What Are AI Agents? Definition, Examples, and How They Work (2025 Guide)

What are AI agents

AI agents are autonomous software systems that use artificial intelligence to perform tasks and achieve goals on behalf of users or organizations. In other words, AI agents act as digital assistants or “workers” that perceive their environment, make decisions, and take actions with minimal human intervention. They often rely on large language models (LLMs) or other advanced AI models as a “brain” to understand inputs (text, voice, sensor data, etc.), plan a sequence of steps, and then execute those steps using tools or APIs. For example, AWS defines an AI agent as “a software program that can interact with its environment, collect data, and use that data to perform self-directed tasks” toward a goal. Similarly, Google Cloud notes that AI agents pursue goals proactively: they set sub-goals, plan actions, and can even decompose complex problems into smaller tasks without step-by-step instructions. In practice, AI agents draw from trusted data (such as corporate knowledge or real-time APIs) and continuously learn from feedback, allowing them to adapt and improve over time (through methods like reinforcement learning).

Unlike a simple program that follows fixed rules, an AI agent has autonomy and goal-driven behavior. It can operate continuously after an initial prompt, making choices at each step without needing a person to guide every action. The agent is rational, meaning it uses data and past experience to make informed decisions that maximize its chance of success. It is also adaptive: as it encounters new situations or receives feedback, it updates its plans and strategies to better achieve its objectives. In short, AI agents combine perception, reasoning, planning and action. They perceive their environment via inputs (text, images, APIs), plan tasks with an AI core (often an LLM), execute actions via external tools, and then learn from the outcomes to refine future decisions.

By leveraging these capabilities, AI agents can tackle multi-step workflows and complex problem solving without requiring detailed instructions for each step. For example, a Travel Planning AI Agent might autonomously book flights, reserve hotels, and arrange ground transportation by gathering data from travel websites – all based on a single high-level user request. In essence, an AI agent extends the power of an AI model from just answering questions into actually doing work on its own.

Key characteristics of AI agents

AI agents are distinguished by several defining features (many of which come from classic AI theory):

• Autonomy: After an initial command or objective is given, agents continue operating without constant human oversight. They can schedule tasks, make decisions, and adapt on their own.

• Goal-driven behavior: Agents pursue explicit goals or utility functions. They evaluate the likely outcomes of actions and choose those that best advance their objectives. For example, an inventory management agent might automatically reorder stock to minimize shortages and costs.

• Perception and interaction: Agents collect data from their environment via sensors, user inputs, or connected systems. This could mean reading emails, listening to audio, querying databases, or using web APIs. The agent uses this information to perceive the world and inform its decisions.

• Reasoning and planning: Agents don’t just react; they think ahead. Using algorithms or prompt-based planning, they break complex goals into subtasks and formulate a sequence of actions. This reasoning process may be iterative: an agent plans one step, executes it, then re-plans based on new information.

• Memory: AI agents typically have memory structures. They maintain short-term context (e.g. conversation history, recent observations) and long-term memory (e.g. user preferences, past data). This memory allows them to recall past interactions and use historical data to guide future decisions. For instance, an agent that handles customer support can remember prior requests from the same user to provide continuity.

• Learning and adaptation: Agents improve over time by learning from successes and mistakes. After each task, they can update their internal models or plans. Techniques like reinforcement learning or continual fine-tuning help agents refine their strategies on-the-fly. This means an AI agent can get smarter as it gains more experience on the job.

• Tool usage: A hallmark of modern AI agents is tool integration. Agents use external tools, APIs or even other agents as “extensions” of their capabilities. For example, an agent might query a web search tool, call a weather API, or invoke a database query to get information needed for a task. By leveraging tools, agents can perform actions in the real world (like sending emails or fetching data) that the underlying AI model alone could not do.

• Collaboration (multi-agent): Often, multiple AI agents collaborate in a system. An orchestrator agent might coordinate several specialized agents, each handling a subtask, to solve larger problems. For example, in a business process, one agent might analyze data while another drafts communications. They share results and align towards a common goal.

Combined, these traits make AI agents far more powerful than simple chatbots or script-driven automations. They are proactive and adaptive software entities, continuously sensing, planning, acting, and learning to fulfill objectives.

Types of AI agents

AI agents can also be categorized by capability. Classic AI theory (e.g. Russell & Norvig) identifies five basic agent types: simple reflex, model-based reflex, goal-based, utility-based, and learning agents. Simple reflex agents act on current input with fixed rules (e.g. a thermostat turns on heat if it’s cold). Model-based agents maintain an internal model of the world to handle more complexity. Goal-based agents add planning – they set goals and evaluate actions by how well they achieve those goals. Utility-based agents even use preferences or scores to make optimal choices. Finally, learning agents improve from experience and can handle uncertainty. In modern contexts, most advanced AI agents are learning agents that incorporate planning and utilities to optimize performance. Importantly, all these types can be combined in multi-agent systems, where each agent is specialized for a task.

Today the buzz is on autonomous/agentic AI. An autonomous AI agent can operate over long time-horizons without human prompts. As Salesforce explains, autonomous agents “continually improve their own performance through self-learning, and without the need for human intervention”. In practical terms, autonomy means the agent takes initiative: once given a high-level objective, it defines sub-tasks, uses tools, and adapts its plan until the goal is reached. Some agents are semi-autonomous, requiring occasional human approvals; fully autonomous agents can run end-to-end. The field of agentic AI focuses on systems that make their own decisions using memory, reasoning, and planning (often referred to as “agentic components”).

AI agents also vary by interaction style. Some are conversational agents (chat-based helpers), others are background agents that work without direct user input (automating workflows or data analysis). They can be single-agent systems or multi-agent ecosystems. In a single-agent setup, one agent tackles the whole task, which is best for clearly defined jobs. In multi-agent systems, agents specialize and collaborate to tackle complex, distributed problems. For instance, one AI agent might handle scheduling, another handle communications, and an orchestrator agent ensures they work in concert.

How AI agents work

At a high level, AI agents follow a perceive–plan–act–learn cycle. Under the hood, most modern AI agents rely on large language models or similar “foundation models” as the reasoning engine. Here’s a typical workflow:

1. Perceive: The agent gathers information from its environment. This could be user instructions, data from databases, sensor readings, or real-time inputs like voice or visuals. For example, an AI assistant agent might read an email or listen to a spoken command.

2. Plan (Reason): Using its AI core (often an LLM), the agent interprets the information and formulates a plan. It breaks down the user’s goal into smaller tasks. This planning may involve chain-of-thought reasoning, decision trees, or heuristic algorithms. Agents continuously reassess plans; IBM calls this agentic reasoning, where the agent self-corrects and updates strategies as it gathers new information.

3. Act: The agent executes the plan by performing actions through tools. For instance, it might invoke an API (like a calendar app to schedule a meeting), manipulate a spreadsheet via code, send a query to a database, or control a robot arm. The tools bridge the agent’s reasoning with the external world. The agent does not output just text; it performs tasks.

4. Learn: After acting, the agent evaluates the outcome. It uses feedback (success metrics, user responses, or reward signals) to adjust its internal models. Over time, through reinforcement learning or continual fine-tuning, the agent refines how it plans and acts, improving with experience.

Building an AI agent requires integrating several components. A typical agent architecture includes:

• LLM/AI core: The foundation model that understands and generates language, enabling reasoning and communication. This is the “brain.”

• Memory module: A system (often a vector database or knowledge graph) where the agent stores short-term context (current session data) and long-term knowledge (historical data, documents). This allows context persistence and personalization.

• Planning engine: Software (or prompt structures) that decomposes goals into actionable steps, either via symbolic planning or by prompting the LLM to “think” step-by-step (as in the ReAct paradigm).

• Tool registry: A suite of external tools, APIs, or microservices the agent can call. These might include web browsers, data APIs, calculators, or custom scripts. The agent learns how and when to use each tool.

• Execution module: The interface that allows the agent to perform tasks (e.g. by making API calls, controlling hardware, or generating output).

• Feedback/learning loop: Mechanisms (like reinforcement signals or user ratings) that feed back into the agent, enabling it to adjust its strategies and improve accuracy.

In sum, an AI agent is more than a static program. It is a dynamic system that continuously senses the world, reasons about what to do, takes actions using connected tools, and learns from the results. This closed loop – perceive, plan, act, learn – is what turns AI models into autonomous agents.

AI agents vs AI assistants vs chatbots

It’s important to distinguish AI agents from related concepts: AI assistants and chatbots. While these terms overlap, they imply different levels of capability and autonomy.

• Chatbots (rule-based automation): These are programs that follow predefined scripts or simple rules. They usually respond to user prompts with canned answers or execute simple scripted flows. Chatbots cannot learn or deviate from their programming; they only react when triggered by a user. Think of a support bot that answers FAQs – it only knows what it was coded to know.

• AI assistants: These are often conversational interfaces (like Siri, Alexa, or Copilot) that use AI to enhance user productivity. Assistants respond to voice or text commands and can perform tasks on request, often by calling APIs (e.g. “schedule a meeting” or “set a reminder”). They understand natural language and can do limited planning, but they generally wait for explicit user instructions for each task. An AI assistant typically does not create its own subgoals or act without a command.

• AI agents: In contrast, AI agents are proactive and autonomous. They don’t just wait for instructions – they set and pursue their own sub-goals to achieve a high-level objective. As IBM explains, the difference is like a movie star’s assistant vs the star’s agent. A personal assistant (Siri or Alexa) follows your orders. An agent, however, is like a talent agent: it actively works towards maximizing outcomes (like increasing the star’s fame or earnings) without needing you to spell out every step. IBM summarizes: “AI assistants are reactive, performing tasks at your request. AI agents are proactive, working autonomously to achieve a specific goal by any means at their disposal.”.

In practical terms, this means AI agents can plan multi-step processes on their own. For example, after initial instructions to “organize my event,” an AI agent might first draft a schedule, then book vendors, and finally send invites – all without further human prompts. Google Cloud similarly notes that agents have the highest degree of autonomy, whereas assistants remain largely user-driven. In short, every agent is an AI assistant (in the broad sense), but not every assistant is an autonomous agent. Agents extend assistants by adding self-management, planning, and initiative.

Examples of AI agents in action

AI agents are already being used across many industries. Here are some concrete examples of AI agents in action:

• ChatGPT Agent (OpenAI): In 2025, OpenAI introduced an “agent” mode for ChatGPT. This AI agent can autonomously browse the web, extract information, and complete tasks end-to-end. For instance, ChatGPT Agent can “navigate websites, filter results, prompt [the user] to log in, run code, conduct analysis, and even deliver editable slideshows and spreadsheets” based on a single instruction. This represents a leap from a conversational bot to an actual agent doing work.

• Customer Support Triage: Some companies deploy AI agents to handle incoming inquiries. For example, an insurance company might use an AI agent to read customer emails or chats, categorize issues, and route them appropriately. According to AI21 Labs, a customer inquiry triage agent “reviewed a surge of support tickets ... and reduced the need for human intervention by 80%,” improving resolution speed by 40%.

• Fraud Detection Agent (Mastercard): Mastercard uses an AI agent called “Consumer Fraud Risk” to monitor payment data in real-time and stop scams. This agent analyzes large-scale transaction data, identifies suspicious patterns, and alerts banks before funds are lost. After deploying this AI agent, one bank (TSB) reported it “dramatically increased its fraud detection”, potentially preventing nearly £100 million in annual losses.

• Smart Home / Robotics: In smart homes, AI agents can autonomously manage devices. An agent might learn a household’s schedule and proactively adjust lights, climate, or energy usage without being asked. Similarly, in robotics, an AI agent can take sensor data (vision, LIDAR) and navigate a robot or autonomous vehicle to accomplish tasks like delivery or cleaning. (For example, an AI driving agent continuously perceives its environment and makes split-second decisions for safe navigation.)

• Personal Productivity: Personal AI agents are emerging that act like “digital concierges.” For instance, an AI travel agent can autonomously plan an entire trip: researching destinations, comparing flights/hotels, making bookings, and adjusting plans on the fly. BotSonic (by Writesonic) and CustomGPT are platforms that allow creation of such agents – e.g., a BotSonic agent can be configured to handle customer chat, order processing, or content creation tasks. (Learn more about these tools in the “Building AI agents” section.)

• Marketing and Sales Automation: Companies like NVIDIA report using agents to personalize marketing at scale. An AI marketing agent can “ingest vast amounts of customer data” to tailor campaigns. It might write promotional emails, adjust ad spend, analyze engagement metrics, and automatically iterate on strategies without human intervention.

• IT and Operations: In IT helpdesks, AI agents can triage tickets. An agent reads technical issue reports, queries knowledge bases, attempts automated fixes (like restarting a service), or escalates when needed. In finance departments, agents reconcile accounts by scanning invoices and matching payments.

These are just a few examples. In general, anywhere there is a routine but complex workflow – from customer service and finance to manufacturing and healthcare – AI agents can be applied to automate and optimize processes. Salesforce reports that autonomous agents are used in insurance to update coverage, process claims, and even issue payments entirely without human intervention. As the technology matures, we expect many more real-world AI agent deployments.

Benefits of AI agents

Implementing AI agents can yield significant advantages:

• Increased productivity: By automating repetitive tasks, AI agents free human workers to focus on higher-value work. AWS notes that delegating routine tasks to agents makes business teams “more productive” since staff can then concentrate on mission-critical or creative activities. For example, an agent that assembles and formats reports on its own can save employees hours of work. Agents can also run in parallel (multiple agents working simultaneously), dramatically increasing throughput without more manpower.

• Cost reduction: Autonomous agents cut costs associated with manual errors and inefficiency. Because agents follow consistent models and optimize processes, they reduce waste and rework. AWS points out that agents can minimize unnecessary costs from process inefficiencies and human mistakes, resulting in significant savings. For instance, an AI agent handling data entry or scheduling eliminates many manual hours and the overhead of hiring extra staff.

• Better decision-making: AI agents can analyze far more data and factors than a human can. They gather and process real-time information at scale, enabling faster, data-driven decisions. The AWS documentation highlights that AI agents’ predictive and analytical capabilities allow managers to make more informed predictions and strategize effectively. For example, an AI finance agent can analyze market trends in milliseconds to advise on investment moves, or an operations agent can forecast demand to adjust inventory ahead of time.

• Improved customer experience: Agents can deliver personalized, on-demand service around the clock. By handling queries and transactions autonomously, they speed up responses and reduce wait times. AWS notes that businesses integrate AI agents to “personalize product recommendations, provide prompt responses, and innovate to improve customer engagement”. A concrete example: instead of waiting on hold for a human representative, customers interact with an AI agent that understands their account, resolves their issue, or guides them through purchasing – providing instant and accurate assistance.

• Consistency and reliability: Unlike humans, AI agents do not get tired or distracted. They follow their optimized model consistently, ensuring that tasks are done the same way every time. This leads to higher quality and fewer errors in routine processes (as long as the agent is well-designed).

• Scalability: Once developed, AI agents can be scaled easily. An enterprise can deploy dozens of agents in parallel or replicate an agent across regions without the delays of hiring/training new employees. This means companies can grow capabilities rapidly when needed (e.g. during seasonal spikes) by simply allocating more AI agent resources.

Overall, AI agents augment human teams by handling volume and complexity. They serve as “digital workers” that complement human intelligence. As AWS puts it: “business teams are more productive when they delegate repetitive tasks to AI agents”, freeing people to add more strategic value.

Challenges and limitations

Despite their promise, AI agents also come with challenges:

• Lack of human judgment: AI agents struggle with tasks requiring deep empathy, moral reasoning, or nuanced understanding. Complex social interactions (e.g. therapy, conflict resolution) and ethical decisions (e.g. legal judgments) remain beyond current AI. Google Cloud notes that tasks involving nuanced human emotions or high ethical stakes are problematic for agents. In these domains, agents may make inappropriate or biased choices without human oversight.

• Data and context limitations: Agents are only as good as the data they have and the tools they can access. In unpredictable environments or when faced with out-of-distribution scenarios, agents can fail. For example, in highly dynamic physical environments (like disaster response), an AI agent may struggle with real-time physical adaptation. Agents may also hallucinate or provide incorrect outputs if their underlying models lack knowledge. Ensuring access to up-to-date, accurate data is critical.

• Complexity and cost: Building and running sophisticated AI agents can be resource-intensive. Training foundation models and maintaining an orchestration of agents require significant compute and engineering effort. Google Cloud warns that sophisticated agents can be computationally expensive, limiting use for smaller organizations.

• Security and safety: Agents that can act autonomously pose new risks. If an agent is compromised or makes an unexpected decision, it could cause harm (e.g. unauthorized transactions, privacy breaches). Ensuring agents only act within safe boundaries and monitoring their actions is an ongoing concern in the field.

• Trust and transparency: Users may find it hard to trust an agent whose decision-making is opaque. Explaining why an agent made a particular decision or linking its actions to human-understandable logic is difficult. This transparency issue can slow adoption in regulated industries.

These limitations mean that, for now, many AI agents are deployed in supervised or semi-autonomous modes. For example, the Salesforce “Agentforce” systems they describe still escalate to humans for approvals on critical steps. As the technology matures, researchers and companies are also working on governance frameworks to ensure agentic AI remains safe and aligned.

Building and using AI agents

For developers and businesses interested in AI agents, there are multiple approaches and tools:

• Frameworks and libraries: Open-source frameworks like LangChain enable building agentic pipelines by connecting LLMs with planning logic and tools. MetaGPT, AutoGen, ReAct, and others are patterns or frameworks for structuring agent architectures. These allow customization of how the agent reasons and calls tools. The IBM Think article notes that paradigms like ReAct (Reasoning + Action) help agents think aloud and iterate on actions. Developers can thus implement agents by combining LLMs with planning modules (e.g. chaining prompts) and hooking up APIs.

• Cloud services: Many cloud providers offer agent-building platforms. For instance, OpenAI’s API and chat interface now support “function calling” and agentic capabilities. Similarly, Microsoft and Google have announced agent tools. These services simplify hooking an LLM to external tools (like web APIs or databases). Often, one can define “tool definitions” and the agent learns to call them.

• No-code platforms: For non-technical users or quick prototypes, no-code platforms exist. For example, CustomGPT is a platform that lets you create custom GPT-powered agents on your own data (a knowledge-based chatbot agent). ChatBot.com provides a visual interface to build conversational agents for websites and customer support workflows. Botsonic by Writesonic offers an AI bot builder that can act as an on-site assistant or information agent. These tools use underlying LLMs and allow configuration of agent goals, triggers, and responses, making it easier to spin up an agent without coding.

• Example workflow: To create an AI agent, one typically defines an overall goal and a set of possible actions or tools. The agent’s LLM is given a “persona” or role description and tool specs. When the user issues a command, the agent’s loop is triggered: it may first query a knowledge base, then plan steps, call APIs (like sending an email or querying a database), and finally present results. Developers can program custom “tool functions” that the agent calls via prompts, or use standardized APIs (e.g. OpenAI’s function calling). Over time, one can refine the agent by tweaking prompts, adding memory, or retraining on feedback.

In short, building an AI agent usually involves integrating an LLM with tools and memory. Key steps include: choosing a base model (e.g. GPT-4, Claude), defining the agent’s domain and tasks, setting up tool-access (code that the agent can invoke), and implementing feedback loops. Many companies are now offering AI agent building blocks, making it easier than ever. For instance, AutoGPT and LangChain have popularized the concept of agents that autonomously use GPT-4, and enterprise AI suites (like IBM Watson Orchestrate) focus on composing agents for business workflows.

Future trends in AI agents

Looking ahead, AI agents are poised to become even more widespread. A few trends:

• Agentic AI mainstreaming: Big AI players are embedding agentic features into consumer products. OpenAI’s ChatGPT Agent (July 2025) shows this trend – now any ChatGPT user (on certain plans) can enable “agent mode” to have the model act autonomously. In the near future, it will be common for AI assistants to include toggles for autonomous mode, effectively turning them into agents for complex tasks.

• Multi-agent ecosystems: We will see more systems where multiple agents coordinate. For example, an AI office suite might include different agents for email, scheduling, and document editing, all working together to manage a project. Research (and companies like IBM and Salesforce) are exploring “agentic workflows” where chained agents handle end-to-end processes.

• Advances in learning: As foundation models improve and tools become more powerful, agents will tackle increasingly sophisticated tasks. Future agents may use reinforcement learning with human feedback to autonomously discover new strategies.

• Regulation and ethics: With greater autonomy comes scrutiny. Governments and industries are beginning to define standards for “AI agents” and their safe use. We may see regulations requiring agents to log their decisions and respect privacy.

• Hardware integration: Agents will not just be software on the web. We’ll see more physical robots and Internet-of-Things devices controlled by AI agents – for example, a home cleaning robot that plans and carries out chores by itself, or drones that autonomously handle deliveries.

In essence, AI agents are an evolving paradigm in AI: moving from passive tools to active collaborators. We are at the cusp of an agentic AI era, where AI systems take real initiative. By 2025 and beyond, most organizations will likely employ some form of AI agent for automation and intelligence augmentation.

Frequently Asked Questions

What are some examples of AI agents?

AI agents appear in many domains. For instance, OpenAI’s new ChatGPT Agent can browse the web and complete multi-step tasks (planning meetings, buying tickets, etc.) autonomously. In finance, Mastercard’s “Consumer Fraud Risk” AI agent scans transactions in real time to detect payment scams. In customer service, AI agents (powered by companies like Salesforce) triage support tickets and resolve issues end-to-end. Personal digital assistants (like virtual concierges) that book travel or manage email are also AI agents. Even self-driving cars and warehouse robots use AI agents to sense their environment and decide actions. In short, any system that uses AI to plan and act on its own (rather than just follow human commands) is an example of an AI agent.

How do AI agents work?

AI agents follow a loop of perceive–plan–act–learn. They use a core AI model (often a large language model) to understand inputs and goals. First, the agent perceives the environment (e.g. reading data or listening to a command). Then it plans by breaking the goal into subtasks and reasoning through options. Next, it acts by executing tasks through tools or APIs (for example, querying a database, writing an email, or controlling a device). Finally, the agent learns from the outcome: it checks results against success criteria and updates its strategy. Over time, the agent’s memory and decision-making improve with reinforcement learning or feedback loops. This cycle enables the agent to handle complex workflows autonomously.

How do AI agents differ from AI assistants or chatbots?

AI agents are more autonomous and proactive than assistants or bots. A chatbot or rule-based bot typically just responds to user commands and follows a fixed script. An AI assistant (like Siri or Alexa) uses AI to understand requests, but still waits for instructions each time. In contrast, an AI agent sets sub-goals and takes initiative. As IBM explains, assistants are reactive (performing tasks only on request) whereas agents work independently to achieve goals. In practice, this means an agent can carry out a multi-step process on its own: for example, after being told “plan my event,” an agent would automatically research options, send invites, and adjust details – all without step-by-step prompts. In summary, every agent can be thought of as a supercharged assistant: it can think ahead, act without being asked at each step, and modify its plan dynamically.

How can I build or create an AI agent?

Creating an AI agent involves linking an AI model to tools and logic. One approach is to use frameworks (like LangChain, ReAct, AutoGPT, etc.) that combine a large language model with planning code. You define an initial goal, a set of available tools (APIs or scripts), and the agent’s role or persona. The agent’s AI core then uses this information to call tools in sequence, based on its reasoning. For example, you might write a program that prompts GPT-4 to output which tool to use next, then executes that tool, and loops. Alternatively, no-code platforms make this easier. For instance, CustomGPT lets users create custom agents (GPT-based bots) on their own data, and ChatBot.com provides a visual interface to build chat-based agents. Botsonic by Writesonic similarly enables quick creation of conversational AI agents. These tools typically handle the integration of the AI model with memory and actions, so you can configure an agent without deep coding.

Tip: Whichever method you choose, focus on clearly defining the agent’s goal, limiting its action space (what tools it can use), and providing feedback. Iteratively test and refine the agent’s prompts and tool definitions. Over time, incorporate memory (store past interactions) so the agent can build context.

How do AI agents learn and improve?

AI agents learn through feedback and data. After each task, the agent evaluates its success (for instance, did it complete the task or satisfy the user?). It can then adjust its strategy. Many agents use reinforcement learning: they get reward signals (e.g., points for correctly solving a problem) and optimize their policies. Others learn through continuous updates: for example, an agent might retrain its underlying model on transcripts of past successes and failures. Memory also plays a role – by storing past cases, the agent can recall what worked before. Over time, this iterative learning makes agents more accurate and context-aware.

Agents can also learn from human-in-the-loop feedback. Some systems allow humans to correct an agent’s actions, and those corrections are used to fine-tune the agent. In practice, well-designed AI agents steadily get better at their tasks as they experience more examples and refine their decision-making processes.