Agentic AI: The Future of Autonomous Systems

Defining Agentic AI

Autonomous systems funded by Agentic AI that operate as goal-oriented, independent agents capable of planning, making decisions and executing actions with little or no human involvement. Traditionally AI systems can only respond to an individual input or carry out a narrowly defined function. Conversely, an agentic AI system can take a larger problem and decompose it into smaller tasks, carry out those tasks across different tool sets and/or environments, and adapt their operation to the results of their task. The emergence of agentic AI systems represents a significant evolution in AI technologies from passive assistants to strong problem solving capabilities.

What is Agentic AI

Agentic AI is based on the feedback loop of perception, reasoning, action and learning. The first step involves the system interpreting the user's desired outcome or input from the surrounding environment. The next step is to form a plan of action using an approach such as large language models to reason through the problem(s). The actions taken may involve making API calls, writing code or interacting with an application, which the agent observes as feedback to the plan. As the agent observes the results of its actions, it can make adjustments to its plans, repeating until the goal has been achieved.

Many existing systems that run on agentic principles contain many different components, including (but not limited to) memory modules for retaining historical context related to its environments, algorithms used to decompose tasks based on current available data and tools that allow an agent to interface with external systems such as databases, applications and Web services. These orchestration features produce an element of functionality and freedom that traditional chatbot applications cannot deliver.

Advantages of using AI robots as agents:

Agentic AI provides the following benefits: 

Most importantly - productivity: Agentic AI agents can perform multi-step workflows (i.e. research, create reports, scheduling, analysis) independently from humans, thus eliminating many manual labor hours and facilitating faster decision making.

Secondly, agentics is adaptable: Agentic AI agents monitor different activities and respond to results based on them, which helps them succeed in environments where conditions are changing or uncertain (i.e. constantly changing environments). In addition, they can work at any hour of the day or night, allowing them to be useful for monitoring, providing customer service automation, and running DevOps processes.

Finally, scalability: Many agents can be deployed doing the same task in parallel to complete large amounts of work, which would be expensive and/or time-consuming to accomplish solely through human workforce.

Examples of How Agentic AI Can Be Used in the Real World

Organizations are now utilizing agent-based AI agents in many of their operations. In software development, agents can run tests, debug code and make recommendations for improvements to the code. Customer support agents have taken over the traditional role of answering FAQs by automating the end-to-end process for handling support inquiries/customer support ticket requests. Financial services organizations use agent-based AI systems to monitor their portfolios and send out risk alerts to financial professionals. Marketing organizations use agent based AI systems for conducting market research and optimizing their campaign strategies.

Productivity is one of the fastest growing areas for agents; agent-based AI systems can assist in planning travel, managing email and summarizing documents while coordinating calendars to complete tasks across multiple applications acting like personal executive assistants.

Challenges and Risks Associated With Agentic AI

The introduction of agent driving AI systems introduces many new types of risk to organizations. Increased autonomy creates a greater likelihood of unintentional actions being taken if the types of goals assigned to those agents is not properly defined. There are many concerns related to security issues, privacy concerns and alignment issues between agent-based AI systems and human intent. All systems capable of performing real world actions must have proper guardrails, monitoring and human-in-the-loop controls.

Reliability is another major concern with agent-based AI systems, as multi-step reasoning processes can actively propagate minor errors into greater error rates if proper evaluation and testing methodologies have not been developed. As such, there are still many evolving evaluation and testing methodologies, so reliability rates cannot be formally reported at this time.

The Future of Autonomous Systems

AI systems with agency are pushing machine intelligence towards becoming more self-sufficient and working more collaboratively than before. As computers develop better reasoning, memory and ability to use tools, we can expect them to replace experimental technology with the norm of working next to us as digital employees or "co-workers" in the new space of machine intelligence. Organizations that start early designing, managing and safely deploying autonomous systems will thus have a distinct competitive benefit as this technology continues to evolve into the future.

The transition from reactive AI to true agency in our systems represents not simply an advancement of existing technologies; but also a total re-definition of the relationship between humans and machines (railroad dominance versus coexistence).