We’ve all heard the promise that traditional automation systems, like Robotic Process Automation (RPA) or AI automation, will eliminate manual work and transform your business operations. This stands true, but only to a certain extent.

The traditional automation systems run on rule-based, rigid frameworks, which limits their ability to adapt to changing environments and make independent decisions. As a result, these systems need more human involvement, deviating employees from focusing on essential tasks. 

For example, if a "fully automated" process still requires five people to monitor it, three analysts to fix daily errors, and an entire IT team to maintain it, your team's productivity will drop significantly. 

To tackle this challenge, organizations need an automation solution that can adapt to changing environments, understand the context, reason through various scenarios, and require less human oversight. 

This is where agentic automation comes to the rescue. 

In this blog, we'll explore what agentic automation is, how it differs from traditional automations, how it works, its use cases, and how businesses can leverage agentic automation to boost their operational productivity.

Agentic automation is an advanced form of automation that enables intelligent AI agents to manage complex, end-to-end business processes autonomously.

Unlike traditional robotic process automation (RPA), which relies on predefined rules to execute repetitive tasks, agentic automation systems are powered by advanced AI technologies.

These include large language models, generative AI, and machine learning, which can learn, adapt, and make decisions based on real-time data.

To acknowledge the value of agentic automation, it's crucial to understand how it differs from other automation methods, mainly Robotic Process Automation (RPA) and AI-powered automation. The table below provides insights:

Robotic Process Automation (RPA)

RPA uses intelligent automation to automate repetitive administrative tasks, such as data extraction, form filling, file relocation, and more. It mimics a human's steps to complete a task by following strict, predefined rules and interacting with the digital systems in the same way a human would.

This approach is best suited for automating repetitive tasks. However, RPA relies on rule-based workflows, which is a significant limitation. 

RPA cannot learn or adapt to new scenarios without significant reconfiguration and manual intervention, which limits its flexibility in today’s dynamic business environment. 

AI-powered automation

AI-powered automation builds on the capabilities of RPA by incorporating advanced artificial intelligence techniques. This approach uses technologies such as machine learning, natural language processing (NLP), optical character recognition (OCR), and even generative AI models to tackle tasks that involve unstructured or semi-structured data. 

For example, AI-powered automation can understand the content of a document, extract key information from emails, or gauge the sentiment behind customer messages.

However, the key point here is that humans still define tasks. 

Agentic automation

Agentic automation is the evolutionary step, moving from rule-based task automation to executing tasks with complete autonomy. This approach utilizes generative AI, including LLMs and advanced reasoning frameworks, to enable AI agents to operate with a higher degree of independence. 

While RPA and AI-driven systems need constant human intervention to set parameters or handle exceptions, agentic process automation runs with minimal oversight. It executes tasks, makes context-aware decisions, adapts to changing conditions, and can even initiate new actions.

Agentic automation operates by orchestrating a series of intelligent, autonomous actions. The process begins with an external trigger—such as a new customer inquiry or a scheduled task—that initiates data collection. And we’ve explained what happens after that in a step-by-step breakdown of the agentic process automation:

Data collection and integration

The system aggregates data from diverse sources, including structured enterprise databases and unstructured inputs like emails or social media. Advanced tools, such as natural language processing (NLP) and large language models (LLMs), integrate this information into a unified data pool.

Analysis and insights

The next step after data collection is analyzing it and extracting insights. This involves using machine learning algorithms to process the data and identify patterns, trends, and anomalies. 

Generative AI models add to this phase by providing predictive insights that guide the automation system in evaluating possible outcomes and adjusting strategies accordingly. 

Adaptive decision-making

With these insights, AI agents use a mix of predefined rules and adaptive algorithms to choose the best course of action for performing the task. They analyze the available information and real-time data to assess the priority and context of each task and then decide the best order to address each task. 

Task execution and orchestration

Once the approach for the tasks is set, the system executes these tasks. The AI agents connect with external applications and APIs to implement the action plan. 

Orchestration ensures that each action happens in the correct sequence. It also handles any waiting times or dependencies between tasks. For example, if one task must be finished before the next can start, orchestration enforces that rule.

Real-time adaptability

Agentic process automation systems monitor the process for unprecedented scenarios during the task execution. If something unexpected happens, like a data error or a processing delay, the agents adjust instantly to keep the workflow moving.

Continuous learning and optimization

After successfully executing the tasks, the system reviews the entire process. It checks if each step went smoothly and where the system encountered problems and gathers this data to improve further.

According to Gartner, by 2028, 15% of day-to-day work decisions will be made autonomously, with agentic AI in place. How does this benefit organizations? Let’s find out: 

Scalability of complex tasks

Traditional automation methods struggle with cognitive skills such as data and context interpretation. This limits their ability to perform complex tasks and requires frequent manual interventions. 

Agentic automation changes this dynamic by harnessing powerful AI agents that can parse unstructured data and adapt their methods as the workflow evolves.

Improved decision-making

Agentic process automation eliminates the guesswork in decision-making by constantly analyzing incoming data, spotting trends, and suggesting the best possible actions in real time. This leads to faster and more accurate decisions.

What’s more? 

Because these systems learn and adapt over time, they get better at reading the bigger picture—be it changing business goals or new market conditions. This means future decisions are always context-aware, accurate, and in tune with organizational goals.  

Enhanced adaptability

One area in which agentic automation excels is its ability to adapt to changing environments during workflow execution. 

While executing a task, the system may encounter an unexpected event that may halt the entire process. Instead of following fixed rules, it adapts to dynamic situations by course-correcting and continuing workflow execution. 

On a practical level, this means connecting to the existing software companies through APIs so that tasks are coordinated smoothly across different applications.

Organizational cost savings

In traditional automation, companies use rule-based systems that handle simple, repetitive tasks but break down when the tasks get complicated. When this happens, an employee must intervene to make suitable adjustments, which takes time and resources. This constant need for oversight and reconfiguration drives up costs.

Agentic automation uses intelligent AI agents that continuously learn and adapt. These agents handle complex tasks and unexpected changes without human intervention, leading to fewer mistakes, less time spent on manual fixes, and lower labor expenses. Enterprises can then use this time and resources for more critical initiatives.

Agentic automation has brought significant potential to extend the capabilities of traditional automation technologies. Let’s see how businesses can adopt agentic process automation to streamline various business functions: 

IT operations

Agentic automation has great potential to enhance IT operations. It introduces intelligent, autonomous workflows to automate routine and complex IT tasks. 

Your enterprise can automate system maintenance, incident management, IT service management, and more by implementing agentic automation in the IT department.  

For IT service management, it streamlines the IT ticketing processes. It categorizes and prioritizes incoming support requests based on their urgency and complexity. Through Gen AI chatbots, agentic process automation independently auto-resolves repetitive queries like password resets and software installation. This results in faster response times and frees IT support teams to focus on solving critical problems.

 HR support

Agenic automation takes over repetitive tasks like responding to common employee queries and updating employee records, as well as complex tasks such as onboarding, offboarding, payroll processing, and leave management.

Through agentic automation, HR teams can get data-driven insights into employee engagement, workplace trends, and performance metrics. This helps them with strategic workforce planning and identifying retention issues and skill gaps. 

For example, it can autonomously handle offboarding an employee. It ensures the company assets are returned, makes final payroll adjustments, gathers feedback from exit interviews, and deactivates the employee account. 

Finance and accounting

Finance teams struggle with analyzing large volumes of data, frequent regulatory changes, and the pressure to make accurate decisions quickly. Mundane tasks like invoice processing, account reconciliation, and compliance checks leave little time for finance teams for strategic planning or proactive risk management.

Agentic automation helps finance teams overcome these challenges by introducing AI-driven workflows to handle large volumes of transactional tasks in real time. These tasks include budgeting and forecasting, tax compliance, risk management, expense management, financial reporting, and more. 

For example, agentic automation helps in risk management by continuously monitoring financial transactions to identify fraudulent activities. When the system encounters malicious behavior, it sends real-time alerts and takes appropriate action, such as blocking the account, to prevent further damage. 

Supply chain management

Agentic automation streamlines supply chain management by providing real-time insights and autonomous decision-making. 

With agentic process automation, enterprises can automate processes like demand forecasting, inventory management, logistics management, and supplier relationship management.

For example, if there is a supply delay, it automatically adapts to this unexpected scenario, plans the following best action, such as reconfiguring the route or sourcing an alternative vendor, and alerts the respective team about the issue. This keeps the supply chain agile and helps businesses manage a steady flow of goods to meet consumer expectations. 

Marketing operations

Marketing teams can use agentic automation to perform tasks such as audience segmentation, personalized content generation, campaign optimization, email marketing, social media management, and customer journey mapping. 

When running campaigns, for example, it constantly monitors performance metrics, such as click-through rates and cost per acquisition, based on which it adjusts the strategies to stay in tune with the market changes.

Not so long ago, building autonomous agents and agentic workflows depended on custom code developments, a team of AI experts, and intensive resource allocation, which was inaccessible for most businesses.

Today, with advancements in generative AI capabilities, many platforms have emerged on the market that offer plug-and-play agentic process automation. However, not all solutions might be suitable for your business needs. 

When choosing an agentic automation platform, consider one that meets your enterprise's complex needs and supports a wide range of agentic workflows. The platform should also allow automation teams with or without deep AI knowledge to quickly build and customize AI agents.

Let’s discuss the key features you should look for in the platform: 

User-friendly interface

A platform with no code and an intuitive user interface helps reduce the learning curve and promote adoption. 

Look for a platform that offers a drag-and-drop workflow builder, pre-built templates, and other intuitive tools to build AI agents. This accessibility shortens deployment time, reduces costs, and improves user experience. 

AI choice and flexibility

Your platform must offer a variety of AI models. This allows you to choose the right one for each situation; for text generation, you can select GPT-4o, and for tasks that require high-speed data processing, you can switch the model accordingly. 

Look for platforms that allow you to train these models using your organizational data. This will ensure that the outcomes are relevant to your business.

Integration with enterprise systems

Ensure your chosen platform seamlessly integrates with popular enterprise applications such as Workday, Okta, Jira, and BambooHR.  This connection is key to agentic automation, enabling AI agents to execute cognitive tasks at scale. 

Data and analytics

While evaluating the automation platforms, check whether the tool can provide you with actionable insights through real-time analytics dashboards.

You should be able to track metrics like the number of issues resolved, MTTR, FCR, number of unresolved queries, and CSAT scores. Having clear visibility into these metrics helps leaders identify areas for improvement and devise strategies to improve workflows.

So far, we’ve discussed agentic automation, its workings, benefits, and key features to look for in a platform. However, as much as it offers benefits, we’ve to understand that it also comes with complexities and limitations that enterprise leaders must consider.

Integration complexity 

Implementing agentic automation means integrating the AI platform with your current tech stack. But, the stark reality is that legacy systems, existing APIs, and integration platforms were not built for AI agents' use. This limits the agents' ability to pull information from external databases and execute workflows independently. 

Businesses must invest time and resources in developing integration frameworks that standardize data inputs and facilitate smooth communication. Without careful planning and execution, these complexities in connecting external systems can undermine the efficiency and responsiveness that agentic process automation is meant to deliver.

Data governance

Implementing agentic process automation in your business would involve giving the system access to confidential information. When these systems connect to other business applications, they can be vulnerable to security risks. 

To protect the enterprise data, you need to use strong security measures such as access controls to limit who can see or change the data and regular audits to check whether security practices are followed to meet compliance. 

Change management

Employees and stakeholders may resist the move from traditional automation to magnetic automation because they are used to the old way of working, may experience a steep learning curve, or perceive agentic automation as a threat to their jobs. 

To tackle this challenge, organizations should conduct training sessions and provide real-life examples of how agentic process automation will make their daily tasks easier. This approach helps employees realize the value of the new system, let go of negative emotions, and ensure a smooth transition.

The leading LLMs, such as OpenAI’s o1, Anthropic’s Claude 3.5, and Google’s Gemini Pro 1.5, are becoming more advanced. They offer larger context windows, advanced reasoning, and support for multiple modalities, which together enable AI agents to be better at reasoning and planning for executing complex tasks, more like a human would.  

At the same time, new user-friendly agentic tools are emerging. These platforms require far less custom coding and facilitate deeper integrations with intelligent automation systems, effectively breaking down data silos. 

Over time, agentic process automation systems will become more efficient in self-learning, which will improve human-AI collaboration. These systems will automate complex tasks at a large scale with minimal human intervention.

Workativ meets the need for advanced enterprise workflow automation. Our platform offers an intuitive drag-and-drop interface, connects with popular enterprise tools such as Zendesk, Jira, BambooHR, and Workday through API and connectors, utilizes reasoning layers, and enables enterprises to fully automate multi-step processes like onboarding, leave management and IT ticketing.

With Workativ’s Knowledge AI, enterprises can unify their data sources by connecting their internal knowledge base, external sources, and Workativ KB. This allows AI agents to pull relevant information seamlessly and execute actions without hiccups. 

Book a demo now to see how Workativ can help you increase agency in your enterprise through agentic automation.

What is agentic process automation?

Agentic process automation enables intelligent AI agents to autonomously manage complex, end-to-end business processes. These systems are powered by large language models, generative AI, machine learning, and other AI technologies that allow them to learn, adapt, and make decisions based on real-time data.

What is an example of agentic AI?

One example of agentic AI is in customer support assistance. It uses advanced language models to understand customer requests, retrieve relevant information from various systems, and generate personalized responses.

What is the difference between RPA and agentic AI?

RPA relies on fixed, rule-based scripts to perform repetitive tasks such as data entry without adapting to new scenarios. 

In contrast, agentic AI uses advanced AI capabilities, like large language models, machine learning, and generative AI, to understand context, learn from experience, and make autonomous decisions.

What are AI agentic workflows?

AI agentic workflows are end-to-end processes managed by intelligent AI agents. These workflows automatically collect data, analyze it in context, and execute actions across multiple systems. They are designed to adapt in real-time, continuously learn from feedback, and handle complex tasks without constant human oversight.

What are the benefits of agentic process automation? 

Agentic process automation helps scale complex tasks, improves decision-making, enhances adaptability to unprecedented environments, and reduces organizational costs on labor and resources.