An AI agent is a goal-driven software system that perceives data, plans actions, and executes tasks autonomously using techniques such as LLM reasoning, memory, and tool integrations. It breaks complex tasks into steps and can call external APIs or systems to complete work without constant human prompts.

How is an AI agent different from an AI assistant or chatbot?

Unlike reactive assistants and rule-based chatbots, AI agents are autonomous and goal-oriented. They plan multi-step workflows, adapt to failures, and proactively take actions, while assistants typically respond only to direct user requests.

What are real business uses for AI agents?

Examples include proactive customer support that resolves issues end-to-end, automated sales outreach and lead qualification, predictive maintenance in manufacturing, and multi-step tasks like booking travel or onboarding employees.

Which platforms help build AI agents quickly?

No-code and low-code platforms such as CustomGPT, Chatbot.com, and Botsonic (Writesonic) let teams prototype and deploy agentic chatbots and agents by connecting documents, APIs and workflows without heavy engineering.

How do I start building an AI agent for my business?

Define a clear goal, gather the necessary data and tools, choose an agent platform or framework, implement memory and tool integrations, and run tight pilots with human-in-the-loop validation before scaling.

Are AI agents safe to run autonomously?

With proper guardrails—permission controls, auditing, prompt constraints, and human oversight—AI agents can be safely deployed. Start with limited scopes and clear rollback procedures to reduce risk.

What Is an AI Agent? | Complete Guide to Agentic AI (2025)

pengarhehe
Aug 12
8 min read

What Is an AI Agent?

AI agents are specialized artificial intelligence systems that autonomously perform tasks on behalf of users. In practice, an AI agent acts like an intelligent worker: it perceives its environment, plans a course of action, and uses its capabilities to achieve a goal. For example, Google Cloud explains that “AI agents are software systems that use AI to pursue goals and complete tasks on behalf of users,” featuring reasoning, planning, memory and enough autonomy to make decisions and adapt. Similarly, AWS notes that an AI agent can “interact with its environment, collect data, and use that data to perform self-directed tasks that meet predetermined goals.” In other words, humans provide the objectives, but the agent itself determines how best to achieve them. IBM puts it succinctly: an AI agent is “a system that autonomously performs tasks by designing workflows with available tools.” These systems typically rely on large language models (LLMs) and other AI techniques to understand user inputs and take step-by-step actions.

AI agents can be thought of as autonomous planners or “AI workers.” They often use advanced natural language processing (NLP) via LLMs to interpret instructions and generate intermediate steps. For instance, IBM explains that agents use LLMs to comprehend and respond to user requests sequentially and decide when to call external tools or services. In practice, an agent will break a complex request into subtasks (like web searches, data lookups, or API calls), execute those steps using external resources, and then combine the results into a final output. This workflow-driven autonomy – planning and acting without constant human intervention – is what distinguishes AI agents from simpler bots or assistants. (For related concepts, see our What is Agentic AI guide and AI Agent overview on these topics.)

How AI Agents Work

AI agents combine several key components and capabilities to operate effectively:

LLM “Brain”: At their core is often a large language model that serves as the agent’s reasoning engine. NVIDIA describes the LLM as the agent’s “brain,” responsible for understanding tasks, making decisions, and selecting tools and data to achieve objectives. The LLM interprets user intent and plans a strategy (sometimes using chain-of-thought or dynamic reasoning) for accomplishing the goal.
Memory: AI agents typically have memory structures to keep context. Short-term memory tracks the current conversation or task state (the agent’s “train of thought”), while long-term memory retains relevant past information and user preferences. This allows the agent to recall previous interactions and improve over time. For example, a sales agent might remember a client’s past inquiries to personalize follow-up actions.
Planning Module: Complex goals are broken down into ordered steps. Planning modules let the agent decompose a task into actionable sub-tasks. As NVIDIA explains, planning can use structured techniques (like tree-of-thought) or feedback loops (like ReAct) to sequence actions. This way, the agent can adapt if an action fails or if new information appears.
Tool Integration: AI agents extend beyond pure language by calling external tools and APIs. They may connect to databases, knowledge bases, calculators, email systems, calendars, or other AI models to gather up-to-date information and perform actions. For example, agentic AI uses tool calling on the backend to fetch real-time data and execute subtasks autonomously. This means an agent could look up current weather, book tickets, or trigger a transaction as part of its workflow.
Autonomy & Adaptability: Agents operate without constant supervision. They make decisions and take initiative. IBM notes that, unlike traditional models that follow fixed instructions, “agentic AI exhibits autonomy, goal-driven behavior and adaptability.” In practice, agents can reevaluate plans mid-course, learn from feedback, and update their strategies to improve results.
Proactivity and Collaboration: AI agents don’t just wait for user commands. IBM’s analogy highlights this difference: an AI assistant is reactive (doing tasks on request), whereas an AI agent is “proactive, working autonomously to achieve a specific goal by any means at their disposal.”. Moreover, agents can collaborate. Multiple AI agents can work together on a problem – each specializing in a subtask – and share data to reach a common objective. An orchestrator agent can coordinate these specialists into a cohesive multi-agent system, enabling large-scale automation.

These components allow AI agents to tackle complex, multi-step problems. As NVIDIA illustrates with an example, an agent given the task “build a website” could autonomously handle the layout design, write HTML/CSS code, connect backend processes, generate content, and even debug issues – all with minimal human input. In essence, AI agents reason about goals and use tools to turn a high-level request into concrete actions.

Agentic AI

The term “Agentic AI” refers to the broader paradigm in which AI agents operate. Agentic AI systems are those where one or more agents have the capability to act on goals with limited supervision. In this context, “agentic” emphasizes independence and goal orientation. IBM defines agentic AI as an AI system that “can accomplish a specific goal with limited supervision,” consisting of AI agents that mimic human decision-making in real time. In a multi-agent agentic system, each agent may handle a piece of the task, coordinated by orchestration software.

Agentic AI goes beyond traditional generative AI. While a standard generative model (like ChatGPT) simply produces content based on a prompt, an agentic system uses those generative outputs to achieve objectives. For example, a generative model might generate text or code, but an agentic AI will take that generated text and act on it – for instance, by using it to complete a booking or modify a database. IBM notes that agentic AI combines LLM-driven generation with external tool use: it might not only calculate the best travel itinerary, but also book flights and hotels without further human intervention. In other words, agentic AI turns the “thought” of a language model into real-world actions.

Agentic systems have been made practical by recent advances in LLMs and memory architectures. They exhibit autonomy (managing multi-step goals), proactivity (taking initiative), and the ability to self-improve. For deeper insight, see our Agentic AI Optimization guide, which discusses fine-tuning and optimizing these autonomous agents. Overall, agentic AI represents a shift from passive AI tools to active problem-solvers that can iterate, plan, and execute.

AI Agents vs. Assistants and Bots

It helps to contrast AI agents with more familiar tools like AI assistants and simple bots. Think of the difference between a personal assistant and an agent: an assistant takes instructions and performs tasks on demand, but an agent works 24/7 to pursue broader goals. IBM’s analogy is apt: “AI assistants are reactive, performing tasks at your request. AI agents are proactive, working autonomously to achieve a specific goal”. In practice, this means:

AI Assistants (e.g. Siri, Alexa): These listen to user commands and complete tasks like setting reminders or answering questions. They generally require step-by-step instructions and do not take unscheduled initiatives.
AI Agents: These can operate with greater independence. They can handle multi-step workflows (like onboarding a client, processing forms, and scheduling meetings all by themselves) and can decide when to act or ask for clarification. They may use reasoning loops to update their plans without explicit user direction.
Rule-based Bots: Traditional bots follow preset rules for specific tasks (like answering FAQs). They have limited adaptability and rely on predefined scripts rather than learning or planning.

In summary, AI agents have a higher degree of autonomy and complexity. They combine features of intelligent planning, learning, and tool use that go beyond what a reactive assistant or static bot can do. For additional background on agents as a concept, see our AI Agent guide, which explores different agent types and use cases.

Applications and Examples

AI agents are finding use in many domains. In business, they can automate complex workflows that traditionally required human oversight. For example:

Customer Service: An AI agent in a call center might ask customers questions, retrieve information from documentation, and even resolve issues without human help. AWS gives this illustration: the agent “will automatically ask the customer different questions, look up information in internal documents, and respond with a solution. Based on the customer responses, it determines if it can resolve the query itself or pass it on to a human.”.
Personal Assistance: Agents can schedule appointments, manage emails, and organize travel. Imagine telling an AI agent your goals for a business trip: it could plan the itinerary, make hotel and flight bookings, and add events to your calendar – all proactively.
Website and Content Creation: As NVIDIA’s example shows, an AI agent can be tasked with building a website, autonomously handling design, coding, content generation, and even debugging.
Business Processes: From HR onboarding to supply chain optimization, agents can collaborate across departments. Multiple agents can specialize (one for inventory analysis, one for supplier communication), coordinate through an orchestrator, and streamline end-to-end processes.
Financial Services: Agents can analyze financial data, detect fraud in real time, and even execute trades based on complex strategies, updating their approach as markets change.

These examples highlight how agents can operate continuously and across stages of a task. In many cases, they learn from each iteration, improving accuracy and efficiency. For instance, an agent could use customer feedback to refine its responses or learn which actions best predict a sale.

Tools and Platforms for AI Agents

Building your own AI agent is increasingly accessible thanks to modern AI platforms. These tools provide interfaces, integrations, and templates for creating and deploying agents:

CustomGPT. A no-code AI chatbot platform that lets businesses upload their own data and create a custom agentic chatbot. It leverages advanced LLMs to power intelligent responses and can integrate with APIs or knowledge bases. Many companies use CustomGPT to turn proprietary documents into an AI agent that employees can query in natural language.
Chatbot: A visual chatbot builder that supports sophisticated AI agents. It allows drag-and-drop design of conversational flows and connects to channels like websites or messaging apps. Its backend often uses large language models and memory modules, enabling the deployment of AI agents that can handle customer queries, book appointments, or guide users through complex tasks.
Botsonic (by Writesonic): An AI chatbot solution for creating conversational agents. Botsonic provides a user-friendly interface to design AI assistants that can answer questions, generate content, or carry out tasks using LLMs. It includes features like context retention and API access, so a Botsonic agent can, for example, look up external data or trigger custom actions.

These platforms encapsulate many of the agentic components discussed above (LLMs, memory, tool integration). They allow non-technical users to build specialized AI agents by combining preset “skills” or uploading datasets. Under the hood, they typically use the same principles (LLM reasoning, planning, tool calls) that we’ve described, making it easier to prototype or deploy an AI agent without coding everything from scratch.

Key Features at a Glance

Agency and Autonomy: AI agents actively pursue goals. They can decide which actions to take and when to act without needing a user to click “next” every step.
Goal-Oriented Planning: They break down objectives into sub-tasks and sequence them. For instance, scheduling a trip involves researching dates, booking flights, and making reservations – all planned by the agent.
Learning and Adaptation: Advanced agents learn from feedback. Over time, they improve performance (IBM notes they can maintain goals and improve on multi-step tasks autonomously).
Multi-Modal Understanding: Many agents process text, voice, or even visual inputs together. They can listen to spoken requests, read documents, and update spreadsheets as part of a workflow.
Collaboration: Agents can hand off work to each other or work in parallel. Multi-agent systems allow specialized bots to communicate and solve parts of a problem cooperatively.

Getting Started with AI Agents

For those new to the field, a good way to think of an AI agent is as “AI with initiative.” Begin with a clear goal in mind. Then choose or build an agent framework (many open-source libraries like LangChain or AutoGPT exist) and connect it to the tools and data it needs. Define its persona and permissions (what it can access), and give it memory capabilities. From there, the agent can start planning and iterating.

If you’re an organization, you might try one of the mentioned platforms (CustomGPT.ai, Chatbot.com, Writesonic’s Botsonic) to quickly deploy an agent prototype. Monitor its behavior, refine its prompts, and possibly incorporate human feedback in the loop. As the agent becomes more reliable, it can operate with less oversight.

Ultimately, AI agents represent a new paradigm where AI doesn’t just assist but actively accomplishes tasks. By harnessing autonomy, planning, and integration, they can transform workflows. As IBM summarizes, AI agents and agentic AI move systems “from thought to action,” enabling them to navigate complex, real-world tasks with minimal human guidance.

Sources: Definitions and insights are drawn from leading AI industry sources. For example, Google Cloud and AWS provide foundational definitions of AI agents. IBM’s articles on AI agents and agentic AI explain the autonomy and workflow aspects. NVIDIA offers practical examples of agent capabilities, and industry guides (like IBM’s AI agents vs. assistants) illustrate how agents differ from conventional AI assistants. These sources form the basis of our explanation.