The Agentic State: Learnings for different jurisdictions

From Digital Government to Agentic Government

Governments have spent years moving services online. Forms, identity checks, and

payments that once required paper or counter visits can now be completed digitally. These changes matter, but they are only one stage in a longer shift. The next stage is not simply more online services; it is a different way for public institutions to organize work, make decisions, support people, and coordinate across boundaries.

An agentic state is one possible name for that next stage. It describes a public sector in

which digital agents can understand a request, identify the steps needed to resolve it, call approved tools, coordinate with other systems, and help complete an outcome. In this model, a person should not need to know which agency owns a service, which form applies, or which database contains the required information. The system should be able to guide the person, collect only what is needed, check eligibility, and route the work to the right place.

This article gives an objective view of what such a state could look like. It does not argue

that every jurisdiction should follow one design. Instead, it draws out general lessons that can apply in different legal, political, cultural, and administrative settings. The main lesson is simple: agentic government is not mainly about adding artificial intelligence to old processes.

It is about redesigning the foundations of public administration so that services can be safer, faster, more coordinated, and more accountable.

What an Agentic State Means

At its simplest, an agentic state is a public administration that can move from passive service delivery to active problem solving. Instead of requiring people to search for the right office, understand complex rules, and repeat the same information many times, government systems would help interpret the need, assemble the right steps, and complete the process through approved channels. The aim is not to remove human judgment from government.

The aim is to reserve human judgment for the moments where it matters most: complex

cases, rights-impacting decisions, disputes, exceptions, ethical trade-offs, and public

accountability.

The Core Shift: From Online Transactions to Coordinated Outcomes

Traditional digital services still usually start with the user. People must know what they need, find the right service, complete the correct form, and wait for processing. Even online, this can reproduce paper-era thinking and require the public to understand government structures designed for administrators rather than users.

An agentic model changes the starting point. It begins with a situation rather than a form. A person might say that they have lost work, started caring for a family member, moved address, opened a business, or experienced an emergency. The system would then interpret the situation, identify relevant public services, check what information is already held, ask only for missing details, and guide the person through the next steps. The service is organized around the outcome the person needs, not around the agency structure behind it.

This shift matters because many public needs do not fit neatly inside one agency. A single

life event can involve identity, payments, licensing, housing, health, education, tax,

employment, and regulation. In a conventional model, each institution handles its own part. In an agentic model, approved agents could coordinate across those boundaries. This does not mean agencies disappear. It means their systems become connected enough for the public to experience government as one coherent service network.

The lesson for any jurisdiction is that the value of agentic government is not only speed. It is coherence. A faster version of a fragmented process is still fragmented. The deeper goal is to reduce the number of times people must explain themselves, lower the need for specialist knowledge, and make public support easier to reach. This requires governments to design services around user journeys, shared data, common standards, and clear accountability across agencies.

Public-Facing Agents: A New Front Door to Government

The public-facing agent is the part of the agentic state that most people would see first. It could appear as a conversation, a voice interaction, a guided digital assistant, or another accessible interface. Its purpose is not simply to answer questions. Its purpose is to help people complete government tasks through a trusted channel that can connect to approved systems.

A mature public-facing agent would need several abilities. It would need to understand

natural language in plain terms. It would need to know which services exist and what rules

apply. It would need to verify identity where required, protect personal information, and keep a record of what it did. It would need to explain decisions in a way that a person can

understand. It would also need to know when to stop and refer matters to a human official.

The most useful public agents would support both reactive and proactive services. Reactive

services respond when a person asks for help. Proactive services identify when a person

may be entitled to support or may need to take action. For example, a change in life

circumstances might trigger an offer of information, a reminder, or an invitation to apply for an entitlement. However, proactive service must be handled carefully. People should knowwhy they are being contacted, what information was used, and how to opt out or seek human support.

The strongest lesson for any jurisdiction is that a single front door only works if the back

office is ready. A polished assistant that cannot access reliable information, call approved services, or update case status will quickly lose public trust. The user interface should therefore be treated as the visible layer of a wider operating model. The agent is only useful because it sits on identity systems, data access controls, service catalogs, communication protocols, and clear rules about authority.

Internal Government Agents: Changing How Public Servants Work

A great deal of attention is usually given to the public side of agentic government, but the deeper change may happen inside government itself. Public servants spend large amounts of time finding information, interpreting rules, checking documents, drafting advice, moving work between systems, and coordinating with other teams. Many of these tasks are necessary, but they also consume time that could be used for judgment, relationship management, policy design, and complex case work.

An internal government agent would help staff work through these tasks in a safer and more consistent way. It could answer procedural questions using approved sources, summarize long reports, draft letters, prepare briefings, compare policy options, check whether a case file is complete, or guide a staff member through a multi-step workflow. The value is not that

the agent replaces the public servant. The value is that it gives the public servant a better tool for navigating government’s own complexity.

For any jurisdiction, this creates a major opportunity to improve the quality of administration. When staff have fast access to accurate, sourced, and up-to-date information, they are less likely to rely on informal knowledge, outdated templates, or inconsistent local practice. An internal agent can also make institutional memory easier to reuse. Instead of knowledge remaining in inboxes, spreadsheets, and the heads of experienced staff, it can be turned into

structured guidance that helps new and existing staff make better decisions.

The most useful internal agents would not be general chat tools sitting outside the real work of government. They would be embedded in the systems and processes that staff already

use. A case officer could ask an agent to identify missing evidence in an application. A policy

analyst could ask for the legal basis for a proposed intervention. A service designer could

ask which existing components can be reused. A manager could ask for the status of a

portfolio of cases without manually checking several systems. In each example, the agent

does not just provide text; it helps complete a task through authorized tools.

Governed Internal AI Platforms: From Isolated Tools to Shared Capability

A jurisdiction that wants to use agents inside government should avoid allowing every

agency to buy or build its own disconnected tools. That path may produce quick wins, but it

also creates long-term problems. Different tools may handle data in different ways, use

different security controls, apply different standards, and produce outputs that cannot be

audited consistently. Over time, this can create a new generation of silos, even if each tool

appears useful on its own.

A governed internal AI platform offers a different approach. It gives public servants one

approved environment for using, building, testing, and reusing AI-assisted workflows. Staff

can access trusted knowledge sources, connect to approved systems, and work within clear

guardrails. The platform can include secure access controls, audit logs, model evaluation,

approved templates, reusable service components, and standard ways to connect agents to

government data. This makes innovation easier while reducing unmanaged risk.

The platform should support different levels of technical skill. A policy expert or lawyer may

need a no-code way to create an assistant that searches an approved knowledge base and

drafts a first version of advice. A business analyst may need low-code tools to map a

process, define decision points, and connect existing services. A software developer may

need full-code access to build more advanced agents, create new integration tools, or

support complex workflows. The key is that all three groups work within the same governed

environment rather than creating separate, incompatible systems.

This kind of platform also creates the basis for an internal marketplace. If one agency builds

a useful workflow, another agency should be able to discover it, assess whether it is

approved for reuse, and adapt it to local needs. Reuse is especially important because many

government processes share common building blocks: identity checks, eligibility rules,

document verification, notifications, payments, case tracking, and record keeping. A

marketplace helps each new project build on what already exists instead of starting again.

For any jurisdiction, the lesson is that internal AI adoption needs both freedom and

discipline. Freedom is needed because useful ideas often come from the people closest to

the work. Discipline is needed because public institutions handle sensitive information, make

decisions that affect rights, and must be able to explain their actions. A governed platform

can balance these needs by making the safe path the easy path. It allows teams to

experiment, but it also requires them to use approved data sources, standard security

controls, and transparent review processes.

Compliance Agents: From Periodic Audits to Continuous Assurance

Regulation is another area where an agentic model could change the relationship between

government and those it regulates. Many regulatory systems still depend on periodic

reporting, manual audits, document-heavy inspections, and after-the-fact enforcement. This

approach can be expensive for businesses and slow for regulators. It can also leave gaps,

because problems may only become visible after harm has already occurred.

A compliance agent would support a different model. Instead of asking organizations to

prepare large reports at fixed intervals, rules could be converted into machine-readable

requirements. Regulated entities could then run approved agents that check their own

systems against those requirements and generate evidence that they are complying.

Regulators could receive trusted signals, proofs, or alerts without needing to see every

underlying commercial or personal record.

The most important idea is not constant surveillance. A well-designed compliance model

should not give regulators unlimited access to business systems. Instead, it should use

privacy-preserving methods that allow a regulated entity to prove something without

disclosing more than is necessary. For example, an organization might be able to prove that

it meets a threshold, holds a valid license, has completed a required check, or has submitted

required data in the correct format, without exposing confidential operational details.

This would shift the role of the regulator. Rather than reviewing every file manually,

regulators could manage by exception. Agents could identify anomalies, missing

submissions, unusual patterns, or repeated failures. Human officers would then focus on the

cases that need investigation, judgment, or enforcement. This can make regulation more

targeted while also reducing the burden on compliant organizations.

For government, the lesson is that agentic compliance needs legal, technical, and

institutional design to advance together. Rules cannot simply be copied into code without

careful interpretation. Legal experts, regulators, technologists, industry representatives, and

public interest voices need to agree what each rule means in practice. They also need to

decide which obligations are suitable for automation and which still require human judgment.

A rule that is precise, objective, and frequently checked may be a good candidate. A rule

that depends on context, fairness, or professional judgment may need a different approach.

The strongest compliance agents would also create better feedback loops. If many

organizations struggle with the same requirement, the problem may not be poor compliance.

It may be that the rule is unclear, the reporting process is too complex, or the data standard

is badly designed. Continuous assurance can therefore help regulators improve the

regulatory system itself. It can show which rules work well, which create unnecessary

friction, and where guidance needs to be clearer.

Shared Technical Foundations: The Infrastructure Behind Agentic Government

An agentic state cannot be built service by service if every service creates its own data

store, integration pattern, security model, and AI toolset. That would only reproduce old

administrative silos in a newer technical form. The central lesson is that the visible agents

must be supported by shared foundations. These foundations are not glamorous, but they

determine whether agentic services can scale, remain safe, and keep working as technology

changes.

The first foundation is an orchestration and interoperability layer. This is the controlled

pathway through which agents, traditional systems, and service components communicate.

It should manage identity, authorization, traffic rules, message formats, monitoring, and audit

logs. Without this layer, each new agent would need a custom connection to every other

system it depends on. With it, authorized agents can communicate through a standard

channel, and government can see what is happening across the network.

The second foundation is an agent registry. A registry is a trusted directory of approved

agents and their capabilities. It records what each agent is allowed to do, which data it may

access, which actions it may take, who owns it, and whether it is currently active. This

matters because agentic government depends on dynamic coordination. An agent handling

a complex request must be able to discover which other agents can help, verify that they are

authorized, and pass work to them safely. A registry also allows government to suspend or

retire agents that are unsafe, outdated, or no longer approved.

The third foundation is a reliable government knowledge service. Agents that give advice or

support decisions need access to current laws, rules, policies, procedures, and public

guidance. If every agency maintains its own copy of this material, inconsistency is almost

guaranteed. A shared knowledge service should provide version-controlled, source-linked,

machine-readable information. When a rule changes, the update should happen once and

be available to all approved agents. This reduces the risk of outdated advice and makes it

easier to explain which rule or policy supported an answer.

The fourth foundation is a data mesh or similar federated data architecture. Governments

often hold data in many separate systems managed by different agencies. A data mesh

does not require all data to be moved into one large database. Instead, agencies remain

responsible for their own datasets but expose approved data products through standard

interfaces. Agents can then discover what data exists, request access through governed

channels, and receive only what they are permitted to use. This approach can support

coordination while preserving stewardship, privacy, and legal accountability.

The fifth foundation is compute infrastructure. Some workloads may be low risk and suitable

for scalable cloud environments. Others may involve sensitive personal information, critical

national systems, or confidential operational data. A mature jurisdiction will need a clear

method for deciding where different workloads run. This may involve a hybrid model, with

secure local infrastructure for the most sensitive tasks and scalable external capacity for less

sensitive work. The important point is that the choice should be governed by data

classification, risk, resilience needs, and cost, not by convenience alone.

For any jurisdiction, the practical lesson is to build the common rails early. Public-facing tools

may attract the most attention, but shared infrastructure is what turns small pilots into a

government operating model. If each agency builds separately, the result may be many

promising demonstrations and very little system-wide value. If the common foundations are

built well, each new service can reuse identity, data access, consent, logging, security, and

communication capabilities. This makes later services faster to deliver and easier to govern.

Data, Privacy, and Cybersecurity Safeguards

An agentic state depends on data, but trust depends on restraint. The fact that an agent can

access or combine information does not mean it should. Public institutions hold information

because people are required, by law or need, to provide it. That creates a higher duty than

ordinary commercial data use. Any jurisdiction considering agentic services must start with

the principle that personal information should be used only for clear public purposes, under

legal authority, with strong limits on access, retention, and reuse.

A practical safeguard is data classification. Not all information creates the same level of risk.

Public guidance, service descriptions, and general policy material can usually be handled

differently from personal records, health information, legal files, or national security data. A

mature architecture should classify information into clear levels and link each level to

approved processing rules. Low-risk material may be available to many agents. Sensitive

material may require masking, tokenization, or strict approval. Highly restricted material may

need to remain in isolated environments with dedicated controls.

Privacy-preserving design should be built into the workflow, not added after deployment.

Where possible, agents should receive only the minimum information needed to complete

the task. If a model only needs to know that a person meets an eligibility threshold, it should

not receive the full income history. If a form can be completed using a verified attribute, the

system should not expose the underlying record. This approach supports better service

while reducing the consequences of misuse, error, or breach.

Agentic systems also need strong data lineage. Officials and users should be able to know

which source was used, when it was accessed, what version applied, and how the

information influenced the answer or action. This matters for accountability. If an agent gives

incorrect advice because a source was outdated, the problem is different from an agent

misreading a valid source. Good lineage allows government to diagnose errors, correct

systems, and explain decisions.

Cybersecurity becomes more complex when agents can act across systems. Traditional

security models often assume that the main risk is a human user outside the network. In an

agentic environment, the system must also protect against compromised agents, malicious

instructions, unsafe tool use, forged identities, excessive permissions, and attacks that try to

trick a model into ignoring its instructions. The security model must therefore assume that no

user, device, service, or agent is automatically trusted.

Agent identity is the first control point. Every agent should have a verified identity, separate

from the human user or agency that created it, showing what it is, who operates it, what it

may do, and which systems it may contact. This allows other systems to check authority

before responding and preserves accountability if something fails.

Least privilege is the second control point. Each agent should receive only the permissions

needed for its approved function. Those permissions should be specific, time-bound where

possible, reviewed regularly, and checked at runtime as services, data, and tools change.

Audit logs are the third control point. Meaningful agent actions should leave a protected

record, including identity checks, data requests, tool calls, record changes, advice given,

escalations, and blocked actions. Logs should be detailed enough to support investigation

without creating unnecessary stores of sensitive information.

Testing and red-teaming are the fourth control point. Before an agent is used in a live

service, it should be tested against realistic cases, edge cases, and deliberate attempts to

make it fail. These tests should examine whether the agent follows its instructions, stays

within its permissions, cites the right sources, avoids unsafe tool use, and escalates

uncertain cases. Red-teaming should continue after launch because attackers, users, rules,

and models all change over time.

Incident response is the fifth control point. Governments should assume that some agents

will fail, behave unexpectedly, or be targeted by attacks. A clear response plan should define

how an agent can be paused, isolated, rolled back, or removed from service. It should also

define who must be notified, how affected users will be supported, and how lessons will be

fed back into design, training, procurement, and governance. Fast shutdown and recovery

are as important as careful approval.

Public redress is the sixth control point. People should not be trapped by an automated

process they cannot understand or challenge. Where an agent affects a person’s rights,

entitlements, obligations, or access to a service, the person should be able to see a useful

explanation, ask for human review, correct inaccurate information, and appeal where the law

allows. The easier it is to challenge a decision, the more likely the system is to earn public

confidence.

The lesson for any jurisdiction is that privacy and security cannot be treated as barriers to

agentic government. They are what make it possible. A system that cannot prove who acted,

why they acted, what data was used, and how a person can challenge the result will not be

trusted for important public services. Strong safeguards may slow early experimentation, but

they make later scaling safer, more legitimate, and more durable.

Governance, Accountability, and Oversight

An agentic state raises a basic question: who is responsible when an agent helps make a

decision, gives advice, or takes an action? This question cannot be left vague. In public

administration, responsibility must be clear because decisions can affect rights, money,

safety, access to services, and trust in government. The more capable agents become, the

more important it is to define who approves them, who monitors them, who can stop them,

and who answers when something goes wrong.

Governance should begin before an agent is built. A proposed service should be tested first

as a public problem, not as a technology idea. The design team should ask whether the

process is suitable for automation, which parts are objective, which parts require judgment,

what data is needed, what harms could occur, and what a person can do if they disagree

with the result. If a service cannot be explained clearly in human terms, it is unlikely to

become safe simply because it uses AI.

One useful method is to test the service with a human operator before building the agent. In

this approach, users interact with what appears to be an automated service, but trained staff

respond behind the scenes using the same information, boundaries, and tools that the future

agent would have. This reveals whether the service logic works before major technical

investment is made. If a skilled human cannot deliver the service under the proposed

constraints, an agent is unlikely to do it well.

Accountability should be tiered by risk. For high-impact decisions, agents should support

officials rather than replace them. The system may gather evidence, summarize rules, draft

reasoning, and identify risks, but a named human officer should review and approve the final

decision. That officer should be able to see the information used by the agent and should

remain legally and administratively responsible for the outcome. For low-risk services, an

agent may act more directly, but a service owner should still be responsible for overall

performance, complaint handling, and continuous improvement.

Oversight also requires evidence. Agents should not operate as hidden systems whose

outputs cannot be checked. Each important action should be linked to the sources used, the

instructions applied, the tools called, and the confidence or uncertainty involved. This does

not mean exposing sensitive internal logic to every user, but it does mean that authorized

reviewers can reconstruct what happened. Good oversight depends on traceable decisions,

not just good intentions.

A mature governance model should combine automated evaluation with human review.

Automated checks can test whether an answer is supported by approved sources, whether it

stays within policy, and whether it follows required formats. Human reviewers should

examine high-risk cases, unusual patterns, complaints, and samples of ordinary cases. This

mixed approach allows oversight to scale without pretending that automated evaluation is

enough on its own.

Jurisdictions should also establish a specialist alignment and assurance function. This team

would include policy experts, service designers, data specialists, security professionals, legal

advisers, and people who understand how models behave. Its role would be to review weak

outputs, update instructions, manage test datasets, investigate recurring errors, and run

adversarial testing. This function should not sit outside delivery as a distant reviewer only. It

should work closely with product teams so that lessons from oversight quickly improve the

services people use.

Redress must be designed as part of the service, not as a legal afterthought. People should

know when they are interacting with an automated system, how to ask for human help, how

to correct information, and how to challenge an outcome. If an agent is used in a decision

that affects a person’s rights or obligations, the explanation should be clear enough for the

person to understand the basis of the decision. A right to review is only meaningful if the

review is accessible, timely, and handled by someone with authority to change the outcome.

For any jurisdiction, the key lesson is that agentic government needs visible accountability.

The public should not be asked to trust a system just because it is modern or efficient. Trust

will depend on clear ownership, explainable decisions, independent review, meaningful

redress, and the ability to stop or change systems that fail. Governance is therefore not a

brake on innovation. It is the condition that allows powerful tools to be used in public

services without weakening democratic accountability.

People, Culture, and Leadership

Technology does not create an agentic state by itself. The transformation also depends on

people, leadership, and culture. Public institutions are not software companies, and they

should not try to become them. Their role is to use technology in ways that serve lawful

authority, public value, fairness, and trust. That requires leaders who understand both the

possibilities of AI and the realities of government: legal mandates, political accountability,

budget cycles, public scrutiny, workforce rules, and the need to serve all communities.

A jurisdiction that wants to move toward agentic government needs clear ownership at the

center. Someone must be responsible for the overall direction, the public mandate, the

investment case, and alignment across agencies. Without that role, each department may

pursue its own priorities and the system will fragment. Central leadership does not mean that

every decision must be made by one office. It means there is a clear owner for the shared

mission, the standards, the sequencing, and the trade-offs when agencies disagree.

There also needs to be strong technical leadership. The shared architecture, data pathways,

security rules, agent registry, knowledge services, and compute choices all require careful

design. These choices are too important to be left to separate procurements or short-term

projects. A senior technical function should set standards, review major designs, manage the

technology roadmap, and ensure that new services strengthen the common platform rather

than create new isolated systems.

At the same time, leadership must exist inside each agency. Central teams cannot

understand every service in detail. Departments and local bodies know their own users, legal

powers, operational pressures, and data constraints. They need leaders who can translate

the shared platform into real services, manage adoption, and ensure that staff have the skills

and confidence to use new tools safely. The strongest model is often a network: a central

platform and standards function, combined with empowered delivery leaders embedded

across government.

Culture is just as important as structure. Many public servants may worry that AI is being

introduced to replace them, reduce professional judgment, or weaken the quality of public

service. If those concerns are ignored, adoption will be shallow and resistance will grow. A

more constructive approach is to treat experienced staff as teachers of the system. Their

knowledge of edge cases, procedural judgment, common errors, and practical workarounds

is exactly what agentic systems need in order to be useful.

In this expert-as-teacher model, staff do not only use agents; they improve them. When a

public servant corrects an agent’s recommendation, explains why a document is incomplete,

or identifies an exception that the system missed, that correction can become a learning

signal for future improvement. This helps staff see the tool as a shared assistant rather than

an external system imposed on them. It also keeps institutional knowledge inside the public

service, where it can be governed and reused responsibly.

Training should go beyond basic AI awareness. Staff need to understand what agents can

do, where they fail, how to check sources, when to escalate, and how to protect sensitive

information. Managers need to understand how to redesign workflows, measure productivity

without creating perverse incentives, and avoid automation bias. Senior leaders need to

understand investment choices, governance risks, and the difference between a promising

demonstration and a service that is ready for public use.

A center of excellence or similar shared capability can help build this capacity. Its role should

be practical, not ceremonial. It can provide reference designs, reusable components, testing

methods, procurement guidance, training materials, and hands-on support for agencies

starting new projects. It can also maintain a pipeline of ideas, help teams move from proof of

concept to live service, and capture lessons from failures as well as successes. This kind of

function is most valuable when it accelerates delivery while improving quality.

For any jurisdiction, the lesson is that an agentic state is a workforce transformation as much

as a technology transformation. It requires leaders who can set direction, technologists who

can build common foundations, service owners who can deliver outcomes, and public

servants who are trusted to shape the tools they use. The aim should be to make

government more capable, not less human. If staff are brought into the change as experts,

reviewers, designers, and teachers, agentic systems are more likely to improve public

service rather than simply automate its weakest parts.

Use-Case Selection and Piloting

One of the most important choices for any jurisdiction is deciding where agentic tools should

be used first. Not every public service needs an AI agent. Some problems can be solved

with a better form, a clearer website, a workflow rule, or a simple data connection. A

government that uses advanced agents for simple tasks may create needless cost, risk, and

complexity. A government that avoids agents where they are genuinely useful may miss

opportunities to improve services and reduce pressure on staff.

A useful principle is minimum viable intelligence. This means using the least complex tool

that can solve the problem safely and well. If a task only requires finding and formatting

information from a trusted source, a retrieval tool may be enough. If a task requires checking

several conditions and preparing a draft response, a tool-using assistant may be suitable. If

a task spans several agencies and requires planning, sequencing, and coordination, a multi-

agent workflow may be justified. The design should match the problem, not the excitement

around the technology.

The first step is to find the real bottleneck. Teams should map the service from end to end

and ask where the main load sits. Is the problem that users cannot find the right service? Is it

that staff must check many documents by hand? Is it that agencies cannot share information

quickly? Is it that rules are unclear? The answer matters because each problem needs a

different response. An agent should not be used to hide a poorly designed process. It should

be used where interpretation, coordination, or repeated checking creates a real barrier.

Once the bottleneck is clear, teams should break the workflow into smaller tasks. Each task

should be described in plain language, with a clear input, action, and output. For example, a

workflow might include checking whether a document is present, confirming that a date is

valid, comparing two names, identifying whether a rule applies, drafting a response, and

asking a human officer to review an exception. Breaking the work down in this way makes it

easier to see which parts can be automated, which parts need human judgment, and which

parts should be redesigned before any technology is added.

The best candidates for automation are tasks with verifiable outputs. A task is verifiable

when another person or system can check whether the result is correct. A license is current

or expired. A required field is complete or missing. A document has been submitted or has

not been submitted. A value matches an approved register or does not match. These tasks

may still require careful handling, but they are different from tasks that depend on

professional judgment, public interest balancing, cultural context, or discretion.

After tasks have been separated, teams should assign each one an appropriate level of

autonomy. Some may need only a knowledge search; others may need a tool-using

assistant that retrieves information, compares facts, and prepares a draft. More complex

services may need an orchestrator that can plan steps and call specialist agents in

sequence.

A simple scoring method can help make these choices consistent. Teams can score a

proposed use case against factors such as user value, staff time saved, urgency, legal risk,

data readiness, technical complexity, cross-agency dependency, and public trust impact. A

high-value use case with clear data, low legal risk, and a measurable burden may be a

strong early candidate. A high-risk use case with unclear rules, poor data, and major rights

impacts may need more policy work before any agent is built.

Workflow decomposition also exposes hidden prerequisites. A team may discover that the

required data is not available through an approved interface, that two agencies use different

definitions for the same term, that the legal authority for data sharing is uncertain, or that no

one owns the end-to-end service. These findings are valuable. They show what must be

fixed before a pilot can succeed. In this sense, use-case selection is not only about choosing

pilots; it is also a way of mapping the deeper reforms needed for agentic government to

work.

A structured piloting pipeline helps governments avoid a common trap: too many trials, too

little learning, and no clear route to scale. Pilots should not be treated as one-off

demonstrations. They should be part of a managed portfolio that tests real service problems,

gathers evidence, reuses components, and either moves successful ideas toward production

or stops weak ideas early.

The first stage is intake. Any agency, team, or public servant should be able to propose a

use case, but every proposal should answer the same basic questions: what problem is

being solved, who is affected, what outcome would improve, what data is needed, what

authority applies, what risks are involved, and who will own the service after the pilot.

The fourth stage is proof of concept. At this point, the goal is not to build a finished service.

The goal is to test whether the core idea works. A proof of concept should use real or

realistic data, involve the staff who understand the process, record failure modes, and test

whether human users can understand and trust the outcome. It should have a clear time limit

and a clear decision point at the end. If the idea does not work, stopping it should be seen

as success, because the pilot has prevented a larger failure.

The fifth stage is a minimum viable service. This is different from a proof of concept because

it is designed for limited real-world use. It should include basic security controls, audit logs,

user support, service ownership, and clear boundaries on what the agent may do. It should

also include a human fallback path. The purpose is to learn how the service performs under

real conditions without exposing the public or the agency to uncontrolled risk.

The sixth stage is production readiness. Before scaling, a service should meet defined

gates. These may include legal approval, data protection review, security testing,

accessibility testing, operational support, monitoring, incident plans, model evaluation, and

evidence that users benefit. A service should not move to production just because the pilot

was impressive. It should move because it has proved that it can be operated safely,

explained clearly, and maintained over time.

The final stage is reuse and learning. Every pilot should leave something behind: a reusable

connector, a tested prompt pattern, a clean dataset, a service design, a policy interpretation,

a risk register, or a lesson about what not to do. These assets should be published inside the

government’s approved environment so that other teams can discover and adapt them. This

is how pilots become system learning rather than isolated experiments.

For any jurisdiction, the lesson is that piloting must be disciplined but not slow. The aim is to

let useful ideas move quickly while stopping weak ideas before they consume too much

attention. A good pipeline gives agencies a clear path from idea to live service, while making

sure each step adds evidence, improves safeguards, and strengthens the shared platform.

The best pilots do not only prove that one service can work. They teach the whole system

how to build the next service faster and better.

Phased Implementation Roadmap

A jurisdiction should not treat the move toward agentic government as a single technology

project. It is better understood as a staged transformation of public administration. Each

stage should build capability, reduce uncertainty, and create evidence for the next stage.

Trying to jump straight to highly autonomous, cross-agency services may create risk before

the foundations are ready. Moving too slowly, however, may allow fragmented pilots and

incompatible tools to become the next generation of legacy systems.

The first phase should focus on exploration and establishment. During this phase, the

government should build the common foundations that later services will depend on. This

includes an agent registry, an approved communication protocol, a secure integration layer,

an initial data catalog, a knowledge service, and basic compute arrangements for different

levels of data sensitivity. It should also clarify who owns the program, who owns the

technical architecture, who owns data products, and who has authority to approve or stop an

agentic service.

This first phase is also the right time to launch low-risk internal tools. Internal agents can

help public servants find trusted information, summarize documents, draft first versions of

advice, and test workflow ideas. These tools build AI literacy across the public sector while

limiting public-facing risk. They also give government early evidence about adoption barriers,

data quality problems, training needs, and the kinds of guardrails staff require.

The second phase should focus on deployment and scaling. By this point, the shared

foundations should be stable enough for selected services to move from pilot to production.

Public-facing agents can begin to support more services, but each new service should

connect through the common architecture rather than creating its own path. Legacy systems

should be connected in priority order, starting with the datasets and registries that are reused

across many services. Production standards should become mandatory, including role-

based access, audit logging, protected runtime environments, security testing, accessibility

testing, and human fallback.

This phase should also introduce more demanding use cases, such as cross-agency

workflows and compliance pilots. A government may choose one regulatory domain where

rules are clear, data is available, and industry engagement is possible. It can then test

machine-readable rules, firm-side compliance tools, regulator dashboards, and exception-

based oversight. The point is not to automate regulation all at once. The point is to learn how

legal rules, technical evidence, and institutional trust interact in a controlled environment.

The third phase should focus on integration and anticipation. This is where the full agentic

model begins to appear. Services can move from single-agency transactions to coordinated,

multi-agency outcomes. Agents can assemble steps dynamically, use approved data

products across boundaries, and support people through complex life events. Proactive

services may also become possible, but only where consent, transparency, legal authority,

and redress are strong enough. Proactivity should be earned through trust, not assumed

because the technology can do it.

The roadmap should not be treated as a fixed timetable. Different jurisdictions will move at

different speeds depending on their legal system, digital maturity, public trust, procurement

rules, workforce capacity, and political conditions. Some may begin with internal productivity

tools. Others may begin with a high-volume public service. Others may start with regulatory

modernization. What matters is that each phase strengthens the foundations needed for the

next one, rather than creating isolated success stories that cannot be reused.

The most important sequencing lesson is to build visible value and invisible infrastructure

together. If a government only builds infrastructure, it may lose support because people

cannot see results. If it only builds visible agents, it may create services that cannot scale

safely. A balanced roadmap delivers early benefits while also creating the common rails,

standards, governance, and workforce capability that allow the next wave of services to

arrive faster and with less risk.

Integration and Anticipation: The Mature Agentic State

Integration means that services can work across institutional boundaries. A person might

need support that involves income, housing, education, licensing, health, employment, or

legal status. A business might need to understand a new rule, update records, apply for

permission, and submit evidence to several public bodies. In a mature agentic model, the

person or business would not be expected to start separate transactions with each agency.

An approved agent would identify the relevant steps, call the right systems, and coordinate

the sequence through a single guided experience. This does not mean all authority becomes centralized. Agencies would still own their legal powers, specialist knowledge, data stewardship, and operational responsibilities. The difference is that their capabilities become discoverable and reusable through common infrastructure. An agent can find the correct service component, confirm that it is authorized, pass the right information, and record what happened. Integration therefore depends on disciplined decentralization: agencies keep responsibility for their domains, while the wider system provides the common rails that allow those domains to work together.

Anticipation is the second part of this mature stage. A reactive service waits for someone to

ask. An anticipatory service recognizes that a person may need support, information, or

action before they know which service applies. This can be valuable where a life event,

business change, or regulatory update creates a clear public duty or entitlement. The aim is

not to make government intrusive. The aim is to reduce avoidable burden by offering timely

help through transparent and lawful means.

There are different levels of anticipation. The simplest form is event-based. If a known event

is recorded, the system can offer a known service. This is useful but limited. A more

advanced form is pattern-based. In that model, several signals may suggest that a person or

organization needs guidance, even though no single event gives the full picture. Pattern-

based anticipation is more powerful, but it is also more sensitive. It must be governed by

clear legal authority, data minimization, strong consent settings where appropriate,

explainability, and easy access to human help.

A mature jurisdiction should therefore set strict conditions for proactive services. The user

should know why the offer was made, which rule or entitlement it relates to, what information

was used, and whether action is optional or required. The system should distinguish clearly

between public service support and any external or commercial offer that may appear

alongside it. People should be able to decline an offer, correct the information behind it, and

ask a human officer to review the matter. Without these controls, anticipation can quickly feel

like surveillance.

Integration and anticipation also require stronger performance monitoring than traditional

digital services. Leaders should be able to see whether agents are completing tasks

correctly, whether users understand the advice they receive, whether human officers are

overriding recommendations, whether appeals are rising, and whether certain communities

are experiencing worse outcomes. Monitoring should include technical measures, service

measures, legal measures, and user experience measures. A fast system that produces

unfair or confusing outcomes is not a successful system.

The final feature of this stage is adaptability. The agentic state will not be stable in the same

way as a traditional IT system. Models will change, standards will evolve, new risks will

appear, and public expectations will shift. A mature jurisdiction should therefore build review

cycles into the system. Agents should be retested, permissions should be reviewed, rules

should be updated, and weak services should be improved or retired. The operating model

should assume constant learning rather than one-time delivery.

For any jurisdiction, the lesson is that integration and anticipation are not starting points.

They are outcomes earned through foundations, safeguards, governance, and trust. A

government that tries to become proactive before it can explain decisions, protect data, or

coordinate across agencies will likely create public concern. A government that builds these

foundations carefully can move toward services that are more humane, less burdensome,

and more responsive without losing accountability.

Lessons for Any Jurisdiction

The move toward agentic government is not a race to deploy the most advanced technology.

It is a test of whether public institutions can redesign themselves around outcomes, trust,

and shared capability. The experience described in this article points to lessons that any

jurisdiction can adapt, regardless of its size, administrative model, or current level of digital

maturity.

The first lesson is to treat architecture as policy. Technical choices about identity, data

access, interoperability, logging, compute, and agent discovery are not back-office details.

They shape how government power is exercised. If architecture is fragmented, services will

remain fragmented. If access rules are weak, privacy will be weak. If audit trails are

incomplete, accountability will be incomplete. Jurisdictions should therefore make key

architectural choices visible to senior leaders and connect them to public values, legal

duties, and service outcomes.

The second lesson is to build common foundations before complexity becomes permanent.

Many governments already have isolated digital services, separate databases, and different

technology contracts. Agentic AI can either make this worse or help solve it. If every agency

builds its own agents, registries, connectors, and knowledge stores, the public sector will

create a new layer of silos. Shared foundations should therefore be built early, even if they

are not immediately visible to users. They lower the cost of future services and make

governance easier.

The third lesson is to keep humans responsible where public judgment matters. Agents can

help gather evidence, check rules, identify missing information, draft explanations, and

suggest next steps. But where a decision affects rights, obligations, safety, eligibility, or

public confidence, a person should remain accountable. This does not mean every task must

stay manual. It means the boundary between automated assistance and human decision-

making must be clear, documented, and understood by the people affected.

The fourth lesson is to design for redress from the beginning. People should not have to fight

the system to speak to a person, correct a record, or challenge an outcome. A useful agentic

service should make review pathways clear and accessible. It should show the basis for

advice or action in plain language and keep enough records for an independent reviewer to

understand what happened. Redress is not a secondary feature; it is part of the service

itself.

The fifth lesson is to use the least complex tool that can solve the problem. Agentic systems

are powerful, but power is not the same as usefulness. Some problems need a simple rule, a

better web page, a cleaner dataset, or a reliable API. Others need a tool-using assistant or a

coordinated workflow across agencies. Governments should assess each use case carefully

and choose the level of automation that matches the task, risk, and public value.

The sixth lesson is to build trust through evidence, not slogans. Public confidence will

depend on whether the system works, whether it can explain itself, whether errors are fixed,

and whether people feel fairly treated. Governments should measure override rates,

unsupported answers, appeal times, user satisfaction, service completion, data incidents,

and fairness across communities. These measures should be reviewed regularly and used

to improve services.

The seventh lesson is to make public servants part of the change. Staff understand the real

work of government: the exceptions, the unwritten risks, the common mistakes, and the

judgment calls that do not appear in process maps. If they are treated only as users of a

system built elsewhere, valuable knowledge will be lost. If they are treated as teachers,

reviewers, and co-designers, the system can learn from their expertise and gain legitimacy

inside government.

The eighth lesson is to pair central direction with local ownership. Shared standards,

platforms, and governance need central leadership. Real services need agency-level

expertise and accountability. A useful model is not total centralization or total

decentralization, but a networked approach: one set of rails, many service owners, clear

decision rights, and strong mechanisms for resolving conflicts.

The ninth lesson is to treat pilots as system learning. Pilots should not be allowed to multiply

without discipline, but they should not be smothered by process either. A good pilot pipeline

helps teams test ideas quickly, gather evidence, build reusable assets, and stop weak ideas

early. The best pilots improve the shared platform and make the next service easier to build.

The tenth lesson is to move in phases. Early work should prove value and build foundations.

Middle-stage work should scale selected services and strengthen production controls. Later

work can support more integrated and proactive services, but only once the jurisdiction has

earned enough trust, technical maturity, and governance capability. The right pace will differ,

but the sequence matters.

Taken together, these lessons suggest that an agentic state is best understood as a

disciplined operating model, not a technology brand. Its success depends on shared

infrastructure, clear roles, careful automation, strong privacy practice, accountable decision-

making, and a public workforce that is trusted to shape the system. Any jurisdiction can

begin from where it is, but none can avoid these basic requirements if it wants agentic

government to be safe, useful, and legitimate.

What an Agentic State Could Look Like

An agentic state would not be a government where machines replace public institutions or

where algorithms make every important decision. A better way to understand it is as a public

sector that has learned to coordinate itself through trusted digital agents, shared

infrastructure, and clear human accountability. In that model, people would no longer need to

navigate the hidden machinery of government on their own. They could explain a need once,

receive guidance in plain language, and move through several services as part of one

connected journey.

The most attractive version of this future is practical rather than dramatic. A person dealing

with a major life event could be shown the services that matter, asked only for information

that is not already available through lawful channels, and offered human support when the

situation is complex. A business could understand its obligations without hiring specialist

help for every routine requirement. A regulator could focus on serious risks rather than

reviewing large volumes of low-value paperwork. A public servant could spend less time

finding information and more time exercising judgment.

This is not a simple destination. It requires governments to rethink service design, data

governance, cybersecurity, procurement, leadership, workforce practice, and legal

accountability at the same time. It also requires restraint. The purpose of agentic

government should not be to automate every process, collect every signal, or remove every

human step. The purpose should be to make public administration easier to use, faster

where speed helps, more consistent where rules are clear, and more human where

judgment is required.

The main risk is that jurisdictions treat agentic AI as a layer of tools placed on top of old

systems. If that happens, the result may be more chat interfaces, more pilots, and more

technical complexity, but not better government. The stronger path is to treat agentic

government as an operating model. That means common rails, open standards, reliable

data, strong privacy controls, clear decision rights, and public servants who are actively

involved in shaping and correcting the system.

Any jurisdiction can begin with modest steps. It can identify high-burden services, map

workflows, separate verifiable tasks from judgment-based tasks, build trusted knowledge

services, create an agent registry, and pilot internal assistants before moving into higher-risk

public services. It can also establish review rights, audit trails, and incident response

processes before they are urgently needed. These early choices may seem technical, but

they shape the level of trust the public will place in the system later.

The objective view is therefore neither optimistic nor pessimistic. An agentic state could help

government become more responsive, less fragmented, and easier to deal with. It could also

create new risks if it is built without safeguards, public explanation, or human responsibility.

The difference will come down to design choices. Jurisdictions that build slowly enough to

earn trust, but quickly enough to learn, will be best placed to benefit. The future agentic state

should not be measured by how autonomous its agents become. It should be measured by

whether people receive better service, whether public servants make better decisions,

whether rights are protected, and whether government remains answerable for the power it

exercises.