AI Agent Definition

An AI agent is more than a simple chatbot or scripted workflow. At its core, an agent is a system designed to act autonomously, interact with its environment, and pursue goals on behalf of a user or organization. Unlike traditional applications that only respond when explicitly directed, agents can take initiative, adapt to new information, and operate in extended loops until they determine their objective is complete. This shift—from purely reactive systems to proactive and autonomous ones—is what makes AI agents one of the most significant developments in artificial intelligence.

Autonomy and the Execution Loop

OpenAI formally defines an agent as: "An agent is an AI application consisting of a model equipped with instructions that guide its behavior, access to tools that extend its capabilities, encapsulated in a runtime with a dynamic lifecycle." Simply put, they say:

Agent = Model + Instructions + Tools + Runtime

Expanding on this framework, Prashant Mital, Solutions Architect at OpenAI, describes an agent as "an AI application that consists of a model that has some instructions, usually in the form of a prompt, access to some tools for retrieving information and interacting with external systems, all encapsulated in an execution loop whose termination is controlled by the model itself." This captures the fundamental structure: the model receives input, decides whether to call a tool, integrates the results, and determines whether to continue working or to stop. That loop of observation, reasoning, and decision-making creates the foundation for autonomous behavior. Instead of simply returning a single answer to a single prompt, the agent governs its own process until the task has been resolved or its goal has been achieved.

This loop also highlights an important contrast with older approaches. A chatbot is reactive; it requires a prompt, provides a response, and waits again. An agent is proactive; it continues working toward a defined outcome, making decisions along the way. This autonomy is what enables agents to take on more complex and ambiguous tasks that cannot be resolved in a single step, which is why building AI agent workflows requires a fundamentally different approach than traditional programming.

Proactivity and Adaptability

AI agents are proactive systems that can adapt to changing circumstances. A chatbot might help you search for flights if you ask, but an agent can take the broader goal of “plan a trip to Rome” and carry it forward. It will look for flights, compare hotel options, propose itineraries, and adjust its plan if new information changes the conditions. For example, if it discovers that a heat advisory is in effect during the chosen travel dates, the agent can respond to this environmental change by reorganizing the itinerary around indoor activities, suggesting a hotel with a pool, or proposing tours in the early morning and evening when it is cooler. This demonstrates the fundamental loop of observing, reasoning, and acting, not just once but continuously until the objective is satisfied.

The ability to observe, adapt, and act in an ongoing cycle is what elevates an agent beyond a static tool. It is not merely executing a fixed script; it is reasoning in context and revising its own plan to achieve a goal under dynamic conditions.

Goal-Directed Systems in Context

Conceptually, an agent can be understood as a system with autonomy and the ability to act in order to achieve one or more goals within an environment. This definition is broad enough to capture the many forms agents may take. The environment might be a digital workspace, a set of APIs, or even the physical world. The goals may be explicitly assigned by a user, dynamically updated based on changing inputs, or emergent as the agent reasons through a problem. What defines the agent is not the underlying model—though large language models are the most common enablers today—but its orientation toward goals and its embeddedness in a context where actions have consequences.

This situatedness matters because it means agents are not abstract reasoning engines disconnected from reality. They are embedded actors, working within constraints and opportunities, oriented toward outcomes rather than just outputs. That orientation is what makes them useful collaborators and not merely sophisticated calculators.

Defining What an AI Agent Is

Taking these perspectives together, an AI agent can be defined as an autonomous system that observes its environment, reasons about information, and acts in pursuit of goals. It is guided by instructions, often expressed in natural language, and empowered with tools that allow it to extend beyond its core reasoning engine. It operates within an execution loop that allows it to continue working until its goals are achieved, and it is situated in an environment where it can perceive, adapt, and act. This combination of autonomy, adaptability, and goal-directed behavior is what makes AI agents distinct from chatbots, scripts, and other forms of software.

Why AI Agents Matter

The rise of agents signals a shift in how work is delegated to machines. Autonomy allows them to handle long-running and complex workflows without constant supervision. Proactivity allows them to adapt to evolving problems and ambiguous situations. However, building AI agents presents unique challenges, and traditional cloud infrastructure often struggles with these stateful, autonomous systems—which is why AI-native infrastructure has become essential for production deployments. Goal-directed behavior ensures that they remain aligned with user intent even as circumstances change. For these reasons, agents are increasingly being adopted as collaborators, taking on meaningful tasks across customer support, research, software development, and countless other domains.

AI agents are not just a new kind of application. They are a new paradigm for how humans and intelligent systems work together—one where the software doesn’t just wait to be told what to do, but actively participates in achieving goals.