Source: AIML.com Research
Introduction
Large language models can write emails, summarize documents, explain code, and answer questions. But answering a question is different from completing a task.
Suppose you ask an AI system to plan a weekend trip. A traditional chatbot might suggest destinations, hotels, and activities based on the information already available in the conversation. An AI agent can go further: it may search for current flights, compare hotel options, check your calendar, use external applications, observe the results, and decide what to do next.
This ability to move beyond a single prompt-and-response interaction is what makes AI agents an important step in the evolution of AI applications.
What Is an AI Agent?
An AI agent is an AI system that can reason about a goal, interact with tools or an environment, observe the results of its actions, and take further actions toward completing that goal.
The large language model often provides the reasoning capability, but the agent is more than the model itself. It operates as part of a larger system that gives the model access to information and actions outside its original training data.
For example, an agent might use web search to retrieve current information, query a database, execute Python code, read documents, or interact with an external application. Research such as Toolformer has demonstrated how language models can learn when to call external tools and incorporate their results into subsequent predictions. (Ref: Toolformer: Language Models Can Teach Themselves to Use Tools by Schick et al.)
The important idea is therefore not simply that an agent can “think.” It can connect reasoning with action.
Source: AIML.com Research
AI Agents vs. Traditional Chatbots
The easiest way to understand an AI agent is to compare how a chatbot and an agent might handle the same request.
Suppose you ask: “Find me a highly rated hotel near Times Square for under $200 per night this Friday.”
A traditional chatbot might respond with a list of hotels that are generally known to be affordable and well-rated. However, unless it has access to live data, it cannot verify whether those hotels actually have rooms available this Friday or whether their current prices are below $200. The response is useful advice, but the interaction essentially ends there.
An AI agent can treat the same request as a goal to accomplish rather than simply a question to answer. It might search a hotel-booking service for the specified date, examine the available options, filter out hotels above $200, compare their locations and ratings, and then present the best matches. If no suitable hotel is available, it can use that result to decide what to try next—for example, expanding the search area or asking whether you are willing to increase the budget.
The key difference is that the chatbot primarily generates a response, while the agent can gather information, take actions, observe the results, and continue working toward the user’s goal.
| Traditional chatbot | AI Agent |
|---|---|
| Primarily generates one-shot responses | Works toward goals |
| Usually reacts to individual prompts | Can perform multi-step tasks |
| Relies mainly on available context | Can retrieve new information |
| May describe what should be done | Can use tools to take actions |
| User drives each interaction | System can determine subsequent steps |
Source: AIML.com Research
This distinction is not absolute. Modern chat interfaces can themselves contain agentic capabilities. The important difference is the behavior of the system, not whether it looks like a chatbot.
Anthropic similarly distinguishes between systems following predefined workflows and agents in which an LLM dynamically directs its own process and tool usage.(Ref: Building Effective AI Agents by Anthropic)
Reasoning and Acting Together
A key idea behind modern agents is that reasoning and action can be interleaved.
An agent may determine that it needs more information, perform an action to obtain that information, examine the result, and then decide what should happen next. This makes the system responsive to information that was not available when the task began.
The ReAct research framework demonstrated this idea by combining language-model reasoning with actions that interact with external environments. Rather than reasoning entirely from internal knowledge, the model could gather information and use new observations to guide subsequent reasoning.(Ref: ReAct: Synergizing Reasoning and Acting in Language Models by Yao et al.)
This does not mean every agent uses ReAct specifically. Instead, it illustrates the broader principle that connects many agentic systems: reasoning can influence actions, while the results of actions can influence future reasoning.
Source: Yao et al., “ReAct: Synergizing Reasoning and Acting in Language Models.” 2023.
When Do You Need An AI Agent?
Agents are useful when a task requires interaction rather than just text generation.
A research agent, for example, may search multiple sources before synthesizing an answer. A coding agent may inspect a repository, edit files, run tests, observe failures, and revise its solution. A data-analysis agent may write and execute Python code before interpreting the output. Other agents can interact with calendars, email systems, databases, or business applications.
The common pattern is that the system is given an objective and has some ability to determine which actions are useful for reaching it.
However, not every AI task needs an agent. If a user simply wants a paragraph summarized or an idea explained, a direct LLM call may be faster, cheaper, and easier to control. Agents become particularly useful when successful completion depends on multiple steps, external information, actions, or changing conditions.
Key Takeaways
An AI agent is more than an LLM that produces sophisticated text. It combines language-model intelligence with the ability to interact with systems outside the model itself.
The defining shift is from generating an answer toward pursuing a goal. Agents can reason about what to do, use tools, observe what happened, and continue working based on new information.
The exact architecture behind this behavior can vary considerably. Refer to the “Core Components of an Agentic System” article to learn more about what goes into an AI agent. For more on the agentic loop and function calling, refer to “What Is the Agentic Loop?” and “What Is Function Calling in LLM Applications?”
Videos
- This video “AI Agents, Clearly Explained” by Jeff Su provides a beginner-friendly explanation of how AI systems progress from standalone LLMs to predefined AI workflows and, finally, AI agents. It uses practical examples to clarify what gives an agent greater autonomy than a traditional chatbot, making it a useful visual introduction to the concepts covered in this article. (Runtime: ~10 mins)
- This video “What are AI Agents?” from IBM Technology provides a concise introduction to AI agents and explains the shift from standalone language models toward systems that can interact with databases and external tools. It is a useful visual companion to this article for understanding why tool access and interaction with the environment make agents more capable than traditional LLM applications. (Runtime: ~11 mins)
References
- Yao, S., Zhao, J., Yu, D., et al. “ReAct: Synergizing Reasoning and Acting in Language Models.” ICLR, 2023.
- Schick, T., Dwivedi-Yu, J., Dessì, R., et al. “Toolformer: Language Models Can Teach Themselves to Use Tools.” NeurIPS, 2023.
- Anthropic. “Building Effective Agents.” Anthropic Engineering, 2024.