From Connected AI to Autonomous Action: AI Agents and Agentic Workflows in the Public Sector

In our previous discussion of Model Context Protocol, we explored how AI systems can be securely connected to organisational data and tools. But connectivity alone isn't enough to transform public service delivery. The next frontier lies in AI agents and agentic workflows—systems that don't just retrieve information, but autonomously reason, plan, and execute complex tasks from start to finish. This capability represents a fundamental shift from AI as a responsive assistant to AI as a proactive collaborator. For UK public sector organisations grappling with resource constraints, increasing demand, and the need for efficiency, understanding and deploying agentic AI could be transformative.

What Are AI Agents?

AI agents are autonomous software components that can reason about goals, call tools and APIs, collaborate with other agents, and adapt dynamically. Unlike traditional AI chatbots that simply respond to queries, agents can break down complex objectives into steps, make decisions about what actions to take, and work iteratively towards a goal with minimal human intervention. Think of the difference this way: Traditional AI Assistant:

User: "What planning applications have been submitted for High Street this year?"
AI: Returns a list from the database

AI Agent:

User: "Process the new planning application for 45 High Street"
Agent:
1. Retrieves the application documents via MCP
2. Checks the proposal against local plan policies
3. Identifies statutory consultees based on the development type
4. Generates consultation letters with correct legal wording
5. Creates calendar entries for the determination deadline
6. Updates the case management system
7. Sends the applicant an acknowledgement
8. Reports back: "Application validated and consultations initiated. Determination due by 15th March 2025."

In an agentic workflow, AI agents are granted autonomy to solve problems independently by analysing a series of steps, reflecting on their work during each stage, and reaching a solution.

The Architecture of Agentic Workflows

An agentic workflow consists of several key components:

1. Reasoning and Planning

The agent uses an LLM to understand the goal, break it into logical steps, and determine what information or actions are needed. This isn't a fixed script—the agent adapts its approach based on what it discovers.

2. Tool Use via MCP

This is where Model Context Protocol becomes crucial. The agent uses MCP connections to access databases, execute searches, call APIs, or trigger actions in connected systems. MCP provides the agent's "hands" to interact with the real world.

3. Memory and Context

Agents maintain working memory of what they've done, what they've learned, and what remains. This allows them to build on previous steps rather than treating each action in isolation.

4. Reflection and Error Correction

Sophisticated agents can evaluate whether their actions achieved the intended result. If something failed or produced unexpected output, they can adjust their approach and try again.

5. Multi-Agent Collaboration

Complex workflows might involve multiple specialised agents working together. A "triage agent" might assess a citizen query and route it to specialist agents for housing, benefits, or social care, with a "coordination agent" ensuring the overall outcome meets the citizen's needs.

Agentic Workflows in UK Public Services: Practical Applications

Let's explore how agentic workflows could revolutionise specific public sector functions:

Use Case 1: FOI Request Processing at Central Government

The Traditional Process:

FOI request arrives by email
Officer manually logs it in the case management system
Officer reads through to understand scope
Officer identifies which departments hold relevant information
Officer emails each department requesting information
Officer chases responses as deadline approaches
Officer reviews all responses for exemptions and redactions
Officer compiles final response and sends to requester
Officer updates system and closes case

Time: 15-20 hours of officer time spread over 18 days The Agentic Workflow: An FOI Agent, connected via MCP to email, case management, document repositories, and departmental systems:

Receives and categorises the incoming FOI request automatically
Creates a case record with all relevant metadata and deadline calculations
Analyses the request to identify the likely location of relevant information
Queries connected systems across departments for potentially responsive documents
Applies preliminary exemption screening based on established FOI case law and departmental guidance
Flags sensitive content requiring human review (e.g. ministerial correspondence, ongoing investigations)
Generates a draft response with properly cited exemptions
Routes to appropriate officers for approval with clear indication of which sections need human judgement
Updates the case system with all actions taken

Officer Time: 2-3 hours reviewing flagged items and approving response Total Time: 5 days Crucially, the agent doesn't replace human decision-making on complex exemptions, but it eliminates the administrative burden, allowing officers to focus their expertise where it matters most.

Use Case 2: Benefits Assessment at DWP

The Challenge: Assessing benefit entitlement requires gathering information from multiple sources, checking eligibility against complex and frequently changing rules, calculating entitlements, and ensuring applicants receive everything they're eligible for. The Agentic Workflow: A Benefits Assessment Agent ecosystem: Intake Agent:

Receives the application via online form or scanned paper
Extracts and validates all required information
Identifies missing information and generates personalised requests to the applicant
Routes complete applications to the Assessment Agent

Assessment Agent:

Via MCP, retrieves relevant information from HMRC (income), local authority (council tax band), DBS (if relevant), and NHS (for health-related benefits)
Applies current eligibility rules across all benefit types
Calculates entitlements, including checking for "passport benefits" eligibility
Identifies if the applicant qualifies for benefits they haven't applied for
Generates a comprehensive assessment report

Decision Agent:

Reviews the assessment for any exceptional circumstances or edge cases
Flags complex cases for human decision-makers with specific questions
For straightforward cases, prepares decision letters in plain English
Updates payment systems with approved amounts and schedules

Proactive Support Agent:

Monitors for life events that might trigger changes (e.g., child reaches age threshold, employment status changes)
Automatically initiates reassessments when appropriate
Sends advance notifications before benefits are due to change or end

Impact: Faster processing, fewer errors, proactive identification of additional entitlements, and better citizen experience—whilst ensuring complex or borderline cases still receive expert human oversight.

Use Case 3: NHS Discharge Planning

The Challenge: Safe hospital discharge requires coordinating multiple services (transport, medications, GP notification, district nursing, social care, equipment delivery), often causing delays and readmissions when not properly orchestrated. The Agentic Workflow: A Discharge Coordination Agent, working within the NHS trust's clinical information governance framework: Assessment Phase:

Monitors patient records for discharge indicators
Reviews care needs assessments and medical notes
Identifies required post-discharge support services
Checks medication requirements and dispensing status

Planning Phase:

Via MCP, checks availability of required services (district nursing capacity, social care assessment slots, equipment stores)
Generates a coordinated discharge plan with optimal timing
Identifies potential obstacles (e.g., prescription not yet dispensed, transport not booked)
Proposes solutions and alternative arrangements

Coordination Phase:

Books required services in the correct sequence
Generates and sends notifications to all parties (GP, community services, family, patient)
Creates medication lists and discharge summaries in required formats
Schedules follow-up appointments

Monitoring Phase:

Tracks completion of all discharge tasks
Alerts staff to any incomplete steps as discharge time approaches
After discharge, monitors for warning signs in connected systems that might indicate complications
Flags patients at high risk of readmission for proactive follow-up

Human Role: Clinical staff make all medical decisions and communicate directly with patients and families. The agent handles the administrative orchestration, ensuring nothing falls through the cracks.

Implementation Considerations for Public Sector Agentic AI

Deploying agents safely and effectively requires careful consideration of several factors:

Governance and Accountability

Who is responsible when an agent makes a mistake? Establish clear governance frameworks that specify:

Which decisions agents can make autonomously
Which decisions require human approval
How agent actions are logged and audited
Processes for reviewing and improving agent performance

Transparency and Explainability

Citizens and staff need to understand when they're interacting with an agent and how decisions were reached. Design systems that:

Clearly identify automated processes
Provide reasoning trails showing how conclusions were reached
Allow humans to challenge or override agent decisions
Maintain human oversight for decisions that significantly affect citizens' rights or entitlements

Security and Privacy

Agentic systems with broad access to data and tools present new security considerations:

Implement robust authentication and authorisation for agent access to systems
Ensure agents respect existing data classification and information governance rules
Monitor agent behaviour for anomalies that might indicate compromise or malfunction
Design agents to handle personal data in compliance with UK GDPR

Incremental Deployment

Start with well-bounded, lower-risk use cases:

Phase 1: Agents that research and draft recommendations for human approval
Phase 2: Agents that execute routine administrative tasks with human oversight
Phase 3: Agents operating autonomously on clearly defined, repetitive tasks
Phase 4: Multi-agent workflows handling complex, end-to-end processes

Human-Agent Collaboration

The goal isn't to replace public servants but to augment them. Design for effective collaboration:

Agents handle repetitive, rules-based tasks, freeing humans for complex judgement
Agents surface relevant information, humans apply context and discretion
Agents maintain consistency, humans handle exceptions and edge cases
Agents work 24/7, humans provide oversight and continuous improvement

The Operational Benefits

When deployed thoughtfully, agentic workflows can deliver substantial benefits: Speed: Tasks that took days can be completed in hours or minutes, with agents working continuously rather than within office hours. Consistency: Agents apply rules uniformly, reducing variation in decision-making and ensuring all relevant factors are considered. Scalability: Agents can handle volume spikes without additional staffing, particularly valuable for seasonal or event-driven demand. Proactivity: Unlike reactive systems, agents can identify issues before they become problems—spotting incomplete applications, triggering preventive actions, or flagging citizens who might need additional support. Resource Optimisation: By eliminating administrative overhead, skilled staff can focus on complex cases, citizen interaction, and service improvement. Audit and Quality: Every agent action is logged, creating comprehensive audit trails and data for continuous improvement.

Challenges and Risks

Implementing agentic AI isn't without challenges: According to McKinsey's 2025 Global AI Trust Survey, the number one barrier to AI adoption is lack of governance and risk-management tools. Organisations must invest in:

Robust testing regimes to identify edge cases and failure modes
"Circuit breakers" that halt agent activity if anomalies are detected
Regular reviews of agent decision-making for bias or drift
Change management to help staff understand and trust agent capabilities

Additionally, agents can sometimes pursue their goals in unexpected ways or fail to recognise when circumstances require human judgement. This is why starting with human-in-the-loop approaches is crucial.

The Strategic Imperative

The UK public sector faces a perfect storm: rising demand for services, constrained budgets, and a workforce stretched thin. Traditional approaches—hiring more staff or asking existing staff to do more—are increasingly untenable. Agentic AI offers a different path. By automating not just individual tasks but entire workflows, whilst maintaining human oversight where it matters, organisations can deliver better services with existing resources. The alternative—continuing to rely solely on manual processes—risks declining service quality and staff burnout.

Moving Forward

To realise the potential of agentic workflows:

Build the foundation: Ensure robust MCP connections to your key systems, high-quality data, and clear process documentation
Identify the right use cases: Look for high-volume, rules-based processes with clear success criteria
Start small, learn fast: Pilot with a single agent in a contained environment, measure results rigorously, and iterate
Invest in governance: Establish frameworks before deployment, not after
Engage your workforce: Include frontline staff in design—they understand the work best and will be the agents' colleagues

The technology is maturing rapidly. The question for public sector leaders is not whether agentic AI will transform service delivery, but whether your organisation will lead or follow this transformation. By starting now, with careful planning and appropriate safeguards, you can position your organisation to deliver better outcomes for the citizens you serve.