Building Machines That Act: A Practical Guide to Implementing Agentic Workflows in Your Digital Business

For most of its short commercial life, enterprise software has been reactive. A system waits for an instruction, executes it, and stops. A human makes the next decision. This arrangement worked reasonably well when business processes were slow enough to tolerate the gap between action and human judgment. It no longer does.

The shift toward agentic workflows, where software systems are designed not just to execute tasks but to plan, sequence, and complete multi-step goals with minimal human prompting, is now a live operational reality in companies across sectors and continents. The question for most businesses is no longer whether to move in this direction, but how to do it without upending operations or chasing results that don’t exist.

What “Agentic” Actually Means in Practice

The term gets used loosely, which creates confusion. An agentic workflow is one in which a software agent, or a coordinated group of agents, can perceive its environment, reason through a goal, select and use tools, and take action autonomously across multiple steps. It differs from conventional automation in that it doesn’t rely on a fixed script. If the situation changes, the agent adapts.

Bain & Company’s 2025 technology report describes four maturity levels, from basic knowledge assistants and copilots at Level 1 to fully autonomous multi-agent constellations at Level 4. Most businesses entering this space today will operate at Levels 2 and 3 — single-task agents executing self-contained loops, and cross-system orchestration that coordinates several agents toward a complex outcome. Level 4 remains largely aspirational outside of advanced enterprise environments.

The distinction matters for implementation planning. A business that expects to jump from manual processes to full autonomy will likely fail or lose money. One that targets a defined, bounded task with clear success criteria stands a far better chance.

Start with a Problem, not a Technology

Every successful agentic deployment begins not with a platform decision but with a process audit. The question to answer is: where does your team spend time doing work that follows a logic that can be made explicit? Customer onboarding, document verification, compliance review, internal reporting, and supplier queries are common candidates. They are high-volume, rule-adjacent, and measurable.

A client in Nigeria, as documented by business transformation firm ADG, is already using AI agents to handle document verification, freeing human staff to focus on customer service and growth. In the fintech sector, agents are being deployed for fraud detection, risk assessment, and KYC automation, not because these companies have unlimited budgets for experimentation, but because the underlying processes are structured enough to make agent logic tractable.

IBM’s research on agentic AI operating models draws a useful distinction between organizations that use agents to optimize existing workflows and those using them to build entirely new capabilities. Both create value; the latter creates more. But the entry point is almost always optimization. Pick one workflow, define two or three success metrics, and run a pilot before any broader commitment of resources.

The Architecture Question

A working agentic system has several functional layers. There is a perception layer that takes in inputs — documents, data feeds, API responses, and user queries. There is a memory layer that holds context across steps. A planning and reasoning engine breaks goals into sequences. An execution layer takes action: writing, querying, sending, and updating. And a feedback mechanism closes the loop.

For most mid-sized businesses building their first agentic system, this doesn’t require custom infrastructure. Platforms like Microsoft Azure AI, AWS Bedrock, and Google Vertex AI now expose these capabilities through managed services and low-code tooling. Open-source orchestration frameworks such as LangGraph and Microsoft AutoGen give engineering teams the structure to design planners, executors, and human-in-the-loop handoffs without starting from scratch.

The critical architectural decision is where the human stays in the loop. Every system that touches customers, financial data, or sensitive records needs a well-designed escalation path, a point at which the agent passes control to a person. This isn’t a sign of immaturity in the system; it’s a governance requirement and, in many cases, a regulatory one. Deloitte’s agentic AI guidance is explicit on this: reliable agents are the goal, but trustworthy, auditable behavior is the prerequisite.

Data Readiness Is the Real Barrier

Technology teams frequently underestimate how much of an agentic workflow’s performance depends on the quality of the data feeding it. Agents reason from what they can see. If the underlying data is inconsistent, poorly labeled, or siloed across systems that don’t communicate, the agent’s outputs will reflect those flaws at scale and at speed.

This is a particular consideration for businesses operating in African markets, where data infrastructure is still maturing, and historical records often exist in fragmented or paper-based form. McKinsey’s analysis of the African AI opportunity estimates that at-scale deployment of AI could unlock $61 billion to $103 billion of economic value across the continent, but getting there requires what the report calls a “business transformation, not just a shift in technology.” Data cleaning, structuring, and curation are non-negotiable first steps.

Before deploying an agent in a live workflow, businesses should conduct a data audit, identify where gaps exist, and determine whether the agent will have access to enough reliable context to reason correctly. This is not a one-time exercise; it is ongoing.

Governance, Oversight, and the Regulatory Landscape

Agentic systems create new governance obligations. When a system acts autonomously on behalf of a business, sending communications, making credit decisions, and routing claims, the business remains accountable for what it does. Logging agent behavior, maintaining audit trails, and defining escalation thresholds are not optional features. They are the difference between a system that builds trust and one that creates liability.

Across Africa, regulatory frameworks around AI are still forming. Nigeria’s National Information Technology Development Agency (NITDA) has been developing an AI policy framework, and the broader African Union is working toward harmonized AI governance under its Digital Transformation Strategy 2030. Businesses implementing agentic systems now should document their architectures, define accountability clearly, and build compliance into the workflow from the start.

Building the Internal Capability

Technical infrastructure is only part of the equation. Microsoft’s AI National Skills Initiative in Nigeria has trained over 350,000 Nigerians with AI skills, with a specific focus on agentic AI applications in fintech and public services. The program’s Agentic AI hackathon produced working prototypes in document verification, risk assessment, and fraud detection. These are areas where the underlying workflow logic is clear enough for agents to operate effectively.

This points to a broader principle. Staff who understand the workflow being automated are essential collaborators in building the agent. Business users who can articulate process logic, identify edge cases, and define acceptable outcomes are not passengers in an IT project; they are designers. Businesses that treat agentic implementation as purely a technology decision tend to build systems that work in test environments and break in production.

Training staff to use AI tools effectively, building internal communities around shared prompts and use cases, and giving teams room to iterate — these aren’t soft add-ons. They are implementation requirements.

The Realistic Trajectory

Gartner projects that 15 percent of day-to-day work decisions will be made autonomously by agentic systems by 2028, up from essentially none in 2024. That trajectory is steep, but it doesn’t mean every organization should be racing toward autonomy. It means organizations that start building capability now, with clear use cases, clean data, and proper governance, will be positioned to extend that capability as the technology matures and as confidence in agent reliability grows.

For African businesses, there is a genuine leapfrog opportunity here. Many sectors on the continent — financial services, healthcare logistics, agri-commerce, public administration — are operating with process bottlenecks that agentic systems are well-suited to address. Unlike earlier waves of enterprise technology, today’s agentic platforms are increasingly accessible to businesses without large IT departments, through managed services and low-code tools.

The implementation path is not simple, but it is tractable. Identify a bounded process. Audit the data. Define what success looks like. Build a pilot with human oversight. Measure it. Then expand. That sequence is less dramatic than the pitch material tends to suggest, but it is the one that produces results.