entities, launched the first package of services to be powered by Agentic AI, and approved the Federal Government Employees’ Skills and Capabilities Development Programme to train 80,000 federal employees across leadership, technical, specialist, general-workforce and train-the-trainer categories.

The UAE also launched an initial cohort of government AI agents in areas including procurement, tax auditing, customer happiness and technical support—demonstrating that the agenda is moving from strategic announcement to practical application.

This ambition is significant because Agentic AI is not simply another channel for digitizing services or answering employee questions. It introduces the potential for governed systems to perform multi-step work: retrieve authorized knowledge, analyse information, prepare outputs, support decisions, coordinate workflow activities and, where explicitly approved, execute defined actions.

For federal entities, the question has therefore changed.

The priority is no longer whether government should explore AI. It is how each ministry and authority will convert national ambition into trusted, measurable and scalable operational capability.

This requires more than technology selection. It requires workflow discovery, project qualification, knowledge and data readiness, capability building, governance, agent design, testing, implementation support and change leadership.

The Real Meaning of the 50% Transformation Target

A target to transform 50% of government services and operations into Agentic AI models should not be interpreted merely as a count of chatbots, tools or automation initiatives. It requires a redesign of how government work is performed.

A successful transformation must address three levels simultaneously:

Level of Transformation	Central Question	Required Outcome
Service Experience	How can AI make interactions easier, faster and more responsive for citizens, residents, businesses and investors?	Improved service accessibility, response quality, timeliness and user experience.
Government Operations	Which internal workflows can AI support or perform more efficiently while preserving accountability?	Reduced administrative burden, faster processing, improved consistency and stronger operational insight.
Institutional Capability	How will leaders and employees manage, supervise and continuously improve AI-enabled government work?	A skilled workforce, clear governance, Human + AI operating practices and sustainable adoption.

The risk of focusing only on the technology layer is that entities may produce individual AI tools without creating a coherent operating model. The risk of focusing only on training is that employees may learn about AI without knowing which workflows should change. The risk of pursuing rapid automation without qualification is that high-impact or sensitive processes may be redesigned without sufficient governance, evidence or public trust.

The strategic task for each federal entity is to build an integrated Agentic AI transformation portfolio: one that connects service priorities, operational workflows, people capability, data conditions, risk controls and measurable outcomes.

Agentic AI in Government: A Governed Capacity, Not Uncontrolled Autonomy

Agentic AI can be understood as an AI-enabled capability designed to perform or coordinate a defined sequence of work toward an approved objective. It may review information, use authorized knowledge sources, prepare analysis, recommend actions, route cases, execute permitted process steps and escalate exceptions to responsible officials.

In government, this capability must be designed with particular care. Public-service decisions and government operations may involve legal obligations, citizen rights, personal information, public funds, compliance requirements, reputational trust and national security considerations.

The relevant design question is not simply whether an AI agent can perform an activity. It is whether it should perform that activity, at what level of authority and under what human accountability.

A Practical Autonomy Spectrum for Government Work

AI Responsibility Level	What the AI Capability Does	Suitable Context	Human Accountability
Assist	Retrieves approved information, summarizes, drafts or prepares options.	Knowledge access, correspondence drafting, briefing preparation.	Officer reviews and approves output before use.
Recommend	Analyses information and suggests a classification, action or decision pathway.	Case triage, service routing, preliminary document review.	Qualified owner validates recommendation and makes decision.
Execute Under Approval	Completes defined process steps after human authorization or within controlled rules.	Approved notifications, standard workflow updates, routine internal requests.	Process owner remains accountable and monitors exceptions.
Execute Within Delegated Boundaries	Conducts multi-step activities within formally approved scope, permissions and controls.	Mature, low-risk or highly structured operations with robust monitoring.	Governance owner establishes boundaries; humans manage exceptions, audits and outcomes.

Public-Sector Design Principle

The goal is not maximum autonomy in every workflow. The goal is the appropriate level of AI responsibility for each government service or operation—grounded in value, risk, public trust and accountability.

The Entity-Level Implementation Challenge

The national direction is clear. The implementation burden now sits with ministries and federal entities, which must convert strategic ambition into practical portfolios of AI-enabled services and operations.

Each entity will need to answer six questions:

1. Which services and operations offer the greatest public or institutional value if redesigned through Agentic AI?

2. Which workflows are sufficiently clear, data-ready and manageable in risk to become early candidates?

3. Which activities must retain human approval, specialist judgment or formal accountability?

4. What capabilities do leaders, managers, specialists and employees need to manage AI-enabled work?

5. How will agents be designed, evaluated, piloted and integrated into secure government environments?

6. How will impact be measured in terms of service quality, efficiency, productivity, trust and responsible use?

These questions cannot be resolved through a single training programme or technology procurement. They require a delivery model that integrates business analysis, operating-model design, capability development, governance and implementation.

How MENTOR Can Support Federal Entities: A Five-Stage Transformation Model

MENTOR’s role is not to replace the UAE’s national programme, strategic knowledge partnerships or central governance. It is to support ministries and federal entities in operationalizing the transformation within their own mandates, services, people and workflows.

MENTOR combines management consulting, professional development, workflow analysis, AI deployment planning and change-management capability. Applied to the UAE Government Agentic AI agenda, this creates a practical five-stage support model:

Stage	MENTOR Support Focus	Entity Outcome
1. Discover	Identify high-friction services, operational bottlenecks and AI-enabled workflow opportunities.	An informed longlist of Agentic AI opportunities linked to entity priorities.
2. Assess and Qualify	Evaluate opportunities against value, feasibility, data readiness, risk, accountability and implementation requirements.	A prioritized project portfolio with clear pilot candidates and excluded/high-control uses.
3. Build Capability	Prepare leaders, managers, specialists and employees to design, supervise and adopt AI-enabled work.	Role-based readiness and Human + AI operating capability.
4. Design and Pilot Agents	Translate qualified opportunities into governed agent concepts, workflows, evaluations and pilots.	Tested agentic solutions with human-oversight and measurement models.
5. Measure and Scale	Track impact, refine controls, manage adoption and develop the entity roadmap for broader deployment.	Evidence-based scaling across services and operations.

Stage 1 — Discover: Identify Where Agentic AI Can Improve Government Performance

Begin with Services and Workflows, Not Tools

A ministry may have hundreds of activities that could be described as candidates for AI. Discovery is the process of identifying where AI-enabled change would create meaningful value—not simply where a technology demonstration is possible.

MENTOR can facilitate structured discovery sessions with entity leadership, department managers, process owners and frontline employees to understand:

• Citizen, resident, business or investor service journeys that involve delay, repetition or difficulty accessing information.

• High-volume internal operations consuming employee effort through document review, data retrieval, correspondence, reporting, routing or follow-up.

• Processes dependent on fragmented policy, procedural or institutional knowledge.

• Service interactions requiring multilingual information support or faster response preparation.

• Functions facing growing workload, limited specialist capacity or high administrative burden.

• Decision-support activities requiring faster analysis while retaining accountable human review.

Discovery Outputs

Output	Description
Entity AI Opportunity Map	Identifies where AI may improve services, operations, knowledge work or decision support.
Priority Workflow Inventory	Defines workflows at a practical level: trigger, inputs, current steps, bottlenecks, outputs and owners.
Service Impact Hypotheses	Clarifies expected benefit for customers, employees, efficiency or institutional effectiveness.
Initial Risk Flags	Identifies privacy, information-security, regulatory, financial, legal, ethical or public-trust considerations requiring deeper review.
Capability Needs Overview	Identifies which roles will need training, new working practices or oversight responsibility.

Illustrative Opportunity Domains

Government Domain	Illustrative Agentic AI Opportunity
Customer Happiness and Service Support	Agent-assisted retrieval of approved service information, multilingual response support, inquiry categorization and escalation.
Procurement and Internal Operations	Intake support, document completeness checks, sourcing workflow assistance, internal status follow-up and exception flagging.
Audit and Compliance Support	First-pass data verification, document comparison against approved requirements and preparation of issues for expert review.
Technical Support and Shared Services	Request classification, knowledge retrieval, standard-resolution support and escalation tracking.
Policy and Regulatory Knowledge	Source-grounded internal assistance for locating approved requirements, identifying updates and preparing briefing summaries.
Executive and Management Reporting	Preparation of decision briefs, action tracking, performance summaries and risk flags from approved sources.

The output of discovery is not a promise that each identified idea should become an agent. It is a portfolio of opportunities ready for structured qualification.

Stage 2 — Assess and Qualify: Select the Right Agentic AI Projects

Not Every Workflow Should Become an AI Agent

The UAE transformation timeline creates legitimate urgency. But urgency increases the importance of qualification: entities must distinguish between use cases that are high-value and achievable, those requiring deeper preparation, and those that should not advance without formal controls or specialist authorization.

MENTOR can help establish an Agentic AI Opportunity Qualification Framework that evaluates potential projects through a transparent, repeatable method.

Agentic AI Opportunity Qualification Framework

Qualification Dimension	Assessment Question	Why It Matters
Strategic Relevance	Does the use case support an entity mandate, service priority or national transformation objective?	Prevents scattered projects without institutional value.
Public/Operational Value	Will it measurably improve service quality, speed, productivity, cost, access or decision support?	Links technology to outcomes.
Workflow Clarity	Is the existing process understood, repeatable and owned?	Avoids automating confusion or uncontrolled practices.
Knowledge and Data Readiness	Are approved information sources, data definitions and access conditions available?	Determines reliability and implementation feasibility.
Risk and Sensitivity	Does the process involve rights, personal data, public funds, compliance, security or reputational risk?	Determines autonomy limits and governance requirements.
Human Accountability	Can a named owner review, approve, escalate and remain accountable for outcomes?	Preserves trust and responsibility.
Technical Feasibility	Can the agent operate within approved infrastructure, integrations and security conditions?	Prevents infeasible concepts from consuming resources.
Adoption Readiness	Are employees and managers prepared to use, supervise and improve the workflow?	Connects delivery with sustained implementation.
Measurability	Can baseline and outcome measures be defined?	Enables evidence-based decisions to scale.

Prioritization Outcomes

A qualified portfolio should divide ideas into practical categories:

Classification	Meaning	Recommended Action
Early Pilot Candidate	Clear workflow, meaningful value, manageable risk, accessible knowledge and accountable owner.	Design and pilot rapidly with appropriate controls.
Prepare then Pilot	Strong value potential, but gaps exist in data, policy, ownership or employee readiness.	Complete readiness actions before agent build.
High-Control Candidate	Potentially valuable, but involves higher-consequence decisions or sensitive information.	Require formal governance, specialist ownership, secure design and deeper evaluation.
Not Suitable / Defer	Low value, unclear process, excessive risk or no viable accountability model.	Avoid investment until conditions change.

Qualification Principle

The fastest way to scale responsible Agentic AI is not to build every idea. It is to identify the few workflows that can demonstrate trusted value, learn from them and expand on evidence.

Stage 3 — Build Capability: Prepare Federal Employees for Human + AI Government

The Transformation Will Be Won Through People

The UAE’s national programme to train 80,000 federal employees reflects an essential truth: Agentic AI implementation is not a technology adoption exercise alone. It is a workforce and operating-model transformation.

Federal employees will not merely become users of new tools. Depending on their roles, they may become:

• Designers of AI-enabled workflows.

• Supervisors of AI-generated outputs.

• Owners of agent performance and service outcomes.

• Custodians of trusted government knowledge.

• Reviewers of exceptions and sensitive cases.

• Champions of responsible adoption within departments.

• Leaders who determine where human judgment must remain decisive.

MENTOR can help ministries and federal entities translate the national learning ambition into entity-specific capability: preparing people for the workflows, services and agents their organization intends to implement.

Role-Based Capability Architecture

The Cabinet-approved programme identifies five occupational categories. MENTOR can support entity-level application by designing practical learning journeys for the specific responsibilities each group will carry inside the entity.

Occupational Category	Entity-Level Capability Need	Illustrative MENTOR Support
Leadership Category	Set ambition, prioritize use cases, sponsor adoption, understand risk and hold teams accountable for results.	Executive briefings, leadership decision labs, transformation governance workshops and portfolio review sessions.
Technical Category	Enable secure environments, data and knowledge access, integrations, testing and technical governance.	Agent design requirements, knowledge/data readiness workshops, evaluation planning and implementation alignment.
Specialist Category	Apply AI within domain-sensitive work such as policy, finance, HR, compliance, audit, legal or service operations.	Role-based workflow discovery, human-in-the-loop design, output-review protocols and applied agent labs.
General Workforce Category	Use approved AI tools responsibly in daily work and understand how roles and workflows are changing.	Practical AI adoption training, safe-use guidance, role-relevant examples and productivity workflow support.
Train-the-Trainers Category	Sustain learning, reinforce behaviors and scale practical competence across the entity.	Facilitator kits, learning materials, use-case libraries, coaching guides and adoption measurement tools.

From Awareness to Applied Capability

Effective capability building should not stop at general AI literacy. It should move through four levels:

Capability Level	Learning Focus	Practical Output
Understand	What Generative AI and Agentic AI are; why they matter to government service and operations.	Shared awareness and leadership alignment.
Use Responsibly	Approved tools, confidentiality, verification, output-review and escalation rules.	Safe individual and team application.
Discover and Design	How to identify workflows, define AI roles, map human oversight and assess readiness.	Qualified opportunity concepts and workflow designs.
Supervise and Improve	How to manage agents, evaluate outcomes, handle exceptions and improve adoption.	Sustainable Human + AI operating capability.

Capability-Building Deliverables MENTOR Can Provide

• Executive Agentic AI orientation and decision workshops.

• Department-manager workflow-discovery workshops.

• Role-based employee AI adoption programmes.

• Practical human-in-the-loop review training for specialists.

• Agentic AI use-case design labs.

• AI champions and train-the-trainer programmes.

• Responsible-use guides, workflow playbooks and learning toolkits.

• Manager coaching for adoption and performance integration.

• Adoption-readiness and capability assessment instruments.

• Learning-impact evaluation and continuous-improvement recommendations.

Workforce Principle

A government powered by Agentic AI still depends on human competence, judgment and values. Employees must be equipped not only to use AI, but to supervise it and improve the work it supports.

Stage 4 — Design and Pilot: Turn Qualified Workflows into Governed AI Agents

From Use-Case Idea to Working Agent

Once a workflow is qualified and the relevant stakeholders are prepared, the entity can move into agent design and pilot implementation. This stage requires close coordination between business owners, service teams, technology and data leaders, governance stakeholders and employees who understand the real operational context.

MENTOR can help translate business needs into deployable agent designs by defining what the agent is expected to do, what information it may access, where it must escalate, how humans supervise it and how performance is measured.

The Government AI Agent Design Charter

Every agent should be defined through a controlled charter before implementation.

Design Element	Required Definition
Agent Purpose	What approved service or operational outcome does the agent support?
Users and Beneficiaries	Which employees, service customers or leadership users benefit from its work?
Workflow Scope	Which activities does the agent perform, support or avoid?
Inputs and Sources	Which approved data, policies, records, knowledge bases or systems may it use?
Output and Action Authority	Does it draft, recommend, classify, route or execute a permitted process action?
Human Oversight	Who validates output, authorizes actions, handles exceptions and remains accountable?
Security and Privacy Conditions	What information restrictions, access permissions and environment requirements apply?
Escalation Rules	When must the agent stop, defer, flag uncertainty or refer the matter to a responsible official?
Evaluation Criteria	How will accuracy, quality, consistency, service impact and risk be tested?
Adoption Requirements	What training, communication and workflow change are necessary for users?
Performance Indicators	What measures determine whether the pilot should scale, improve or stop?

Illustrative Government Agent Designs

The UAE’s announced first agent cohort provides useful archetypes for entity-level implementation. Each archetype requires a clear human-accountability model.

Agent Archetype	Potential Contribution	Essential Human/Governance Control
Customer Happiness Agent	Retrieve approved service information, assist employees with multilingual responses, categorize inquiries and flag unresolved needs.	Approved knowledge sources, service-quality monitoring, privacy controls and escalation for complex or sensitive cases.
Procurement Support Agent	Assist with intake, document completeness review, approved process guidance, workflow status and exception flagging.	Procurement authority remains human; public-fund controls, auditability and approvals remain mandatory.
Tax or Audit Support Agent	Assist with data verification, checklist comparison, evidence organization and issues summaries.	Qualified auditors approve conclusions; secure data handling and traceable review are required.
Technical Support Agent	Categorize requests, retrieve approved resolutions, suggest actions and route incidents.	Access control, incident escalation, cybersecurity requirements and continuity oversight.
Policy Knowledge Agent	Locate approved policies and procedures, explain requirements and prepare internal summaries.	Source ownership, version control and specialist review for interpretation or formal action.
Executive Briefing Agent	Prepare decision briefs and performance summaries from approved reports and records.	Executives and analysts validate material findings and retain all decision authority.

Pilot Delivery Approach

A disciplined pilot should be small enough to control and meaningful enough to prove value.

Pilot Activities

1. Define the current workflow and baseline performance.

2. Approve the agent charter, autonomy level and human review model.

3. Confirm approved data, knowledge and technical environment.

4. Prepare evaluation cases, including normal, difficult and exception scenarios.

5. Build or configure the agent within the approved implementation setting.

6. Train pilot users, reviewers and workflow owners.

7. Test output quality, failure modes, escalation and user experience.

8. Run the pilot in a controlled operating context.

9. Compare results against baseline and collect stakeholder feedback.

10. Decide whether to refine, scale, restrict or discontinue the agent.

Agent Pilot Evaluation Dimensions

Evaluation Dimension	Illustrative Questions
Service Impact	Did the workflow reduce waiting time, improve response preparation or strengthen service consistency?
Operational Efficiency	Did it reduce manual steps, cycle time or repetitive administrative effort?
Quality and Accuracy	Did outputs meet standards, use correct sources and require acceptable correction levels?
Human Oversight	Were approvals, escalations and exceptions handled as designed?
Security and Trust	Were information and access requirements followed without incidents?
Employee Adoption	Did users understand, trust and correctly supervise the workflow?
Scalability	Are the process, sources, controls and technical model ready for broader use?

Agent Pilot Evaluation Dimensions

Government AI agents should not be judged by demonstration quality alone. They should be judged by whether they improve approved workflows safely, measurably and under clear human accountability.

Stage 5 — Measure and Scale: Build an Entity-Level Agentic AI Operating Model

From Pilots to Institutional Capability

Successful pilots create evidence. They do not, by themselves, create sustainable transformation. To scale Agentic AI across an entity, ministries and authorities need an operating model that governs the portfolio, supports adoption, manages knowledge, tracks impact and determines where the next wave of deployment should focus.

MENTOR can help entities structure this next phase through an Agentic AI Transformation Office or implementation governance model aligned with the entity’s existing transformation, strategy, digital or PMO arrangements.

Entity-Level Operating Model Components

Component	Purpose
Leadership Steering and Sponsorship	Maintain alignment with entity mandate, national direction and implementation priorities.
AI Opportunity Portfolio	Track discovered, qualified, piloted, deployed and deferred use cases.
Governance and Risk Review	Confirm autonomy boundaries, data conditions, specialist oversight and escalation requirements.
Agent Design Standards	Establish consistent charters, workflow mapping, evaluation and human-oversight practices.
Knowledge and Data Readiness	Organize approved knowledge and data required to support reliable agents.
Capability and Adoption Programme	Develop leaders, managers, specialists, employees and champions as deployment expands.
Performance Dashboard	Monitor service outcomes, operational value, adoption, quality, incidents and scale decisions.
Continuous Improvement Cycle	Use feedback and evaluation evidence to refine agents and identify new opportunities.

Measurement Framework: What Should a Federal Entity Track?

Performance Dimension	Illustrative Indicators
Government Service Outcomes	Response time, case turnaround, service consistency, customer experience and accessibility improvements.
Operational Productivity	Cycle-time improvement, administrative effort released, throughput, reduction in repeated processing or improved workflow visibility.
Quality and Reliability	Accuracy, completeness, correction rates, exception-handling quality and adherence to approved sources.
Human Capability	Employees trained by role, manager readiness, agent supervisors prepared, train-the-trainer capacity and adoption confidence.
Adoption and Usage	Number of operational workflows adopted, active appropriate users, workflow recurrence and user feedback.
Governance and Trust	Escalations, approvals, access compliance, data incidents, quality issues and audit/control findings where relevant.
Portfolio Progress	Opportunities discovered, use cases qualified, pilots executed, agents scaled and deferred projects appropriately managed.

Measurement Principle

Agentic AI progress should be measured by services and operations improved responsibly—not merely by the number of agents created.

A Practical First 100-Day Roadmap for a Federal Entity

The national two-year target creates urgency, but a ministry or federal authority can establish a credible implementation engine within its first 100 days.

Days 1–30: Align, Discover and Mobilize

Leadership Objectives

• Translate the national Agentic AI direction into entity-specific priorities.

• Establish governance sponsorship and implementation ownership.

• Begin workflow discovery across selected departments.

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MENTOR Support Activities

• Executive Agentic AI orientation session.

• Entity AI ambition and outcomes workshop.

• Department-manager discovery sessions.

• Initial service and operational workflow mapping.

• Opportunity longlist and preliminary risk screening.

• Stakeholder and capability-readiness assessment.

Deliverables

• Entity Agentic AI transformation charter.

• Leadership governance and sponsorship structure.

• AI opportunity map.

• Initial workflow inventory.

• Capability and adoption-readiness findings.

Days 31–60: Qualify, Prioritize and Build Capability

Leadership Objectives

• Select credible pilot opportunities.

• Prepare responsible owners and employee groups for practical adoption.

• Clarify controls required before agent design begins.

MENTOR Support Activities

• Agentic AI project qualification workshops.

• Value–risk–readiness scoring and prioritization.

• Pilot candidate selection and business cases.

• Role-based capability-building programme design.

• Manager and specialist applied AI workshops.

• Initial governance, oversight and evaluation design.

Deliverables

• Prioritized Agentic AI project portfolio.

• Selected pilot use cases and agent design briefs.

• Risk-tiering and human-oversight recommendations.

• Entity capability-building plan.

• Pilot measurement framework and baselines.

Days 61–100: Design, Pilot and Prepare Scale Decisions

Leadership Objectives

• Convert selected use cases into working agent pilots.

• Test value, quality, risk and adoption under controlled conditions.

• Determine the roadmap for the next implementation wave.

MENTOR Support Activities

• Detailed workflow and AI agent charter development.

• Knowledge and data readiness support.

• Agent build/configuration support in coordination with approved technology stakeholders.

• Pilot-user enablement and change support.

• Evaluation testing and controlled operating pilots.

• Impact analysis, lessons learned and scale recommendations.

Deliverables

• Piloted AI agent workflows.

• Evaluation and impact report.

• Refined governance and adoption model.

• Priority scale roadmap aligned to the two-year national transformation horizon.

Executive Takeaway

Within 100 days, an entity does not need to transform half of its operations. It needs to establish the capability to transform responsibly: leadership alignment, a qualified portfolio, trained owners, controlled pilots and evidence-based scale decisions.

What MENTOR Brings to the UAE Government Agentic AI Journey

The UAE’s initiative requires a combination of technology capability and practical transformation execution. Federal entities need partners who can work at the intersection of strategy, workflow, people, governance and implementation.

MENTOR is positioned to support this need through:

1. Business-Led AI Discovery

MENTOR begins with government objectives, service journeys and operational workflows—not with a preselected technology solution. This helps entities identify AI opportunities that matter to public value and organizational performance.

2. Practical Project Qualification

MENTOR applies structured analysis to help entities determine which use cases are ready to pilot, which require preparatory action and which demand more formal control before any agent implementation.

3. Capability Building and Change Management

MENTOR combines advisory services with professional development capability, enabling entities to move from AI awareness to role-based adoption, workflow ownership and sustained Human + AI ways of working.

4. Governed Agent Design and Pilot Support

MENTOR can help convert qualified opportunities into agent charters, workflow designs, human-in-the-loop models, evaluation plans, pilot programmes and implementation roadmaps—working in alignment with approved technical, security and governance requirements.

5. Measurement and Institutionalization

MENTOR can help entities establish performance indicators, adoption dashboards, learning feedback loops and portfolio-management practices that enable successful pilots to become sustained capability.

Positioning Statement

MENTOR supports UAE federal entities in converting the national Agentic AI ambition into practical implementation: identifying the right workflows, qualifying responsible AI-agent opportunities, building the capability of leaders and employees, designing governed pilots and helping scale AI-enabled service and operational performance.

A Necessary Boundary: Strategic Implementation Support Within the National Ecosystem

The UAE Government has appointed national strategic knowledge partners and established central governance structures for the federal Agentic AI programme. MENTOR’s role should therefore be understood clearly and credibly:

• MENTOR does not claim ownership of the national programme or central federal governance.

• MENTOR can support ministries and federal entities in applying the national direction within their services, departments and operating realities.

• MENTOR can complement national learning initiatives through applied, entity-specific workflow discovery, managerial enablement, adoption support and pilot execution.

• Agent development and deployment should be coordinated with the entity’s authorized technology, data, cybersecurity, privacy and governance stakeholders.

• Public reporting of client work, outcomes or named agent implementations should be subject to formal client authorization and confidentiality review.

This positioning strengthens credibility: it presents MENTOR not as a commentator on an ambitious national initiative, but as a practical execution and change partner equipped to help organizations deliver against it responsibly.

Conclusion: The Future of Government Will Be Built through Trusted Execution

The UAE has set an ambition that changes the conversation about public-sector AI globally. Moving 50% of government services and operations toward Agentic AI within two years is not simply a technology objective. It is a redefinition of how government capability, employee expertise, service quality and institutional responsiveness can work together.

The next challenge is execution.

Federal entities must now identify the workflows where Agentic AI creates real public value; qualify opportunities with discipline; prepare leaders and employees to work within Human + AI operating models; develop agents within appropriate boundaries; and measure the impact of change through service quality, productivity, reliability and trust.

The most successful entities will not be those that build the largest number of agents first. They will be those that develop the strongest capability to choose wisely, design responsibly, implement securely and improve continuously.

The UAE has defined the Agentic AI ambition. The institutions that lead in delivery will be those that turn that ambition into trusted workflows, capable people and measurable public value.