Kenyan banks, telcos, and large enterprises must prepare their current systems to support AI agents before they can deploy these tools across their operations. That was the central point made by WSO2 at WSO2Con Africa, where the company argued that simply adding a chatbot to a website is no longer the main challenge. Instead, businesses need to connect autonomous agents to the existing infrastructure that powers their daily activities.
An AI agent can perform tasks independently, but its usefulness depends on the systems it can access. A single request might require information from customer management tools, payment processors, compliance databases, and internal operations software. Without a way to link these separate systems, an agent cannot complete a full workflow.
Connecting the Dots
WSO2 noted that many current enterprise systems are designed primarily for human use or developer access, not for autonomous agents. To introduce agents, organizations often need to make their infrastructure “agent-ready.” The company’s WSO2 Integrator provides this layer, offering more than 600 connectors to link applications and services.
WSO2 uses the Model Context Protocol to expose data and functionality to agents. MCP servers act as an interface that allows agents to interact with existing systems. This approach lets Kenyan businesses connect new agents to the software they already use rather than replacing those tools.
Handling Complex Workflows
Agents can go beyond answering questions to assist with actual business processes. WSO2 highlighted “long-running workflows” that let a process pause when it needs input or approval, then resume once that step is finished. In a loan application, for example, an agent could handle initial checks, wait for a customer to provide documents or an employee to approve the request, and then continue with the remaining steps.
The company emphasized that organizations need tools to evaluate agents during development and monitor their actions in production. Human-in-the-loop guardrails can limit what an agent is authorized to do. An enterprise might allow an agent to handle transactions below a certain threshold automatically while requiring a human to approve larger actions.
Adopting this technology changes how enterprises introduce AI into everyday operations. The challenge extends beyond selecting a model or building an agent; it involves designing how those agents access data, interact with applications, and operate within established controls. Integration becomes a central consideration, not just a back-end technical concern.
