At FutureNet World 2026 in London, one of the more thought-provoking sessions came from Víctor Fernández of Vodafone Group, who explored what it really means to enable AI to act at scale through APIs. While much of the industry conversation around AI still focuses on models and data, this talk shifted attention to a more practical layer. If AI agents are to move beyond insights and into action, they need structured, reliable and well-designed interfaces into the network and its capabilities.
The central idea was straightforward but important. AI agents cannot deliver value in isolation. They need access to systems, data and control points, and APIs are the mechanism that makes this possible. However, simply exposing APIs is not enough. If they are not designed with AI consumption in mind, they risk becoming bottlenecks rather than enablers.
A key theme was the need to rethink API design principles for an AI-driven environment. Traditional APIs have largely been built for human developers who understand documentation, constraints and context. AI agents operate differently. They require APIs that are easy to discover, clearly defined and consistent in structure. Standardisation becomes critical here, not just for interoperability across systems, but also to allow agents to reliably interpret and use them without ambiguity.
Closely linked to this is composability. AI agents are most powerful when they can orchestrate multiple services to achieve a goal rather than executing a single isolated task. This means APIs should not be designed as standalone endpoints, but as building blocks that can be combined into workflows. In a telecoms context, this could involve stitching together network data, policy controls and service exposure capabilities to enable more advanced automation scenarios.
Another important aspect discussed was scale. As AI agents begin to interact with APIs at a much higher frequency than human users, the load on systems will increase significantly. This introduces new challenges around monitoring, management and optimisation. It is no longer just about whether an API works, but how it behaves under continuous, automated usage. Observability, rate control and intelligent traffic management become essential to ensure reliability.
There is also a governance dimension. Allowing AI agents to take actions through APIs introduces risks if not properly controlled. Mechanisms are needed to define what actions are permitted, under what conditions, and with what level of oversight. This is particularly relevant in telecom networks where certain actions can have wide-ranging operational impacts. Ensuring that AI-driven interactions remain safe and predictable is as important as enabling them in the first place.
The session also highlighted the importance of a developer-friendly ecosystem. Even in an AI-centric world, developers remain central to creating and maintaining the underlying capabilities. Clear documentation, sandbox environments and consistent tooling are all necessary to support both human developers and the AI systems they build. In many ways, the goal is to create an environment where APIs are equally usable by humans and machines.
What stood out from this talk was how it grounded the AI discussion in something tangible. There is a growing recognition that the real challenge is not just building smarter models, but integrating them into operational systems in a way that delivers measurable outcomes. APIs sit right at that intersection.
For operators, this represents both an opportunity and a responsibility. By exposing network capabilities through well-designed APIs, they can enable a new class of applications and services driven by AI. At the same time, they need to ensure that these interfaces are robust, scalable and secure enough to handle the demands of automated consumption.
As the industry continues to explore the role of AI in telecoms, conversations like this help move the narrative forward. They shift the focus from what AI could do in theory to what it needs in practice to operate effectively at scale.
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