A single employee can be easy to manage. A team of ten can drive meaningful productivity gains. But when an organisation has hundreds of employees working across departments, processes and systems, success depends on visibility, control, governance and coordination.
By Greg McDonald, sales and technology director for South Africa at HPE
The same is becoming true of AI agents. A single AI agent can deliver measurable value. But as organisations deploy dozens, hundreds or even thousands of autonomous agents across workflows, departments and business systems, a new challenge emerges: visibility.
Without a clear understanding of how agents operate, what data they access, what resources they consume and what outcomes they deliver, organisations will struggle to realise the full value and transformational potential of agentic AI.
South Africa’s shift towards the agentic enterprise is gathering pace as organisations invest in autonomous AI systems and move from pilots to production. Just earlier this year it was reported that almost half of local banks allocated more than R30 million each to AI initiatives, much of it directed to autonomous agents. More broadly speaking, the country’s enterprise agentic AI market is forecast to grow at an annual rate of 49.1% through 2030.
These investments signal a fundamental shift in how organisations operate. We are moving beyond isolated AI tools and into environments where intelligent agents work alongside people, planning, acting and taking action across workflows. Intelligence is becoming embedded across applications, workflows and infrastructure, giving rise to the agentic enterprise.
A new economic model for AI
The economics of agentic AI are fundamentally different from those of traditional AI tools. In a conventional AI workflow, a user submits a prompt, the model processes the request and returns a response. With agentic AI, however, what appears to be a single interaction can trigger hundreds of processes behind the scenes.
A service desk escalation, for example, may require an agent to analyse the issue, retrieve relevant knowledge and policy content, assess service-level requirements, gather further context and recommend a resolution. This ability to coordinate complex tasks is where much of agentic AI’s value lies. At the same time, each step may involve additional model calls, retrievals and system interactions, creating a more dynamic consumption model.
As agents move from isolated pilots into continuous, enterprise-scale use, organisations need a clearer view of both the resources being consumed and the value being created. With different business units deploying agents to address departmental needs, visibility and optimisation become essential to coordinating demand, selecting the right models, resources and infrastructure, and directing investment towards the workloads that deliver the greatest impact.
Visibility turns activity into value
Scaling this opportunity requires a new operational discipline. Organisations need a clear understanding of which agents are running, which models they use, where resources are being consumed, what data and systems they can access, and, most importantly, the value they deliver. This visibility enables organisations to optimise costs and prove return on investment.
The economics of agentic systems extend beyond model pricing. They are also shaped by workload design, orchestration patterns and where workloads run across public, private and hybrid environments. With deeper insight into how agents operate, organisations can place each workload in the environment best suited to its performance, data and economic requirements. HPE’s own internal productivity platform illustrates the opportunity. Greater visibility into the autonomous agents supporting the platform enabled HPE to identify workloads that could be moved from public cloud infrastructure to a private environment, dramatically improving the economics of the solution.
Infrastructure becomes the foundation for success
As organisations move from experimentation to enterprise-scale deployment, AI economics increasingly become infrastructure economics. Growing numbers of autonomous agents create new demands across the technology stack, making infrastructure choices central to performance and value. The critical question is no longer how many AI outputs an organisation can generate, but whether those outputs create measurable business impact.
With visibility into how agentic workloads behave, organisations can make smarter choices about how and where those workloads run, balancing performance, risk and cost to maximise value. These infrastructure decisions should be viewed as part of a holistic model. Successfully scaling agentic AI requires an architecture that brings together data, infrastructure, networking and governance into a coherent operating model. Each element depends on the others, and weaknesses in any one area can limit the value organisations realise from AI.
Data sits at the centre of this equation because it provides the context on which AI agents reason and act. Organisations need to focus on both data quality and the infrastructure that supports it. Data must be accurate, governed and accessible wherever it resides. Poor-quality data does not simply generate poor insights; in agentic systems, it can drive flawed decisions and actions automatically, and at scale. Accuracy, completeness and reliability therefore become essential. Equally important is where intelligence is applied. Rather than continually moving large and sensitive datasets to AI systems – increasing cost and latency – organisations should aim to bring AI to the data. Running intelligence close to governed data sources is often more efficient, secure and scalable.
As organisations scale agentic AI, networking provides the connective foundation through which enterprise intelligence moves, connecting agents with the data, applications and systems they depend on. When networks lack visibility or adaptability, AI initiatives can struggle to scale beyond isolated use cases. Conversely, modern AI-native networks can help organisations support growing AI workloads while simplifying operations, improving resilience and enabling a more autonomous operating model.
Governance is the accelerator
As AI becomes more deeply embedded in core operations, ensuring that its activity is visible, measurable and governed becomes just as important as the network that supports it. As agents gain greater autonomy, organisations must also define where human oversight, approval and intervention remain essential. That requires clear policies, robust security and access controls, and continuous visibility into what agents can access, what actions they take and how AI consumption translates into business outcomes. Essentially, governance becomes an accelerator rather than a constraint.
Harnessing the full value of agentic AI
A growing population of AI agents needs an operating model that connects visibility, data, infrastructure, networking and governance. With these foundations in place, organisations can scale agentic AI with a clear view of the resources it consumes and the value it creates.
The opportunity is not simply to deploy more agents, but to reshape how work gets done through intelligence that can act at scale. The advantage will belong to organisations that build for that opportunity and know how to make every agent count.