Federated AI Architectures: A Careful Balancing Act – Deloitte

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Joost Verbraeken and Marion Robin outline four key strategies to create a robust federated architecture that organisations can use to effectively scale agentic AI.
This page is part of A C-suite guide to capturing the potential value of AI.
While AI adoption has exploded in our private lives, most organisations struggle to move beyond using it as a basic assistant.
That reflects the fact that most enterprise IT functions can’t keep up with the speed of AI innovation. Compared to last year, organisations’ perceived readiness has declined across technical infrastructure (43%) and data management (40%), according to Deloitte’s The State of AI in the Enterprise – 2026 AI report.
Building the capabilities required to scale agentic AI demands a cohesive, enterprise-level technology framework and strategy: an “enterprise architecture”. As explained in an earlier Deloitte article, there are many different elements to consider regarding enterprise architecture for agentic AI.
One of the key dilemmas is the extent to which architecture should be centralised to enable agentic AI: For our clients, finding the right balance is often the biggest hurdle in moving AI from pilot to enterprise-wide production. Too little centralisation, and you end up with inadequate tools and inaccessible data, weak governance, and agents trapped in silos. But with too much centralisation, you end up with a massive IT bottleneck stifling domain-specific innovation.
The answer isn’t pure centralisation or pure decentralisation—it’s a federated approach. With this approach, organisations centralise foundational capabilities and governance, providing an effective framework for decentralised teams to innovate without creating IT bottlenecks or siloed agents.

Below, we share four key strategies that we have implemented at clients to achieve the right balance, addressing barriers in building, governing and connecting agentic AI. Note, the databarriers are covered in another blog.
Enterprises deploying AI agents across multiple domain platforms face a critical governance challenge: Domain-specific systems handle local agents well, but cross-domain agents move beyond assumed trust and governance frameworks. This creates risks that traditional security approaches often don’t cover. A dual-track AI platform strategy resolves this tension.
When scaling agentic AI, a lack of visible reusable assets often drives teams to rebuild the same foundational components from scratch (pipelines, integration tools, etc.). These duplications waste resources, introduce inconsistencies, and create unnecessary risk. By establishing a centrally-managed library of reusable agentic AI building blocks, organisations can significantly accelerate the development of new agents withindomains while reducing risks and lowering costs.
AI agents present significant security, privacy, and governance risks including autonomous task execution, prompt injection, and credential exposure. For example, earlier this year, widespread usage of the new OpenClaw agent framework, highlighted both the potential of agents to act autonomously as well as significant security, privacy, and governance threats (such as taking undesired actions, prompt injection, credential exposure, etc.). Addressing these risks and ensuring compliance with regulations, such as the EU AI Act, requires centralised visibility over federated agents and updated risk frameworks.
To successfully support cross-domain AI use cases. However, if agents deployed within domains cannot easily communicate with each other, their value is capped within their silo. It is important to standardise how agents connect, communicate, and discover oneanother across the organisation.
To bridge the gap between agentic AI’s potential and scalable enterprise value, enterprise architects must find the right balance between centralisation – creating IT bottlenecks – and decentralisation – leading to inefficiencies and risks. The answerlies in a deliberate, federated architecture: centralise the foundation to decentralise the innovation.
By implementing the four strategies outlined in this article – a dual-track platform strategy, reusable building blocks, responsible AI governance, governed agent interoperability – organisations directly address the critical barriers to unlocking value. This federated approach delivers the best of both worlds: domain teams are empowered to build and deploy agents rapidly and securely, while central teams maintain necessary visibility, control, and compliance.
Ultimately, striking this balance will transform agentic AI from a series of isolated experiments into a cohesive, enterprise-wide capability.
For further information, or to discuss your scaling challenges, please contact us.
This page sets out a practical roadmap with key questions and deep dives to help senior leaders assess readiness, prioritise use cases and accelerate value realisation with AI. Explore the five areas to identify priorities and turn AI pilots into measurable business impact: strategy, organisation and people, risk and compliance, technology and implementation, and ecosystem and partnerships.
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