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Mar 21, 2025

12 minutes read

AI agents: Everything you need to know

Discover what AI agents are, how they work, and their practical uses in solving key business challenges.

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The proliferation of AI agents marks the next phase of AI, taking us beyond the question-answering capabilities of popular virtual assistants to systems that can autonomously execute tasks. Although interacting with AI virtual assistants can be useful to get quick answers, AI agents represent a more advanced application: they don't just respond to your questions; they also proactively help to solve problems on your behalf.

For leaders looking to augment operational efficiency and drive new levels of productivity, AI agents offer a compelling proposition. Some experts now view AI agents as a necessary step toward Artificial General Intelligence (AGI)—which refers to a type of AI that can perform any intellectual task that a human can do—as these systems demonstrate the rational, “intelligent” behavior that distinguishes sophisticated AI from the hype cycle.

But what exactly is an AI agent? And how can it help to make business operations more efficient, improving productivity, and powering smarter analysis and decision-making? This article provides an overview of how AI agents can support better business operations.

##### What is an AI agent and how does it work?

AI agents are software systems that can autonomously pursue goals and complete tasks. Unlike basic AI applications that respond to prompts, AI agents demonstrate reasoning, planning, and memory capabilities, with enough autonomy to take actions, learn from experiences, and adapt to new situations.

The concept has historical roots in AI research, dating back to the mid-20th century when researchers began exploring computational entities with qualities such as autonomy, reactivity, proactiveness, and social ability. These intelligent systems today are capable of perceiving their surroundings through sensors, making decisions, and taking contextually relevant actions via actuators (the component of an agent that carries out actions based on the agent’s decisions). They can operate across physical, virtual, and mixed-reality spaces.

AI agents are primarily defined by their ability to autonomously complete tasks through planning, executive, and iteration. AI agents can simultaneously process various types of information—text, voice, video, audio, and code—while conversing, reasoning, learning, and making decisions. They can also coordinate with other agents to perform complex agentic workflows.

A simple yet illustrative scenario of an AI agent in action would be email management. The agent drafts responses, schedules meetings, and follows up on email threads. In this sense, the agent becomes a proactive entity that displays intelligent behavior rather than just acting as a response tool.

##### How it works: AI agents explained

These advanced agents are built and trained over five key phases:

• Data collection: AI agents begin by gathering data from diverse sources, such as customer interactions, transaction histories, and social media. This data collection provides the context necessary for the agent to understand most user needs. AI agents can integrate and process this information in real time, ensuring they work with the most current data available. For example, a marketing AI agent might autonomously collect and publish campaign metrics on a weekly basis through connectors as part of the intelligent search and retrieval system.
• Data processing: Once collected, the agent analyzes this information to extract meaningful insights. This involves contextual analysis to understand patterns, recognize relationships between different data points, and compare performance metrics against expectations. During this stage, the agent might request business context from a human operator to ensure precise interpretation of the data.
• Decision-making: Based on the processed information, the agent makes decisions about how to proceed. This step often involves generating recommendations or action plans. For instance, a marketing agent might write a report proposing campaign optimizations. Human operators can review and refine these recommendations prior to implementation.
• Action: With a plan in place (and often after receiving human approval), the agent executes the required tasks. This could involve answering customer queries, updating platforms with new settings, or facilitating transactions. The execution is designed to be efficient and accurate, delivering results to users.
• Adaptation: Perhaps most importantly, AI agents continuously learn from every interaction, refining their algorithms to improve effectiveness. They update their knowledge base and incorporate feedback to help improve future performance. This ongoing learning ensures agents remain relevant, even as business requirements change and evolve.

##### Types of AI agents

AI agents come in various forms, but they all share a common characteristic: they interact with their environment to achieve specific outcomes. Think of agents as digital workers with varying levels of sophistication—some are rule-based (using predefined instructions to act), while others (such as those based on machine learning) can learn and adapt over time. Let's break down the main types of AI agents you're likely to encounter.

###### Reflex agents

These are the simplest of the bunch—they react to what's happening right now without considering past events or future consequences. Imagine a smart thermostat...