Agentic Process Automation: Increase Efficiency with AI Agents
Since current automation technology like RPA relies mostly on rules and custom AI modules, it:
- Can’t adapt to changes in processes
- Requires a significant programming and testing work to roll-out a robot.
Agentic process automation (APA) aims to change this by:
- Simplifying programming with generative AI and allowing non-developers to build AI agents
- Self-healing AI agents that can adapt to process changes
How is APA different from other automation?
Different types of automation work for different tasks. Digital automation is evolving from basic rule-based systems to sophisticated AI-driven processes. Here’s a simple look at the three main types: RPA, AI-powered automation, and APA.
Source: Bot Nirvana1
- Robotic Process Automation (RPA): RPA is best for tasks that follow clear, fixed rules. It uses software robots to do repetitive jobs, like data entry. Think of it as a helpful robot that follows a checklist perfectly. People tell the robot exactly what to do.
Source: PROAGENT: FROM ROBOTIC PROCESS AUTOMATION TO AGENTIC PROCESS AUTOMATION2
More on RPA, check out our articles in the RPA category.
- AI-Powered Automation: This type of intelligent automation (IA) technology adds AI skills to the software robots. It can handle more difficult tasks. This includes understanding what’s in a document or the feeling behind an email. People still define the tasks and processes. But the tasks themselves need more advanced skills.
For further information, read Use Cases / Examples of Intelligent Automation.
- Agentic Process Automation (APA): APA takes automation to a new level. It gives AI agents the power to make their own decisions. It uses the latest AI to let agents understand, plan, and complete their work on their own. Instead of people giving a robot a checklist, they can just give it a goal.
Use cases of agentic process automation
APA’s ability to make its own decisions and adapt in real time makes it useful for complex, changing tasks. This is different from traditional robotic process automation and other automations that follow a fixed set of rules.
Here are more specific examples of how APA is used today.
Banking and financial services
- RPA can extract numbers from invoices and enter them into accounting systems.
- APA goes further by analyzing live market feeds, spotting unusual price movements, and suggesting portfolio adjustments for each client.
APA agents can also generate personalized financial plans by combining client risk profiles, income history, and current market data. In compliance, agents can scan millions of transactions in real time and flag suspicious activity before regulators detect it.
Insurance
- RPA can auto-fill claim forms.
- APA can manage the entire claims journey: check a customer’s policy, assess the claim’s validity using external data (such as weather or accident reports), request missing documents, and communicate empathetically with the customer via chatbot.
Case study
U.S.-based commercial insurance brokerage uses Agentic AI, which reviewed insurance policies across multiple carriers. They claimed that what used to take 3 hours per policy is now done in under 30 minutes. It extracts, compares, and summarizes policy data automatically.3
Public sector
- RPA may automate document routing in government offices.
- APA can interpret large datasets such as hospital admissions, vaccination rates, or urban traffic flows.
Real-life example
In Singapore’s JTC, Facility managers work with AI agents that can detect issues like HVAC problems and test alternative fixes, helping manage building operations proactively.4
Manufacturing
- RPA might automate purchase order creation.
- APA uses predictive maintenance: an agent analyzes vibration, temperature, and machine sensor data to predict breakdowns.
Real-life example:
Siemens applied intelligent document processing to streamline its document handling, automating the processing of over 35,000 delivery note formats. The system achieved 98% accuracy and enabled touchless processing for more than 90% of delivery notes within two weeks of implementation.
This shift to AI-driven automation, utilizing generative AI and large language models, significantly reduced manual work and improved efficiency in Siemens’ document management workflow.5
Healthcare
- RPA may automate billing or appointment scheduling.
- APA can read unstructured medical notes, cross-check them with patient histories, and suggest treatment options tailored to each individual.
Agents can analyze genetic datasets to identify drug targets and simulate outcomes of new compounds, accelerating drug discovery pipelines. In hospitals, APA can prioritize patients in emergency rooms based on real-time data (symptoms, vitals, lab results).
Telecommunications
- RPA might activate new SIM cards or process simple billing tasks.
- APA can continuously monitor network traffic, detect anomalies that predict outages, and automatically re-route data flows to prevent service disruption.
Agents can even suggest optimal times to roll out software updates, minimizing downtime.
Customer support
- RPA powers simple chatbots that answer scripted FAQs.
- APA agents can hold dynamic conversations, identify customer frustration from tone, and proactively offer solutions such as discounts or callbacks.
Real-life example
H&M deployed an agentic virtual assistant on its e-commerce platforms that understands natural language, recommends products, and handles returns. It resolved ~70% of customer queries without human help, and boosted conversion by ~25%.6
Employee experience and HR
- RPA might onboard employees by setting up accounts in payroll or IT systems.
- APA can forecast hiring needs by analyzing turnover trends and business growth.
It can draft job postings, screen resumes using natural language understanding, conduct initial chatbot interviews, and even negotiate offer details. During onboarding, APA coordinates IT setup, training schedules, and benefits registration without HR needing to step in.
IT support
- RPA resets passwords on request.
- APA can read incoming IT tickets, analyze system logs, identify the likely root cause, and run fixes automatically.
For example, if a server runs out of storage, the agent can free space, archive old files, and notify the team. If an issue requires escalation, the agent compiles diagnostics and sends them to the right engineer.
Facilities and IoT management
- RPA might schedule HVAC system checks.
- APA integrates with IoT sensors in factories or hospitals. It can monitor energy usage, detect overheating equipment, and adjust conditions in real time.
Deployed sensors and AI models can predict machine failures with ~95% accuracy. This accuracy results in:7
- ~75% less unplanned downtime,
- 40% lower maintenance costs, and
- 30% longer machine life-span.
How APA works, step by step
Agentic Process Automation (APA) handles complex tasks by creating and managing its own workflows. It does this using special AI agents, which are like smart sub-programs that can make decisions during a task. Two key types of agentic AI components are the DataAgent and the ControlAgent.8
- DataAgent: This agent manages complex data tasks. A DataAgent can take a task described in simple language and complete it on its own. It handles the data side of the workflow, making sure the right information is processed and used. Even though it’s run by an agent, its data still works with existing systems. This makes the data flow more flexible and capable.
- ControlAgent: This agent manages the “if-then” logic of a workflow. It makes decisions in real time about what step to take next. A ControlAgent can choose from different options based on the data it receives. This allows the workflow to change and adapt during a task instead of just following a fixed path.
Here is a simple look at the key steps in how Agentic Process Automation (APA) works:
- Task interpretation: The process starts with an AI agent understanding a user’s instructions. These instructions can be in plain language, which makes it easy for anyone to use. The agent uses advanced language models to break down the task into smaller steps.
- Workflow construction: After understanding the task, the AI agent builds a workflow to do the work. It automatically designs the steps to make sure they are strong and effective.
- Tool integration: APA works by connecting to other tools and platforms. The AI agent can bring in any tool or API it needs to do the task. This is key for the flow of information.
- Control logic management: A main part of APA is its ability to manage how the workflow works in real-time. The agent can adjust the process on its own to make sure it performs well and handles any issues.
- Data processing: The AI agent can process and analyze data right away. This data-driven approach helps it make informed decisions. This makes sure the workflow is accurate and uses the newest information.
- Self-Improvement: A unique feature of APA is that it gets better over time. The agent watches its own work and finds ways to improve. This learning helps the system become more effective and a better way to do business.
Infrastructure and Technology for Agentic Automation
Enterprise-level agentic automation needs several key capabilities:9
- AI and ML models: Agents rely on large language models plus other methods like reinforcement learning, natural language processing (NLP), and computer vision. These allow them to make decisions, analyze data, and understand text, images, or signals.
- Process orchestration: Agents need a way to coordinate tasks and work together. Orchestration ensures smooth collaboration between multiple agents, humans, and systems across the enterprise.
- Event triggers: Agents must react to real-time events, such as an email, IoT alert, or weather change. Continuous monitoring ensures the right agent is activated at the right time.
- RPA integration: RPA bots can carry out routine tasks, feed data into models, or execute steps that agents assign. This gives agents “hands” to perform actions.
- Learning loops: Agents should learn from errors and past outcomes. Feedback loops help them improve decisions and adapt to new situations.
- Context grounding: To act correctly, agents need access to business rules, customer data, and historical decisions. This context helps them predict, decide, and act effectively.
- Prompt support: Assisted prompt engineering helps design better prompts, reducing errors and making agents more accurate and useful.
- Human interaction: Agents should interact naturally through chat, voice, or other interfaces. Human-in-the-loop processes allow people to handle exceptions and guide outcomes.
- Security and compliance: Strong safeguards are needed to protect sensitive data. Agents also need built-in monitoring and auditing to ensure fairness, compliance, and transparency.
Benefits of agentic process automation
Agentic process automation (APA) helps overcome the “paradox of automation“, suggesting that humans become more critical in fixing problems as systems become more efficient in non-agentic AI models. APA enables AI agents to act autonomously and proactively address problems without needing a human intervention to fix an issue that’s gotten out of control.
Agentic process automation increases:
- The number of processes that can be automated thanks to the increased autonomy that APA brings
- Resilience of bots since bots can rely on LLMs to resolve unexpected errors
This allows companies to get more out of typical automation benefits:
- Efficiency: Its ability to automate complex processes results in more efficient process management in compared to the existing automation tools. Complex and dynamic workflows can be used to automate not repetitive and rule-based tasks, so APA can be used in the tasks requiring cognitive abilities, which RPA is not sufficient.
- Flexibility: With its ability to understand and process unstructured data, agentic process automation can oversee workflows dynamically. It can use many different data and output them as desired.
- Accuracy: Traditional automation systems also offer a very accurate output for rule-based tasks, but in the case of complex and dynamic tasks, this changes. APA can automate tasks that require human-like intelligence. Traditional automation methods will fail to give correct outputs in those algorithms, but agentic AI systems can analyze data in more complex processes.
FAQ
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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