What is agentic automation?
Agentic automation refers to a new generation of intelligent automation systems that use AI agents capable of reasoning, learning, and acting independently across digital environments. Unlike rule-based robotic process automation (RPA), these systems leverage large language models, generative AI, and reinforcement learning to make automation more adaptive and human-like.
Agentic automation combines the strengths of artificial intelligence, machine learning algorithms, and intelligent automation to enable AI-driven agents that can analyse data, make contextual decisions, and collaborate with humans. This shift represents the evolution from simple task automation to automation that understands business intent, context, and outcomes.
Within an AI-enabled contact centre, for example, agentic automation enhances customer service by letting digital agents interpret customer intent, access CRM data, and recommend next actions without manual intervention.
How does agentic automation work?
Agentic automation works through a network of AI-native agents that interact with applications, users, and data sources in real time. These agents rely on large action models, natural language processing, and decision trees to process inputs and generate meaningful actions.
Here’s a simplified breakdown:
- Data inputs: Information flows from business workflows, enterprise applications, and Internet of Things devices.
- Processing and reasoning: Large language models and deep learning frameworks interpret the data and form responses.
- Decision-making: Machine learning algorithms and predictive models evaluate automation decisions, maintaining decision-making fairness and reducing bias.
- Execution: AI systems trigger automation workflows, orchestrate digital agents, and deliver real-time automation outcomes.
These capabilities enable multi-agent orchestration, human-agent collaboration, and adaptive behaviour that evolves with every user interaction. Integration with tools such as CRM, process mining, and intelligent document processing allows a seamless exchange of structured and unstructured data.
What are the benefits of using agentic automation?
Agentic automation transforms traditional business functions by bringing autonomy and intelligence into daily operations. The key benefits include:
- Smarter decision-making: AI-driven agents process real-time data to predict next steps, identify errors, or trigger escalation flows.
- Improved customer experience: Digital workers and customer service chatbots provide faster, context-aware responses while maintaining a human tone.
- Efficiency and accuracy: Automated dispute categorisation, payment exception operations, and fraud detection reduce human error.
- Scalability: Agentic systems manage thousands of automation workflows simultaneously, optimising workforce capacity.
- Cross-domain integration: The technology connects business process management tools, enterprise software applications, and legacy systems under one automation cloud.
In industries such as government and healthcare, housing, and retail, these systems deliver efficiency, compliance, and data accuracy in processes that depend heavily on information flow.
What’s the difference between agentic automation, AI-powered automation, and RPA?
RPA focuses on predefined rule-based actions such as filling forms or moving data between systems. It works best in structured, repetitive workflows.
AI-powered automation uses artificial intelligence and machine learning to interpret unstructured data, apply logic, and automate decision-making processes. It adds intelligence to traditional automation but still depends on human oversight.
Agentic automation extends these concepts by giving AI agents autonomy. They can reason, learn from past actions, and work collaboratively with humans and systems. Through techniques such as reinforcement learning and prompt engineering, agentic automation adapts dynamically to user interactions, unlike static RPA bots.
While RPA automates “what” needs to be done, agentic automation understands “why” an action is required, aligning automation features with business goals and customer experience outcomes.
What are some common applications of agentic automation?
Agentic automation is being adopted across industries to simplify complex digital workflows. Common applications include:
- Customer service: Intelligent chatbots handle live interactions across omni-channel platforms, analyse tone through natural language processing, and suggest personalised replies using generative AI.
- Dispute management: AI agents manage dispute processing features in payment exception operations, ACH payment dispute processing, and Visa dispute management. They categorise cases and route them through appropriate resolution paths.
- Fraud detection: Machine learning models identify unusual patterns, reducing fraud claims and improving compliance with financial regulations.
- Human resources: Intelligent document processing streamlines recruitment processes and automates repetitive approval tasks for Business Users.
- Education and training: In higher education, agentic automation enhances student query management and administrative workflows through AI-powered scheduling.
Agentic automation can also be found in customer care, lending institutions, and lending dispute processes, improving accuracy and reducing turnaround time.
What infrastructure and technology are required to support enterprise-level agentic automation?
To support enterprise-scale automation, organisations require a robust AI infrastructure combining large-scale data processing and governance controls.
Essential components include:
- AI models and deep learning frameworks for real-time monitoring and predictive decision-making.
- Process mining and business process management systems to identify optimisation opportunities.
- AI Gateway and governance tools that manage data security and compliance with regulatory frameworks.
- Automation Cloud platforms to orchestrate multi-agent workflows and integrate with enterprise software applications.
- Collaboration tools such as Microsoft Teams integration to improve transparency between automation developers, Citizen Developers, and Business Users.
By linking workforce optimisation systems with AI-powered analytics, enterprises can balance automation with a human-centric approach, ensuring better control and governance across automation programs.
What challenges come with implementing agentic automation?
While agentic automation offers significant advantages, it also brings new challenges.
- Complex orchestration: Coordinating AI-driven agents and legacy systems requires advanced integration expertise.
- Data governance: Protecting sensitive information and maintaining structured evaluation sets is critical for compliance.
- Bias and fairness: Decision-making bias in machine learning models must be monitored through structured scoring and evaluation strategies.
- Change management: Adapting to an automation landscape that includes digital agents and multi-agent orchestration demands organisational readiness.
- Skill development: Automation program managers, automation developers, and business users must learn to build, evaluate, and monitor AI-native agents effectively.
To overcome these barriers, organisations often establish a Center of Excellence to oversee design, testing, and orchestration processes while aligning automation goals with ethical and governance standards.
Agentic automation use cases
Agentic automation has found success in several real-world scenarios:
- Retail banks: Automating credit and debit card billing disputes through smart dispute workflows and AI-powered categorisation.
- Healthcare operations: Using intelligent document processing for patient data validation and fraud detection.
- Public services: Streamlining dispute management processes in payment types and fraud claim resolutions.
- Education and housing: Improving case management and communication flows between departments.
- BPOs and Not-for-proft: Enabling remote agents and digital workers to handle large data sets securely through accessibility tools and multi-lingual support.
When combined with AI-powered agent support, organisations can deliver more intelligent customer interactions while ensuring secure transactions through secure payments.
The future of agentic automation
Agentic automation will continue evolving as AI systems gain autonomy through self-learning mechanisms and real-time data orchestration. The emergence of large action models, multi-agent orchestration, and Gen AI frameworks will enable seamless human-machine interactions and collaborative automation workflows.
Future developments will integrate agent-powered automation with enterprise applications, business workflows, and intelligent dispute management tools such as Pega Smart Dispute Agentic Automation. The focus will shift from simply automating tasks to enabling AI systems that think, collaborate, and optimise outcomes continuously.
Final thoughts
Agentic automation represents a major step in the evolution of intelligent automation. By blending large language models, machine learning, and human collaboration, it transforms business workflows and customer experiences. As organisations expand their automation landscape, this approach will redefine efficiency, accuracy, and scalability across industries.
If you want to explore how agentic automation can support your digital transformation journey, request a demo.
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