Agentic AI: From passive interaction to intelligent agents

We have been interacting with artificial intelligence since at least 2017, when voice assistants such as Alexa entered everyday use. At the time, few anticipated what would follow. The technology was largely perceived as a novelty, a machine capable of responding to spoken commands.

That perception shifted dramatically in 2022. With the introduction of ChatGPT, artificial intelligence became more conversational, accessible, and widely adopted across industries. Until then, AI systems were largely passive: users asked questions and received answers; they issued prompts and received replies. The interaction followed a predictable pattern nothing occurred unless the user initiated it.

That model is now changing.

AI agents are beginning to reshape how people interact with machines and systems. Unlike traditional AI tools, these systems do not wait for user input. They are designed to work autonomously toward defined goals. Rather than simply offering guidance, AI agents can be delegated responsibilities. Users increasingly act as project managers, while digital assistants execute tasks independently.

What may appear to be a modest shift is, in practice, a fundamental transformation in how technology is used.

What AI agents are and how they work in practice

At a surface level, AI agents can resemble conventional chatbots. Users may still interact through a familiar conversational interface. The distinction lies beneath that interface: unlike standard chatbots, this interface also functions as a control system with the ability to take action.

A common misconception particularly among non-technical users is to equate AI agents with tools such as ChatGPT, Gemini, or systems developed by Anthropic. These tools are large language models, or LLMs. Within an AI agent, LLMs typically serve as the “brain” the reasoning engine that enables natural language understanding, interpretation of vague requests, and evaluation of multiple options.

How AI agents work in practice

AI agents operate through a cycle that closely mirrors human cognition: perceive, reason, act, and learn. An agent may detect a trigger such as an email, a meeting request, a help ticket, or a user prompt. It then determines an appropriate response using a large language model, acts by deploying available tools including calendars, web browsers, corporate systems, or databases and improves future performance based on prior outcomes.

This cycle can function autonomously, reducing the need for manual approval at each step.

At its core, an AI agent is a system capable of connecting to multiple tools, breaking complex objectives into smaller tasks, selecting the appropriate resources, acting on them, and continuing until the objective is achieved or an obstacle is encountered.

A simple example is scheduling a meeting for four people. With a traditional chatbot, a user might ask for advice on wording an invitation or identifying a suitable time. With an AI agent, the user states the objective “Find a time for the four of us to meet next week.” The agent then checks calendars, proposes a time, coordinates responses, sends invitations, books a room, and escalates only if a problem arises. The difference is not intelligence alone, but autonomy and adaptability.

Why individuals adopt AI agents faster than organizations

In personal use, the risks associated with AI agents are relatively low. If an agent schedules an unintended fitness class or recommends an underwhelming restaurant, the consequences are minor and easily corrected. The benefits, by contrast, are immediate: reduced cognitive load and more time for higher-priority activities. For many users, that alone makes agentic AI transformative.

Within organizations, however, adoption is more complex. Enterprise environments involve fragmented data, layered permissions, and workflows that often exist informally rather than in documented systems. When autonomous agents begin updating records, transferring funds, or interacting with customer accounts, the stakes increase significantly. Errors raise questions of accountability and control.

As a result, the primary barrier to enterprise adoption is not technological capability, but organizational readiness. Effective implementation requires clean and well-governed data, clearly defined permissions, escalation mechanisms for uncertainty, robust security practices, and an institutional commitment to continuous improvement.

Without these foundations, failures may not only occur quietly they can be consequential. Loss of oversight increases risk, which explains why many agentic AI demonstrations and pilot projects, though compelling in theory, struggle to translate into real-world deployment. Technology is advancing rapidly, but many environments remain unprepared to support it.

Why trust remains the central issue

At its core, this shift is not solely about technology; it is about trust. Traditional systems are designed to seek permission before acting. Agentic AI introduces systems that act independently and report back later. While this autonomy can be liberating for individuals, it represents a risk that organizations must manage carefully.

Agentic AI is not a question of whether it will be adopted, but how much autonomy it will be granted, under what constraints, and with what oversight.

These systems are becoming increasingly efficient and accurate in their ability to act. The more difficult question is how quickly institutions are willing to redesign processes, safeguards, and expectations to accommodate that autonomy.