During the last 12 months, AI agents have steadily become an enterprise innovation priority. Key industries such as financial services and telecommunications are recognising the potential of agents that can autonomously plan and execute complex decisions, and interact with one another across systems and workflows.
According to PwC’s Africa Cloud Business Survey for 2025, while only 37% of businesses report that they are actively scaling agent AI implementations, 91% consider agentic AI capability a critical factor when choosing a cloud services provider.
By investing in flexible and scalable infrastructure to enable AI innovation, enterprises in Africa are moving beyond typical chatbots and recognising the broader potential of autonomous agents.
However, with agentic capabilities comes the need for orchestration. As enterprises deploy more agents, their ability to deal with that complexity across IT and business environments will become the measure of long-term success.
Hybrid cloud: A key component of agentic AI
Across West Africa, many companies are still in the early stages of IT modernisation, working to centralise, manage and automate traditional workflows. Many are only at the start of their AI journeys, but their focus is quickly shifting from deploying simple chat interfaces to high-density, autonomous workflows. The goalposts have not shifted; they have just grown in size and scope.
With that expansion comes growing complexity and risk, and nowhere is that more evident than with agentic AI systems. Poor design and implementation can lead to AI agents failing, whether it be through bottlenecks, conflicting objectives, resource conflicts or even feedback loops.
The way forward is to take a step back. Infrastructure and implementation are the most challenging aspects of using AI agents, and key to those aspects is enterprises prioritising open hybrid cloud platforms that let agents function consistently across different environments.
A hybrid cloud approach also enables enterprises to move models, workloads and policies whenever and wherever required, contributing to the system agility and scalability that’s critical to innovating with AI.
Implementation requires orchestration
Today, enterprises and industries are building and deploying agents for very specific use cases. An excellent example of this is in financial services, where agentic AI is revolutionising fraud detection and risk management by automating fraud response procedures, optimising and tuning prevention strategies and conducting real-time behavioural risk assessments.
Key to agentic AI in financial services and other industries is not relying on a single, general-purpose AI solution.
Agents need to be designed and deployed for specific tasks, whether those tasks are customer-facing, such as automated billing or personalisation, or at the back end of the business, including data retrieval and analytics. In all cases, agents need to be fit for purpose.
The end result is multi-agent systems (MAS), where agents not only execute their specific workflow but also collaborate with each other to accomplish greater, more complex tasks.
They do this through agent and multi-agent orchestration, a subset of AI orchestration that addresses key functions such as cross-agent communication, role allocation and conflict resolution.
By successfully orchestrating AI agents, enterprises in West Africa can automate complex workflows, unlock operational efficiency and improve the speed of their decision-making. They can also explore new business models, products, services and market opportunities.
Keep in mind, AI agents are meant to augment, not replace human expertise, and so any successful use case is also dependent on orchestrated collaboration between agents and technology teams, complete with full operational visibility and oversight.
A reliable, consistent foundation for AI innovation
As is the case with many elements of enterprise IT, agentic AI faces challenges related to consistency, interoperability and visibility.
Key to addressing those challenges is standardising the tools that developers use to build and manage AI systems, along with platforms that streamline development and enable developers to deploy agents and the applications they empower across different IT environments.
The solution lies with AI-integrated platforms that not only offer key essential IT functions but also come equipped with built-in integrations that let enterprises control their entire AI lifecycles, including building and tuning models, applications and agents.
Open standards and APIs further enable coordination between the tools and vendors that enterprises use, as well as enable them to move applications, agents and their workflows across hybrid environments.
automation through agentic AI represents a huge chunk of the future of IT transformation and modernisation across Sub-Saharan Africa. Realising this future starts with laying a reliable and consistent foundation for innovation.
With the power of open, hybrid and integrated platforms, enterprises in the region not only optimise their infrastructure and automate complex workflows with the help of agents but also reach a point where they can successfully manage and orchestrate those agents across their entire IT estates.
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