What is Agentic AI ?

• Agentic AI represents an advanced paradigm in artificial intelligence in which systems are designed to function as autonomous agents capable of goal-oriented behavior. Traditional AI systems, including most early machine learning and language models, operate in a largely reactive manner: they receive an input, process it, and generate an output. Once the response is delivered, the interaction ends. Agentic AI, in contrast, is designed to operate continuously, maintaining context over time and actively working toward the completion of objectives rather than responding to isolated prompts.

• At the core of agentic AI is the ability to perceive, reason, plan, and act within an environment. An agentic system observes its current state, evaluates possible actions, and selects the most appropriate course of action to move closer to its goal. This process may involve breaking complex objectives into smaller sub-tasks, prioritizing actions, and dynamically adjusting plans based on intermediate outcomes. The system does not rely on explicit step-by-step instructions from a human at every stage, but instead makes decisions independently within defined constraints.

• Another defining characteristic of agentic AI is the use of memory and statefulness. Unlike stateless AI applications that treat each request independently, agentic systems retain information from past interactions, experiences, and actions. This memory enables long-term reasoning, learning from previous successes or failures, and improving future performance. Through feedback loops and self-reflection mechanisms, agentic AI systems can evaluate their own behavior and refine their strategies over time.

• Agentic AI systems are also distinguished by their ability to take actions in the real or digital world. These actions may include invoking external tools, calling APIs, querying databases, writing code, interacting with software systems, or collaborating with other agents. As a result, agentic AI systems move beyond passive information generation and become active participants in task execution, automation, and decision-making workflows.

• In practical applications, agentic AI enables the development of systems such as autonomous research assistants, AI-driven software engineers, intelligent data pipelines, and multi-agent collaborative systems. By combining reasoning, planning, memory, and action, agentic AI serves as a foundational approach for building intelligent systems that can operate with minimal supervision while solving complex, real-world problems over extended periods.

Examples of Agentic AI Systems

• To better understand agentic AI, it is useful to examine how these systems operate in real-world scenarios. An agentic AI system is not defined by a single model or algorithm, but by its ability to autonomously plan, act, and adapt while working toward a goal. The following examples illustrate this behavior across different domains.

• One common example of agentic AI is an autonomous research assistant. Such a system can be given a high-level objective, such as preparing a literature review on a specific topic. Instead of generating a single response, the agent plans a sequence of actions: identifying relevant sources, searching academic databases, extracting key points, organizing findings, and summarizing results. Throughout this process, the agent maintains memory of previously gathered information and adjusts its strategy if certain sources are unavailable or irrelevant.

• Another example is an AI coding agent used in software development. When assigned a task such as building a feature or fixing a bug, the agent analyzes the codebase, identifies relevant files, proposes a solution, writes or modifies code, runs tests, and debugs errors if the solution fails. The agent evaluates feedback from test results and iteratively improves its approach until the task is completed. This multi-step, self-correcting behavior is a defining characteristic of agentic AI.

• Agentic AI is also widely applied in data engineering and analytics workflows. For instance, a data pipeline agent can monitor incoming data, detect schema changes, clean and transform datasets, trigger validation checks, and notify stakeholders when anomalies occur. If a failure is detected, the agent can attempt predefined recovery actions or revise its processing strategy, rather than simply reporting an error and stopping execution.

• In customer-facing applications, an intelligent customer support agent demonstrates agentic behavior by managing entire support cases rather than answering isolated questions. The agent may gather information from the user, retrieve relevant policies, perform account-level actions through backend systems, escalate issues when necessary, and follow up until the case is resolved. Memory and context allow the agent to maintain continuity across multiple interactions with the same user.

• Finally, multi-agent systems provide a powerful example of agentic AI in collaborative settings. In such systems, multiple agents with specialized roles—such as planning, execution, verification, and communication—work together to solve complex problems. These agents coordinate, share information, negotiate responsibilities, and collectively adapt their strategies based on shared outcomes. This form of collective intelligence is particularly useful in large-scale problem solving, simulations, and enterprise automation.