Agentic process automation: The future of intelligent automation

Imagine process automation systems that don't just follow rules to carry out routine, repetitive tasks but also adapt to changing circumstances, proactively identify and resolve issues, and even optimize their own performance. That's the power of agentic process automation.

Agentic automation represents the next leap in automation technology, combining traditional robotic process automation (RPA) with advanced artificial intelligence (AI) capabilities. This approach integrates large language models (LLMs) and generative AI to create systems that can learn, adapt, and make decisions autonomously.

Through my work with Genpact's centers of excellence (COEs) for AI-enabled automation, I've witnessed how these advancements are not just transforming specific tasks but also reshaping entire systems. This article distills those experiences and looks at how agentic process automation (APA) is steering enterprises toward becoming autonomous – a vision that has become a reality with advancements in generative AI in the last two years.

Unlike traditional RPA, which is structured and logic-based, agentic automation autonomously handles ambiguity and decision-making tasks. AI agents learn from grounded data, making them adaptable and responsive to business needs and helping enterprises to:

• Improve efficiency and deploy faster: Developed with clear natural language instructions, agents can convert scripts to code, improve flexibility, reduce errors, and speed up deployment
• Respond swiftly to evolving business scenarios: Agentic AI uses reinforcement learning to assess situations and make optimal, real-time decisions independently
• Continuously optimize: Real-time data analysis allows agentic AI to refine processes and enhance operational performance over time
• Enhance integration capability: Agentic AI integrates seamlessly with existing systems, maximizing the value of current technology investments
• Autonomously resolve production support issues: AI-driven error logs can identify potential issues and resolve them autonomously, a capability already seen in IT support helpdesks

However, enterprises don't have to choose one approach over the other. Leading companies in industries like healthcare, life sciences, and finance are already looking at using AI agents alongside humans and traditional bots to manage complex processes.

Automation can be split into two approaches: structured and cognitive. Traditional RPA represents structured tasks – logical, rule-based, and systematic. Agentic automation, on the other hand, involves cognitive tasks requiring creativity, decision-making, and learning from experience. AI agents manage these tasks autonomously, significantly expanding the potential of automation.

The convergence of robotic and agentic automation allows businesses to automate most human tasks, freeing employees to focus on higher-value, strategic work. Robotic systems handle structured tasks efficiently, while agentic AI agents manage complex and dynamic tasks, working together to boost efficiency and drive innovation.

Agentic AI integrates seamlessly with existing enterprise applications through app connectors. These connectors enable agentic AI to communicate effectively with diverse systems, databases, and platforms, allowing for real-time data sharing, smoother processes, and optimized agentic workflow.

The architecture of agentic AI systems

The architecture for agentic AI systems combines AI, machine learning, and cloud services to enable intelligent agents to execute autonomous tasks. Its key components include:

• Agents and multiagent collaboration: Specialized agents perform various tasks, communicate with each other through APIs, and interact for effective decision-making to drive autonomous workflows
• Cloud infrastructure: Provides scalability, flexibility, security, and computational power
• Security and governance: Checks that agents operate within controlled parameters and follow responsible AI guidelines
• AI models and decision-making: Enables agents to make autonomous decisions using pretrained or fine-tuned prompt-driven models
• Data processing and storage: Allows users to efficiently manage data streams and databases using cloud providers and enterprise packages
• Large language models (LLMs): LLMs allow agents to go beyond rule-based actions and interpret unstructured data, analyze documents, and generate actionable insights in real time. LLMs also enable more complex tasks like dialogue management, summarization, and even code generation for task execution. Optical character recognition (OCR), natural language processing (NLP), and sentiment analysis can help agents enhance their understanding and interaction with data, particularly when dealing with large datasets like invoices, contracts, and emails
• AI-driven orchestration and workflow management: Enables efficient workflow management and the creation of dynamic workflows. This enables businesses to optimize multistep processes with minimal human intervention

Connecting agentic AI to the technology infrastructure is just one piece of the puzzle. To use agentic AI effectively, enterprises must implement agile sprint planning to integrate agentic AI into operations. A structured approach will enable a smooth transition and faster time to value. An optimal sprint model typically involves:

• Rigorous operational reviews to align project goals with business objectives
• IT and infrastructure set up to establish the necessary foundation
• Onboarding to familiarize teams with new technologies
• Technical assessment and proofs of concept (POCs) to develop and test AI agent capabilities
• Phased development to refine AI models and scale deployment
• Ongoing support and iteration to maintain and enhance AI capabilities

Agentic automations operate through AI agents that possess intelligence and autonomy and dynamically execute agentic workflows that are part of a larger end-to-end business process automation system.

A prime example of such an AI agent system is the recent implementation of our Refund Processing AI Agent solution within the finance process of a Fortune 500 client. We helped the client transition the processing of customer refund requests from a traditional, manual-intensive system to a sophisticated, multiagent AI model.

The previous manual workflow involved numerous handoffs between business teams and bots, such as collecting refund requests, formatting Excel data, and validating eligible refund requests in various ERP systems. In the future state model, we have established an autonomous touchless workflow to automate these upstream tasks. Here's how the key agents in this solution work:

• User proxy agent: Gathers and validates refund requests
• Organizer agent: Structures and formats incoming refund data
• Orchestrate agent: Calls for task flows and applies governance protocols
• Task agent: Executes validation steps and posts data to ERP systems
• Assistant agent: Provides real-time status updates to users using chatbots, improving customer satisfaction and loyalty

Together, these agents have reduced manual errors and rework, enhanced operational efficiency, and reduced human involvement, allowing the team to focus on higher-value tasks.

Our work implementing agentic AI platforms has revealed several critical insights:

1. Prioritize robust prompt engineering design and sequential testing: Incorporating these workflows during the build phase is essential to minimize rework and prevent extended timelines caused by incomplete end-to-end testing. Further, it helps check that AI workflows meet both business requirements and stakeholder expectations
2. Use a small-scale POC to rigorously test infrastructure readiness: It's vital to do this before development begins to facilitate smooth operations and avoid unexpected bottlenecks during implementation. Additionally, access to the necessary host environments and systems, supported by IT specialists, plays a crucial role in enabling seamless development and deployment. Similarly, providing access to critical enterprise systems like ERP platforms ensures streamlined processes and uninterrupted development
3. Security and governance are paramount in agentic AI deployment: Implementing role-based access control (RBAC) ensures that only authorized users can access sensitive development and test environments. This should be complemented by robust authentication measures and user-specific credentials, which enhance traceability and secure system interactions. Additionally, compliance-focused practices, such as audit logs and activity monitoring, strengthen accountability and build trust in the system

By applying these learnings, organizations can unlock the full potential of agentic AI platforms, achieving scalability, efficiency, and AI-led innovation tailored to their unique objectives.

In conclusion, with the new-age capabilities of agentic AI software development platforms, the vision of an autonomous enterprise is now more within reach than ever. By leveraging low-cost RPA solutions, where applicable, alongside advanced agentic AI automation, businesses can tailor their strategies to align with specific objectives, achieving an optimal balance of efficiency, innovation, and decision-making prowess. These tools empower organizations to streamline operations, foster creativity, and make smarter decisions in real time.

The future of business lies in embracing agentic AI – are you ready to lead the transformation to drive your enterprise's goals?

This point of view was authored by Raghu Ram, agentic AI solutions and engagement lead, Genpact.