Agentic AI represents a shift in how we understand the role of machines in decision-making. Unlike traditional AI systems that await a human instruction, agentic AI is driven by goals rather than tasks. We tell it what we want to achieve, and it decides how best to get there. That independence makes it immensely powerful, but also presents new ethical, operational and cultural challenges.
For government, this presents both a new opportunity and a significant challenge. To explore what agentic AI means for the civil service in practice, Civil Service World and Capita convened a roundtable of senior civil servants to discuss the opportunities and challenges of adopting these systems in government. Hosted under the Chatham House Rule, their message was clear: agentic AI is coming. The question is how we prepare for it in a way that is intelligent, responsible, and with a focus on public good.
Opportunities for agentic AI in government
There is no doubt that agentic AI could revolutionise the way government operates. Imagine public services that adapt in real time to individual needs, tailoring support with precision and speeding up delivery where it matters most. Agentic systems can manage administrative drudgery, freeing civil servants to focus on the strategic, human decisions where they are most valuable.
Participants agreed that agentic AI has the potential to help make sense of the legacy systems that have long hampered digital transformation across Whitehall. Many departments remain tied to ageing platforms that do not integrate easily and often resist change. Sorting through these systems – understanding what is there, what is useful, and what is redundant – is an ongoing challenge. While agentic AI will not solve this by simply sitting on top of outdated infrastructure, it could play a key role in navigating, interpreting and rationalising what exists. The aim isn’t to mask complexity with automation, but to work through it in a way that is more intelligent and, ultimately, more strategically.
The key challenges
Alongside these opportunities, agentic AI presents several challenges for its use in the complex setting of government. Some are an extension of wider challenges with AI adoption.
First, the procurement dilemma. Current systems are risk-averse by design, focused on minimising cost and error. Innovation, by contrast, is inherently risky. That disconnect means new technologies often struggle to get off the ground. Roundtable participants spoke of AI procurements that technically met their briefs but delivered little real-world impact.
Then there is cyber security. As criminals become more sophisticated in their use of AI, the threat to digital services grows – agentic AI, with its autonomy and system access, raises the stakes even further. What happens if it is compromised? Can we still trust the outcomes? Who is ultimately accountable?
Transparency is another issue. If a system makes decisions that even its developers cannot fully explain, how can government justify those decisions to the public? Furthermore, one participant raised the critical issue of liability. If something goes wrong, or if harm is caused, who takes the blame?
People at the centre of change
Attendees also raised a number of workforce challenges, primarily around the need to build skills but also confidence around AI and agentic AI. Participants reported a mix of enthusiasm and fear among their colleagues across government with some officials welcoming the opportunity to reduce administrative burdens while others worry about their own jobs and the implications as AI systems begin to make decisions.
There is also a wider challenge around adapting to work with a hybrid team - managers will need to lead AI agents, agentic AI, as well as human team members. This change, the table noted, will require new skills and cultures – creating space for curiosity rather than fear.
Participants also observed that human experience in certain tasks will remain essential even if AI systems begin to perform them – if you cannot do a task yourself, you cannot easily instruct an AI to do it for you.
How do we get there?
As discussion turned to how leaders can address these challenges, several participants noted that the most important challenge is not technological but human. Progress requires a focus not on AI itself, but on the outcomes which public services seek to deliver.
One attendee pointed to the importance of thoroughly reviewing processes across an organisation to understand any problems before exploring solutions. AI may be the right tool – but it also might not be. There could be simpler answers for many process problems, and these should not be overlooked.
Focusing on outcomes and process improvement would also mean investments in AI can focus on areas where they are likely to add the most value.
Participants also considered how to identify and choose the first use cases for agentic AI. One participant suggested starting with internal use cases to build confidence and understanding, while another spoke of the importance of choosing projects which help to build evidence on what works and why.
Others emphasised the need to balance this approach with inspiration from elsewhere and suggested that government should also be learning from countries and organisations that are already using agentic AI in meaningful ways.
Participants also noted that leaders should recognise that this technology will become increasingly embedded in the products that suppliers offer, and this means public sector teams must be ready to assess and manage agentic AI, even if they are not building it themselves.
A culture and mindset shift
Adopting agentic AI means embracing a degree of risk, and that is not always comfortable in the public sector. Participants said it will be vital to help people adjust through thoughtful communication, strong leadership, and a willingness to engage openly with concerns.
One attendee explained that they brought psychologists to support implementation of new AI tools to ensure that decisions were made with behavioural expertise. They found sharing experiences from peers who have already used AI systems can help reduce anxiety and build trust.
The discussion drew to a close with service leaders urged to think not only about the AI tools themselves, but the kind of society we want to build with them. Education, they argued, will be key for both within the civil service and across the wider population.
In the end, the message from the roundtable was simple but profound: keep your eyes on the goal. Agentic AI is a powerful new tool, but it is still just that: a tool. The purpose must be public value. Whether is it improving services, saving time, or tackling challenges that once felt insurmountable, the aim is not just to innovate for innovation’s sake. It is to make people’s lives better.
The difference between agentic AI and AI agents
AI agents are like a car. They are capable and useful, but they require someone to be in the driving seat. You decide where to go and how to get there, and the AI carries out your instructions.
On the other hand, Agentic AI is more like a chauffeur. You explain where you want to go – the goal – and it determines the best way to get there. It may change the route if there is traffic, alter plans if your calendar shifts, or let you know that there is a better restaurant along the way.
Technically, AI agents are systems that respond to instructions. They look at their environment, make decisions within a limited scope, and then take actions to complete defined tasks. They are good at what they are told to do, but they rely on the user to define both the what and the how.
Agentic AI systems take things a step further. You provide them a goal, and they determine what tasks are required to meet that goal. Operating with a higher level of autonomy, they adjust their actions based on changing conditions.