An AI agent is a software system that autonomously performs tasks by planning workflows and using available tools to achieve predefined goals. It acts on behalf of a user or another system, making decisions and solving problems without continuous human intervention.
AI agents go beyond basic functions such as natural language processing. They interact with external environments, execute complex actions, and adapt their behavior. These capabilities enable AI agents to handle diverse challenges in real time.
At their core, many AI agents rely on large language models (LLMs). These models process natural language and support reasoning. Unlike traditional pretrained LLMs, agentic AI supplements the model with tool access, such as web APIs and databases, enabling it to obtain fresh information and perform subtasks autonomously.
Though AI agents operate independently, their autonomy is framed by human-defined goals and rules. Three main stakeholders influence agent behavior:
- Developers design and train the agent’s system.
- Deployment teams provide user access and control environments.
- Users specify the tasks, objectives, and tools available to the agent.
Because many goals are complex, AI agents apply task decomposition. They break large tasks into subtasks, then plan a sequence of actions. For example, a vacation planning agent might divide its goal into researching flights, gathering weather data, and booking accommodations.
Tools play a vital role for AI agents. These tools include:
- External datasets
- Web search engines
- APIs for specialized information
- Other AI agents that contribute expertise
After collecting information via these tools, an AI agent updates its internal knowledge and reviews its plan. This iterative reassessment is called agentic reasoning. Through self-correction, the agent improves decision quality and adapts dynamically.
Feedback mechanisms enhance an agent’s performance. Human-in-the-loop systems and cooperation with other AI agents provide evaluations that lead to iterative refinement. Past successful solutions are stored to avoid repeating errors and speed up future decision-making.
AI agents differ significantly from non-agentic chatbots. Non-agentic bots lack memory, reasoning capabilities, and external tools. They handle only simple, short-term queries. In contrast, agentic AI chatbots manage more complex requests, plan autonomously, and learn from interactions to personalize responses.
There is no single architecture for AI agents. Two common reasoning paradigms include:
- ReAct: Agents think, act, and observe iteratively. They plan each step after considering new information and choose tools accordingly.
- ReWOO: Agents conduct upfront planning without relying on tools during execution, confirming their plan before acting.
AI agents can be classified into types based on their complexity:
| Agent Type | Description | Example |
|---|---|---|
| Simple Reflex | Acts on current perception using fixed rules; no memory. | Thermostat with condition-action rules. |
| Model-Based Reflex | Maintains an internal model with memory; updates based on perception. | Robot vacuum cleaner navigating obstacles. |
| Goal-Based | Plans actions to achieve specific goals using a world model. | Navigation system finding fastest route. |
| Utility-Based | Chooses actions that maximize user-defined utility (cost, time, efficiency). | Navigation optimizing fuel consumption, tolls, and time. |
| Learning | Adapts by learning from experience; updates knowledge base autonomously. | E-commerce system offering personalized recommendations. |
Real-world use of AI agents is broad. They appear as virtual assistants, mental health support tools, interview simulators, and more. In healthcare, multi-agent systems collaborate to assist in treatment planning and medication management, demonstrating their problem-solving capability in complex domains.
Key takeaways:
- AI agents autonomously perform tasks by designing workflows and using tools under human-defined goals.
- They combine reasoning, planning, and interaction with external resources for complex decision-making.
- Large language models support agents, enhanced by external tool access for current information.
- AI agents decompose complex tasks into subtasks, then plan and execute them strategically.
- Feedback and learning mechanisms improve agent performance over time.
- Agentic AI differs from simple chatbots by supporting autonomy, memory, and long-term adaptation.
- Various agent types exist, ranging from simple reflex to learning agents.
- Applications include customer service, healthcare, virtual assistance, and problem-solving systems.
What defines an AI agent?
An AI agent is a system that performs tasks autonomously. It designs its workflow and uses available tools to act on behalf of users or other systems.
How do AI agents handle complex tasks?
They break down complex goals into subtasks. Planning allows the agent to create step-by-step actions for efficient problem-solving and decision-making.
What role do tools play in AI agents’ functions?
AI agents use external tools like data sets, APIs, and web searches. These tools help fill knowledge gaps and enable the agent to update its knowledge base and reason effectively.
How is autonomy balanced with human input in AI agents?
Agents act autonomously but rely on goals and rules set by humans. Developers, deployment teams, and users influence the agent’s behavior and access to tools.
What distinguishes agentic AI chatbots from non-agentic ones?
Agentic chatbots use tools, memory, and planning to perform subtasks and self-correct over time. Non-agentic chatbots lack these capabilities and operate only within fixed responses.