Six steps for making agentic AI real in the public sector

Why? Because AI agents analyze, adapt and optimize processes, not just repeat them. They make independent, intelligent decisions, not just follow predefined rules. And they work together to carry out complex, multi-step processes – not just complete individual tasks. 

 These capabilities open the door to new levels of efficiency and service in the public sector. But keeping up with such complex, fast-evolving technologies can be a challenge, and implementing them can be even harder. How do you identify the best use cases, know which platform to choose, or decide an appropriate level of autonomy for your AI agents? And what foundations need to be in place before you can get started? 

When it comes to demystifying and deploying AI, my motto is “Make it real.” And this is what we set out to do in our new point of view, Architecting AI Agents in the Public Sector. 

Simplifying a complex field

Our point of view breaks down what AI agents are and how they work – both individually and as part of multi-agent architectures.  

It also includes lots of real-world use cases, so you can see the breadth and depth of the potential these tools offer. And, most importantly, it sets out six steps for integrating AI agents into public sector workflows in a secure, responsible and observable way. Because in my experience, that’s where many technical architects and leaders are currently getting stuck.  

First, what exactly are AI agents? 

AI agents are intelligent digital assistants that take automation to a new level. That means that, rather than following pre-set instructions, they can: 

• Perceive: gather and analyze data from various sources. 

• Process: use algorithms and models – especially Large Language Models (LLMs) – to evaluate and process that data.  

• Act: carry out actions autonomously, based on their analysis – from sending messages and triggering workflows to generating and orchestrating other agents to help complete a task. (See below).  
   

The real game-changer, though, is their ability to understand and apply different “languages” – from APIs and code to natural language.  .

Stronger together

Individual AI agents can help address rising public sector workloads and staff shortages by handling routine, repetitive tasks. They can also help meet growing demand for digital citizen services while improving the quality of those services.  

But when specialized agents work together to complete specific tasks, they can also manage complex, cross-agency workflows at scale. And whether they work alone or in a multi-agent architecture, they free up employees for more meaningful work.  

It’s not an either/or, though. AI agents can operate at varying levels of autonomy, depending on your needs and the level of human oversight required. Our point of view shows the full spectrum, from zero agent involvement to AI-integrated processes (where agents manage complex, cross-functional tasks) and fully autonomous systems requiring little human input. 

Let’s make it real

Here’s an example. At Capgemini, we partnered with the German Federal Employment Agency to automate the process of creating IT service tickets within an internal system that supports social benefit processes. Instead of rigid automation, a team of AI agents now extracts key details, structures Jira tickets, and checks for errors and duplicates – all while staying secure and compliant.  

The result: faster, higher-quality outputs and less manual work. The example also shows how AI agents can support, not replace, public employees by freeing them to focus on more complex, value-adding tasks. 

From agentic vision to action: six steps to take now

So, how do you move AI agents from potential to practice? In our point of view, we’ve outlined six practical steps for doing so safely, responsibly and at scale. Here’s a summary.  

1. Build a strong data foundation

AI agents are only as good as the data they rely on, so make sure yours is high-quality, accessible and trustworthy. Create a common data model and use APIs so your systems can talk to each other. And always test in safe, isolated environments, to protect live systems and personal data. 

2. Assess your automation readiness

Map your existing stack to see where agentic automation could slot in easily, such as into processes that already partly digitized. And be clear from the start what the system is responsible for (and what it isn’t). 

3. Choose the right architecture  

Every public sector organization has its own mix of systems, compliance requirements and security priorities. Choose the model (on-premise, cloud or hybrid) that fits yours best while meeting your needs for speed, scalability and data sovereignty. And save your agentic system’s “thought processes” for more consistent results. 

4. Design prompts and interfaces systematically  

Creating shared libraries and APIs for common tasks will improve the user experience by making the agents behave in more predictable, consistent ways. So treat prompts like software components, not one-off tricks. 

5. Start with high-value, low-risk use cases

Don’t try to automate everything at once. Use our decision matrix to pick low-risk use cases that bring quick benefits – for example, automating routine citizen enquiries or appointment scheduling. These allow you to test safely, measure success and iterate fast. 

6. Monitor, test, and improve continuously

Once your agents are live, keep a close eye on how well they’re working and helping users, and keeping refining them over time. That includes tracking key technical and operational stats, including speed and errors, and running controlled experiments.  

Final word

Of course, integrating AI agents in the public sector is different – and much more complex – than in private sector settings. Every automated decision must be legally accountable and explainable, for a start. AI must also be able to integrate across a fragmented IT landscape, and of course, citizen data must be protected under national and regional laws. 

We’ve tried to address these complexities in our point of view. If you still have questions, or you’d like to know more, get in touch with me or one of my colleagues. 

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