Reimagining automation with Business Process Agentification

Take the construction of the Egyptian pyramids for example. From an operations perspective, scribes meticulously documented every process – quarrying, transporting, and assembling stones – ensuring consistency and collaboration over decades, for an outcome that has lasted centuries.

Processes reflect the values, priorities, and technological capabilities of their time.

With each leap in technology, processes have evolved. In today’s age of digital transformation, automation and AI are redefining processes and, enabling organizations to tackle complexities in innovative ways. 

We now enter the next stage: is Business Process Agentification, which is creating a new era of generative AI powering dynamic ecosystems of adaptive, intelligent, and action-driven workflows.

It’s a new era for business processes

The evolution of business processes can be understood in four key stages, each building on the previous to address growing complexity and scale:

1. Workflow documentation: The foundation of business processes is their formal documentation: a blueprint for ensuring consistency, accountability, and efficiency, and identifying areas of improvement.
2. Task automation: Early automation entailed specific, repetitive tasks using rule-based systems or expert systems, which relied on predefined workflows. Later, Robotic Process Automation (RPA) introduced flexibility and human emulation, but its reliance on structured data limited scalability.
3. Hyper-automation: Scaling workflows brought together advanced technologies like AI, machine learning, and process mining to deliver hyper-automation: automating end-to-end workflows that enabled organizations to integrate systems and adapt to complex operations.
4. Agentification: The next stage of business processes involves driving outcomes introduces generative AI-powered systems capable of learning, adapting, and acting autonomously. Agentic systems go beyond execution, to focus on high-level objectives, and evolve with business needs. Agentification is automation reimagined.

In technology, innovation always iterates on the best of the previous eras. When it comes to processes, each stage of maturity reflects a step closer to intelligent, adaptive systems that redefine what processes can achieve.

Gen AI brings a whole new level of adaptability, combining the precision of rule-based systems, the efficiency of RPA, and the end- to- end optimization of hyper-automation—it bridges their gaps and makes it possible to tackle complexities that were previously out of reach.

It’s the natural progression for managing complexity in modern business environments.

Why use Gen AI to reshape business processes?

As businesses evolve, so do their processes. Generative AI offers a new lens that is fundamentally altering how organizations think about automation. Let’s look at this shift more closely: 

Simplifying process complexity

Defining exhaustive rules for processes is impractical in mission-critical or data-heavy environments, which need hardcoded rules for every possible scenario, or at least the most frequent use cases – which, in turn, limits process coverage.

Gen AI removes the need for those rules by recognizing patterns and making autonomous decisions. This allows businesses to automate complex, data-driven processes that were previously infeasible.

Adapting to change

Traditional automation workflows are rigid. They are designed to handle narrowly defined, predictable scenarios. The lack of flexibility leads to numerous edge cases, requiring manual intervention or ad hoc solutions that disrupt efficiency. The challenge becomes even greater when processes involve unstructured data—such as text, audio, or video—which traditional systems are ill-equipped to handle.

Generative AI models thrive in dynamic environments. They adapt workflows based on real-time data or changing conditions, which improves the agility of automated processes and reduces the need for constant reconfiguration. They process unstructured data like text, audio, and video, integrating this information into workflows while learning from new data to adjust processes in real time.

Focusing on outcomes

Traditional automation often breaks processes into discrete, task-specific steps, which is a fragmented approach that can limit in achieving broader business goals. Gen AI shifts the focus from tasks to outcomes, which enables automation systems to prioritize objectives:

• Natural language interactions: Gen AI helps businesses enable more seamless communication between humans and systems. For example, advanced conversational AI drives efficient customer service, internal help desks, and cross-team collaboration.
• Intent and action: Gen AI creates agents that interpret intent, execute actions, and orchestrate workflows across diverse roles, even when contributors are spread across teams or organizations. They foster smoother transitions and greater alignment with overarching objectives.

Agents don’t just provide information – they execute decisions and adapt in real time. They integrate into workflows to drive meaningful results. Gen AI agents create new possibilities for what business processes enable.

What is an agentic business process?

An agentic business process reimagines traditional workflows by introducing intelligent agents that operate autonomously yet collaboratively. It addresses the limitations of rigid, static systems by embedding generative AI at its core.

Businesses operate and change in many diverse scenarios, from evolving existing processes to creating entirely new ones for emerging functions. In both cases, agentification transforms workflows by redefining steps with specific personas in mind, orchestrating actions through a central coordinator, and linking agents into dynamic, multi-agent systems.

Steps defined by personas

In an agentic process, each step is tied to a specific persona, with agents acting as specialized roles.

Role-specific prompts: Each agent operates based on a defined set of instructions or prompts, guiding its actions in alignment with the process objective.

Contextual understanding: Agents are equipped with access to contextual knowledge or a shared history, allowing them to make informed decisions.

This granular role-based approach ensures that every aspect of the process is aligned with the personas involved, increasing efficiency, precision and maintainability.

High-level coordination

A central supervisor (also named coordinator or orchestrator) oversees the entire process. This entity, whether human or AI-driven, translates objectives—derived from human input or system calls—into tasks for individual agents. It coordinates the tasks and ensures that the objective is met through collaboration between agents. This orchestration keeps the process aligned with the overarching goal; even as autonomous agents conduct the individual steps.

A multi-agent system

Agents do not work in isolation. They function as part of a multi-agent system, linked through the supervisor to create a sequence of actions that solve complex objectives.

The system defines the flow between agents, ensuring that tasks are executed in the right order and context. They work in tandem in a dynamic system capable of tackling complex, interdependent workflows.

Gen AI is the glue.

Gen AI has the critical role of defining the rules and parameters that govern this multi-agent system:

• It creates and refines the prompts that guide individual agents.
• It establishes the rules for interaction and coordination, to ensure alignment.
• It adapts these rules over time, allowing the system to evolve with changing needs.

Guided by gen AI, agentification creates intelligent ecosystems out of business processes. Agents are not passive processors of information; they actively drive intent and execute decisions. It gives us a powerful framework for addressing complex, multi-step objectives with agility.

How would agentification work in practice?

Let’s explore how the agentic approach would benefit a critical process with an example.

Consider a plant manager faced with a machine defect on a production line. In a traditional setup, this would require manually coordinating multiple teams—data analysts, defect specialists, emergency response teams, and spare parts management—to diagnose and resolve the problem. Meaning delays, inefficiencies, and higher costs.

If the plant manager submitted the request to a multi-agent system, a supervisor agent would receive the objective—investigate and resolve the machine defect—and delegate tasks to a network of specialized agents. A business analyst agent gathers data, a defect analyst agent identifies the root cause, a defect emergency agent resolves the issue, and a spare part management agent handles procurement. All the agents work autonomously, yet collaboratively, to drive action on behalf of the team and deliver a quick, efficient resolution.

 Agentification turns a complex, once-manual process into a dynamic workflow that saves time and minimizes disruption.

Automation reimagined.

The conversation around Gen AI needs to evolve. Agentification represents a shift from task-focused automation to intelligent systems designed to align action with objectives. A new era is giving us the opportunity to use Gen AI to create a framework for rethinking business processes.

Begin with targeted experimentation: deploy agents in specific areas to assess their impact, and gradually expand to multi-agent systems. Combine Gen AI with other techniques like machine learning, rule-based systems, and simulation to build a mature, adaptable automation ecosystem. Not every step will require Gen AI, but thoughtful integration ensures each piece contributes to a cohesive strategy.

True transformation is collaborative. Our business leaders, app teams, and data specialists need to align with a common objective. Agentification is not mere technology—it’s the opportunity to reimagine processes, together. Let’s act now.