Three lessons from governing Agentic AI at scale

Many are also discovering that they're no longer managing a handful of AI tools. They're overseeing a growing AI estate. Agents are being created across teams, platforms and business functions, often within already complex technology environments. Without clear governance, ownership and orchestration, complexity grows quickly. The lesson is simple: governance cannot be bolted on later. It needs to be built in from the start.

What delivering Agentic AI at scale taught us

At Capita, we've been on our own AI adoption journey. For more than 40 years, we've helped organisations across the public and private sectors run and transform some of their most critical and complex services. Today, we support millions of citizens and customers every day through large scale front, middle and back-office operations, from helping Transport for London serve more than 4 million customers each month across digital and contact centre channels, to processing 20 million NHS eyesight service claims every year and collecting £1.5 billion in council tax annually on behalf of local authorities.

That experience gives us a unique perspective on what it takes to introduce new technologies into live, high volume and often highly regulated environments. Before helping clients scale AI, we believed we needed to prove it within our own organisation.

Today, more than 5,000 colleagues have access to Copilot. Across our business, hundreds of intelligent agents support operations, generating around 350,000 Copilot interactions each month and delivering approximately 19,000 hours of Copilot assisted productivity.

As adoption accelerated, we recognised that while the technology was advancing rapidly, long-term success would depend on governance. By putting the right controls, visibility and accountability in place early, we were able to scale confidently and avoid the fragmentation that many organisations encounter as Agentic AI adoption grows. We learned that successful Agentic AI adoption requires an operating model, not just a technology strategy.

That's why we invested in capabilities such as our AI Catalyst Lab, Copilot Academy, Mulesoft, Pathfinder network and responsible AI governance framework. It is also why we strengthened the integration and orchestration capabilities needed to support a growing multi agent estate.

This approach has been recognised through our shortlisting in the Personnel Today Awards 2026 Excellence in AI Adoption category, as well as the Digital Transformation Award at the Utility Week Awards 2025. The latter recognised our co-development with a client of Agent Assist, an AI-powered solution that empowers frontline teams, enhances customer experiences and delivers scalable, human-centred service improvement. Together, these achievements reflect what we believe is the next phase of AI maturity: moving beyond proving that AI works to ensuring it can be governed, trusted and scaled with confidence.

Three lessons about governing AI at scale:

1. You cannot govern what you cannot see

The first challenge organisations encounter is visibility. Whether an organisation has five agents or five hundred, the same issues emerge. Ownership becomes unclear, teams duplicate effort and leaders lose sight of where risk and value exist.

At Capita, we've addressed this by developing the ability to discover, catalogue and govern agents across multiple platforms and environments. Every agent can be registered, assigned an owner and governed through a common framework. Using MuleSoft Agent Fabric as our enterprise control plane, we can automatically discover agents across approved platforms, maintain a central registry and apply consistent governance, security and observability controls regardless of where the agent was built. That visibility continues throughout the agent's lifecycle, linking its purpose and owner to its risk assessment, controls, approvals, operational performance and continuing assurance. Because ultimately, you cannot govern what you cannot see.

2. Governance should enable innovation

Governance is often viewed as a control mechanism. Our experience suggests it can be an accelerator. As AI adoption grows, organisations frequently find different teams solving the same problem multiple times. Without clear standards and governance, duplication increases and value is lost.

Common standards, reusable patterns and proportionate guardrails reduce the decisions each team must make from scratch. Teams can discover existing capabilities before building new ones, understand the route to approval and scale successful ideas with greater confidence. The result is a shift from isolated experiments to a managed portfolio of capabilities that can be reused, improved and governed consistently.

3. Orchestration is becoming the missing layer

Deploying agents is only part of the challenge. The real test comes when organisations try to coordinate agents, systems, data and people within live operational processes. This is where orchestration becomes critical.

In our view, orchestration is not simply about connecting technologies. It determines how work moves between agents, systems, data and people, and where policy, oversight and accountability are applied. Done well, it provides the visibility needed to understand how an agent estate is behaving, not merely what has been deployed. Technology orchestration alone is not enough. Organisations also need the operational, process and change expertise to embed the model into day-to-day service delivery.

That's why we've developed our Forward Deployed Orchestrator™ model, embedding AI fluent process and change expertise directly within our clients’ operational teams to ensure AI continues to deliver value once it is live and operating at scale. We've already seen the benefits in practice within our own recruitment operations, where Agentic AI has helped save more than 1,000 management and recruitment hours in four months, while reducing candidate screening time by 43%.

Turning capability into outcomes

The organisations that will realise the greatest value from Agentic AI are unlikely to be those that simply build the most agents. They will be the organisations that can govern, orchestrate and operationalise them most effectively.

At Capita, we're already seeing the benefits of that approach. Across government and private-sector case management and back-office operations, we've delivered 30-40% reductions in case management costs, a 25% uplift in operational capacity and 88% faster dispute resolution. These results haven't come from deploying technology in isolation. They've come from combining AI with process expertise, governance and operational ownership.

Looking ahead

AI capability will continue to evolve rapidly. The more interesting question is how organisations choose to operationalise it. Our experience suggests the greatest challenge is not building AI systems. It is creating the conditions that allow those systems to be trusted.

The organisations that succeed will be those that establish visibility, ownership and governance from the outset, combine human judgement with AI capability and treat orchestration as a core business capability rather than a technical afterthought. Because ultimately, successful AI adoption is not just about capability. It is about confidence. And confidence is built through trust, transparency, accountability and responsible governance.

Find out how Capita can help you adopt Agentic AI safely and responsibly, embedding governance and accountability into live service delivery from day one: