What is Agentic Automation?

Agentic automation is related to AI agents. They can decide and take actions autonomously. This type of automation is different from the traditional one. The traditional one works under predefined rules and workflows. On the other hand, the agentic automation is adaptable. It can learn and optimize its behavior on dynamic environments and aims.

It is important to underline that agentic automation is on very early stage of development. But the processes are quickly evolving and are going forward to human-augmenting automation, and in the future to fully automated systems.

What led to Agentic Automation?

Since antient times people are aiming to develop machines that can replace their work. A recent milestone is the Industrial Revolution, the creation of electricity and lastly the computers that form the evolution of the field in the last century.

The evolution of artificial intelligence has a number of reasons for the breakthrough of automation technologies advancement. Before the emerging of AI automation had initially a very high cost, because the systems are rule-based. They don’t have the ability for dynamic reasoning that humans have. Systems like that are at high cost because they require meticulous design. Traditional robotic process automation (RPA) are non-agentic systems. They work well on structured, repetitive tasks, because they operate on a linear, static way. They lack awareness and can not work in dynamic environment. RPA do not have the ability to reason. When change is applied to a given scenario, they tend to break down. They are not equipped to learn or adapt to new scenarios.

Furthermore, traditional robotic process automation systems can not handle complex and unstructured inputs, because traditional computer systems cannot understand human language and don’t have the same production abilities. Automated systems are controlled statically. If a user wants to change something in the system, a slider has to be moved manually or a box has to be checked via an interface.

What is “paradox of automation”? The automated system efficiency is related to the human contribution of the operators. If there is an error in the automated system, it multiplies until a human involves to fix it.

What are large language models? LLMs are AI model automation driven by advanced machine learning algorithms. Large language models are a huge improvement, but AI systems that are non-agentic are still reactive. The work only if they are instructed and they follow narrowly defined prompts. For example, in the trading business, a forecasting model can predict a demand spike, but further prompting is needed for reordering stock, notifying sales teams or adjusting delivery timelines. The refiguration and retraining are expensive and time-consuming in order to introduce new contexts into the automated systems.

What are the advantages of agentic automation?

The creation of agentic process automation is a major step in automation develop ment because agents are defined as adaptive and more importantly, they can make decisions that are based on  real time data. According to these features human intervention is drastically reduced unlike the large language models (LLMs). Business goals can be divided on parts by the agents. Agents can also prioritize the goals and execute them in a sequence that evolves based on real-time context. The results reflect on the complex workflows positively with more intelligent automation of the processes.

The feedback of the environment reflects on the adaptability of the agentic AI technologies. The performance is improving because real-time data is incorporated and this leads to decision-making processes. Another advantage is that the response of unexpected disruptions is dynamic.

A lot of non-agentic AI models can not cope with data that is unstructured, such as, emails, documents or open-ended language. Agentic systems stand out with using NLP – natural language processing and genAI – generative AI. This gives them the opportunity to comprehend complex inputs. It also makes them function more like humans. When the agents are not certain how to cope with a given sitiation, they are able to use methodologies called human-in-the-loop, so they can obtain human validation.

Each agent can specialize in a specific type of task when working together with other agents in a so-called “multi-agent AI orchestration.” Multiple agents can integrate with applications, APIs and external systems. This way they can accomplish complex automated workflows.

How does it work?

The basis of agentic automation is combining several technologies that complete tasks which usually require human intervention. It is important to underline that not all agents have these capabilities. Advanced automations require several AI agent types.

What are the components of AI agents?

The first one is called perception. Agentic AI starts with collecting data from its environment through sensors, APIs, databases or other interactions. This process makes sure that the system is up-to-date with information for data analysis and can act further.

The second component is called reasoning. When the data is already collected, the AI process it in order to extract meaningful insights. AI uses NLP, computer vision and other capabilities. It analyses queries from users, detects patterns and comprehends wider context. This process is helpful to define what actions AI to take in a certain situation.

 The third component is called goal-setting. The agents set objectives that are defined by either goals or inputs of a user. Secondly, the agents develop strategy to achieve these goals. The ways this happen are by using decision trees, reinforcement learning or other planning algorithms.

The forth component is called decision making. The agent evaluate multiple possible actions. It has to choose the best one that is defined on several factors – efficiency, accuracy and predicted outcomes.

The fifth component is called execution. After a careful selection of an action, the agents perform execution in two ways – interacting with external systems (APIs, data, robots) or giving replies to users.

The sixth component is called learning. Learning is defined by evaluation of the outcome and the gathered feedback. This way future decisions will be improved and be more precise. The strategies of the agents are refined continuously through either reinforcement learning or self-supervised learning. The agents become more effective in coping with similar tasks in the future.

Agentic automation use in business

Agents could be used in virtually any business sector, but in certain areas they are an emerging automation tool.

Agentic automation in Finance

AI-driven systems could cope with tasks as invoice processing, fraud detention, financial reporting and compliance monitoring.

Agentic automation in Healthcare

Automation platforms can coordinate patient data intake, insurance eligibility checks, appointment scheduling and billing processes.

Agentic automation in Supply chain optimization

Agentic systems can monitor real-time data across multiple domains, from inventory levels to shipping logistics to vendor performance metrics.

Agentic automation in Human resources

Agentic AI can coordinate the entire onboarding process from parsing resumes to scheduling interviews.

Agentic automation in Customer experience

Agentic automation is used also a chatbot in customer support for faster interactions.

Agentic automation in IT support

Agentic bots are able to process IT tickets, run diagnostics, reset passwords and escalate issues.

Maisa AI

Maisa Studio works in three directions.

The first one is to build a digital worker. Business users can create, test and deploy digital workers. No technical skills are required to build them in a natural language. The employees can provide feedback to refine accuracy and performace.

After deploying the digital workers they can be used across all teams and departments, so the adoption will be consistent through the organization.

The second direction is managing a digital workspace. The digital workers can be monitored and improved in one platform – Maisa Studio. Each worker automatically exposes an API, that makes it easy to share across teams.Digital workers can integrate in own interface for a flowless employee experience.

The third direction is called “Trust the outcome”. Maisa Studio makes sure that every digital worker acts transparently, it is predictable and within guardrails. Every step follows a code-driven path for consistency and accuracy. Workers detect and resolve issues automatically. Built-in checks eliminate errors and noise.

Maisa AI can be used in various industries: Banking and financial services, Insurance, Engineering and Infrastructure, Manifacturing.

For detailed information about Maisa AI in every of the mentioned industries click here: https://maisa.ai/