Agentic automation

Agentic automation is the latest step in automation’s evolution. It enables software “agents,” powered by large language models (LLMs), generative AI (GenAI), and large action models (LAMs), as well as other advanced AI, to take autonomous action. Agentic automation agents can perceive their environment, reason and ask questions about it, and formulate and execute a set of actions to achieve specific goals. People do not need to structure and direct these agents’ work; rather, agents themselves can assess data, recognize patterns, formulate new questions, draw conclusions, structure processes to get the work done, and execute the work.

RPA and AI-powered automation are not going away. But agentic automation is bending the future of automation, making it even more core to business operations in a digital and AI-saturated world. Now, with the addition of agentic automation to the panoply of automation approaches, we can automate complex business processes that were previously too non-routinized, or unpredictable to be automated. That means enterprises can now fully automate complex end-to-end workflows. And they can now address the “long tail” of processes that have not, until today, been automatable. 

Agentic automation delivers the following benefits across departments and industries:

Agentic automation extends the reach of automation across a broader spectrum of organizational processes, making it possible to automate tasks that were previously too complex or nuanced for traditional methods. This technology brings the speed, extra capacity, and efficiency of automation to a wider range of slow, manual, and costly processes, including the long tail of work that traditional RPA cannot handle on its own. Agentic automation thrives in dynamic environments where rules aren't always exact. Its adaptability and dynamism means that automation can now be applied to complex workflows that involve unstructured data, pattern recognition, and real-time decision making.

While traditional automation approaches such as RPA have been successful in eliminating repetitive tasks with predefined rules, they are not as well-suited to handling less predictable, non-rules-based work. That means there may be places in end-to-end processes that must be filled via manual human intervention. With agentic automation delivered through advanced automation technology, agents can now handle these types of probabilistic, less-well-structured tasks, allowing more processes to be fully automated end-to-end. The result: a high-performing, accurate automated process on the one hand, and more time for people to focus productively on higher-impact work on the other.

For many enterprises, many decisions require rapidly analyzing massive amounts of data, drawing accurate conclusions, and taking quick action (think, for example, about real-time supply chain optimization, instant fraud identification, or on-demand next-best customer actions). Because AI agents can process and analyze data at a speed and scale beyond human capabilities—and do so in real time—they can quickly provide high-quality, accurate, and actionable insights. Moreover, with their “always on” or “on demand” analysis of information flows, AI agents help ensure that decisions are based on the most accurate and up-to-date information available.

Staying competitive requires constant forward motion. AI agents’ ability to analyze vast amounts of data, identify patterns, and make recommendations also extends to supporting innovation. Agents can autonomously carry out complex analyses that can help find new markets, effectively and efficiently launch new products, and identify ways to deliver better, faster, and cheaper goods and services. In addition, by taking on time-consuming, lower-level, and foundational analyses, agents can free up people to exercise more of their “human” ability to create, imagine, and think farther outside the box to develop truly novel ideas.

Agentic automation can empower employees by giving them an AI collaborator to tackle mundane and repetitive tasks, so people can focus on more creative and strategic work. This shift not only increases job satisfaction, but also enables employees to contribute more meaningfully to their organization’s goals. Businesses get a more engaged and motivated workforce that is freed to do “human” things like creating, imagining, and innovating—all of which are key to driving long-term success and advantage.

The most sophisticated AI agents can monitor their own performance and learn and improve over time without significant human intervention. As agents learn, processes become even more efficient and effective.

Agentic automation’s ability to consistently self-monitor not only supports continuous process improvement (see above). It also enables rapid and accurate calculation of automation impact and ROI at both task and process levels. Organizations can see how their business operations are working, and what areas might need further attention. Businesses can also gain confidence in their investment decisions and use results and insights to focus and justify additional investments.

By providing the necessary frameworks and intelligence to ensure the most appropriate and efficient models are employed for any given task, agentic automation can help businesses more fully capitalize on their AI investments and expand their AI capabilities while reducing the time to capture ROI.

Agentic automation is inherently scalable and flexible. So, whether it’s scaling up to handle increased demand, responding quickly to market disruptions, or pivoting to a new market, agentic automation provides the agility and speed needed to respond to changing business conditions. This innate flexibility is essential for enterprises looking to future-proof their operations.

Organizations have their pick of a range of approaches and automation tools to automate processes. RPA, AI-powered automation, and agentic automation all play key, distinct roles in enterprise automation.

RPA is perfect for cost-effectively and accurately handling rules-based tasks. RPA is a pro at handling structured data and following clear instructions, making it ideal for routine, predictable processes like data entry and invoice processing. These are essential tasks for most organizations, but they’re often uninteresting ones that don’t require much human judgment, creativity, or empathy.  Letting robots take on these types of repetitive activities therefore frees people up to take on higher-value work.

AI-powered automation (sometimes called “intelligent automation”) gives advanced AI skills to software robots. With these skills, robots can handle a wider range of more challenging tasks, such as understanding document content, extracting data, understanding the sentiment in an email, and the like. The AI might include techniques like machine learning, natural language processing (NLP), optical character recognition (OCR) and, more recently, GenAI and large language models (LLMs). The tasks and processes are still defined by people—but the tasks themselves require a range of AI-enabled capabilities.

Agentic automation moves automation to a new level of autonomy by harnessing the power of GenAI to give agents the capabilities to analyze unstructured data, recognize patterns, plan actions, and make decisions on their own. Rather than requiring that people tell robots exactly what to do within a defined process, agentic automation leverages the latest GenAI capabilities to enable agents (think of them as software robots with extremely high levels of cognitive skills) to act autonomously in understanding, structuring, and completing their work.

For example, a human worker could give an agent a “task prompt,” like “please compile a detailed report based on data from two different systems that includes detailed analyses, recommended actions, and rationale for these actions.” The agent would be capable on its own of determining what had to be done, where the information could be found in different systems, how the information needed to be analyzed, and the like. The agent could then build out and execute a work process.  

The answer is decidedly “no.” In any given process, there are a great many tasks and processes that RPA can and should perform. Particularly for predictable, rules-based tasks of lower complexity and variation, RPA will perform with much higher efficiency, reliability, and precision than agentic automation. As for intelligent automation, it also delivers higher computational efficiency and—crucially—higher reliability and trustworthiness than agentic automation in areas like document processing, document analysis, communications mining, and the like. For this reason, agentic automation is far more likely to partner with, rather than crowd out, RPA and intelligent automation. Think of it as a mosaic of agents and differently skilled robots working together in an orchestrated process, each doing the job that it does most effectively and efficiently.

To make this more real, consider this scenario: to complete an end-to-end process, an AI agent might call on RPA robots to perform the routinized, rule-based activities. If the process requires understanding documents and extracting information from internal and external systems, the AI agent might leverage robots with these intelligent automation skills.

While still an emerging technology, agentic automation has broad applicability across industries, departments, and classes of processes. Below are use cases where agentic automation has been successfully implemented—with the caveat that many, many more applications will rapidly emerge in the future.

Agentic automation requires a range of technological capabilities, including:

Agentic AI is often characterized as the next step in the evolution of large language models. But while LLMs are necessary, they are not sufficient. Rather, agentic AI leverages an ensemble of different AI and ML techniques. These include advanced approaches like reinforcement learning, deep learning, and supervised/unsupervised learning models to enable autonomous decision making; natural language processing and computer vision to ensure agents can leverage documents and other multi-modal data to gain an understanding of the context in which they operate; and machine learning models that analyze historical data to predict the future so that agents can anticipate the future and make proactive decisions.

A human workplace in which everyone ran around doing their own thing—and no one was particularly good at organizing their own workflows—would be a pretty chaotic and unproductive setting. The same holds true for an agentic workplace. That’s why flexible and powerful process orchestration is a must-have for agentic automation. Agents must have a “higher power” to help them coordinate tasks, manage workflows, and optimize operations to achieve predefined goals. A process orchestration capability must be able to support dynamic workflow execution—that is, ensuring that multiple agents performing a range of different tasks can efficiently work together in harmony. And it must also enable multi-agent collaboration, so that processes involving multiple agents, robots, and people happen smoothly and synchronously.

End-to-end processes almost always involve multiple systems, apps, and technologies. Therefore, orchestration capabilities should be cross-enterprise and agnostic—able to work across the entire enterprise technology ecosystem. And if different decisions are made by different agents—a process called distributed decision making—the process orchestration capability must ensure the proper sequence of decisions, actions, and input into other decisions.

A trigger is an event or activity that spurs an AI agent to take action—whether it’s the content of an email, a request from an employee, an unfavorable weather report, a signal from an Internet of Things (IoT) device, or myriad other events. They’re fundamental in initiating and guiding AI agents’ actions and are essential in ensuring that agents can react dynamically without a human jump-start. Therefore, every agentic system requires the capability to identify triggers and alert the correct agent(s). This requires a capability for continuous and accurate surveillance of processes, inputs, activities, and internal and external events.

From gathering data across systems for input into agentic AI models to actually performing a range of tasks at an agent’s behest, RPA is a critical capability for executing agentic automation. Think of RPA robots as the force that carries out a great majority of tasks throughout an end-to-end agentic workflow.

Agentic systems need to be able to autonomously and automatically learn from past experiences and adjust to improve their performance. Much as people learn from their mistakes, agents must be able to monitor themselves for errors—for instance, when interpreting unstructured data, discovering patterns, or making context-based decisions—and use that feedback to adjust and optimize. This capability requires combining an automated feedback/assessment process with AI and ML models.

To make the right decisions, an AI agent needs to understand the environment in which it is operating. This can include everything from business rules and policies to historical decisions to specific information about customers, products, partners, or suppliers to the company’s values and behavioral norms. Agentic systems must have a means of providing their agents with the relevant context and situational understanding they require to decide, understand, predict, and act. Automated processes for allowing agents to access this contextual data need to be built into agentic systems.

Assisted prompt engineering is essential for enhancing the effectiveness of agentic systems, especially those that rely on LLMs for natural language understanding, decision making, or interaction. Assisted prompt engineering enhances people’s ability to design and optimize prompts. Better prompts allow agentic AI systems to achieve greater precision, act more contextually, and meet key business goals while minimizing errors and biases. The result: more productive, adaptive, and user-friendly agentic automation.

AI agents can act as virtual coworkers and highly intelligent assistants (e.g., copilots) but to be maximally effective, they must have the ability to interact with people naturally, through intuitive interfaces such as chatbots, voice assistants, and the like. In addition, agentic systems should make incorporating and using human in the loop processes simple and easy—so people can quickly identify and respond to exceptions and performance issues.

AI systems often handle sensitive data that must be protected from insider and external threats. Therefore, they require security tools powered by AI that can detect and mitigate these risks autonomously—without constant human monitoring and intervention. In addition, systems need to ensure that automated processes and AI-generated decisions are fair, unbiased, and compliant with legal and regulatory standards. Therefore, agentic AI systems should include both autonomous auditing and monitoring capabilities and support for human governance, including visibility, monitoring, and controls.

While agentic automation offers tremendous potential for businesses, its implementation comes with challenges and considerations that require careful attention.