From Chatbots to Controllable Agents: How LLMs Calling Tools Redefine AI Assistance

The distinction between AI chatbots and agents is becoming more critical as systems grow in capability. Many conversations treat these terms interchangeably, leading to confusion. However, it’s clear there’s not necessarily a clear distinction between them. The real takeaway is that the idea of an agent and a chatbot are orthogonal. A chatbot describes the interface, while an agent describes a functional capability. Understanding this separation helps in grasping the current state and future direction of AI applications.

The Interface: What Defines a Chatbot?

At its core, a chatbot is simply a conversational user interface. It’s how a user interacts with an underlying system through natural language. Think of ChatGPT – it’s an exemplar of a chatbot interface. You type in a question or a command, and it responds in text. This interface style is familiar, intuitive, and widely adopted, making AI accessible to a broad audience.

Many early AI applications, from customer service bots to virtual assistants, relied solely on this conversational format. Their primary function was to respond to user input within a defined set of rules or knowledge. The interaction was often turn-based, with the system waiting for user input before generating its next reply.

The interface aspect is important because it dictates how users perceive and interact with AI. A well-designed chatbot interface makes the AI feel more approachable and user-friendly, even if the underlying AI system is complex. Conversely, a clunky interface can hinder the adoption of even the most sophisticated AI.

Even with advanced LLMs, the fundamental interaction model of a chatbot remains. You input text, the system processes it, and it outputs text. This is why even a highly intelligent system like GPT-5 Thinking, which performs complex internal operations, is still experienced by the user as a chatbot. The conversational paradigm is deeply ingrained in how we interact with these AIs, often obscuring the advanced capabilities operating behind the scenes. This interface is excellent for accessibility and ease of use, allowing almost anyone to communicate with AI without needing specialized technical knowledge. However, it can also create a false impression of simplicity, leading users to underestimate the underlying complexity and functional differences between systems.

The Function: What Defines an Agent?

An agent, in this context, is an AI system that possesses the ability to call external tools in a loop to achieve a goal. This definition focuses purely on the functional capabilities of the AI, rather than its presentation. An agent doesn’t need a chat interface; it could operate completely in the background, interacting with other software or data sources.

The key components of an agent are:

• LLM (Large Language Model): This is the brain, responsible for understanding the goal, deciding which tools to use, and interpreting the tool outputs.
• Tools: These are external functions or APIs that the LLM can invoke. Examples include web search engines, code interpreters, databases, calendars, email clients, or even custom internal APIs.
• Loop: The LLM repeatedly calls tools, processes their outputs, and makes decisions until the specified goal is achieved. This iterative process allows for complex, multi-step tasks.
• Goal: The objective the agent is trying to accomplish. This could be as simple as finding a piece of information or as complex as writing a full blog post and publishing it.

Consider the example of an AI agent designed to plan a trip. Its goal is to create an itinerary. It might use a web search tool to find flight times, a calendar tool to check availability, a booking tool to reserve a hotel, and an email tool to send a confirmation. It would execute these steps in a loop, processing information from each tool until the itinerary is complete. This iterative, tool-calling capability is what fundamentally differentiates an agent from a mere chatbot.

Orthogonal Concepts: Unpacking the Relationship

The core insight here is that “chatbot” describes how you talk to an AI, while “agent” describes what the AI can do through action. These are two separate attributes. An AI can be an agent without being a chatbot (e.g., a background automation script running an LLM). Conversely, an AI can be a chatbot without being an agent (e.g., a simple Q&A system that only responds based on its internal knowledge and cannot invoke external tools).

The interesting area is where these two concepts intersect: an LLM that acts as an agent, calling tools in a loop, but presents this functionality through a conversational interface. GPT-5 Thinking is a prime example. While the interface is still a chatbot, it is simultaneously an agent because it thinks, executes searches, accesses memories, and runs code in a loop until it’s ready to respond. The chat interface is just the window into the agent’s operations.

This means that just because you are interacting with an AI via chat, it doesn’t mean it’s limited to being only a chatbot. It could be a sophisticated agent operating behind that conversational facade. This dual nature is often misunderstood, leading to a conflation of terms. The interface is merely the delivery mechanism for the agent’s actions and outputs. It’s like saying a car’s dashboard is the car itself; the dashboard is how you interact with the car’s underlying mechanical systems, but it’s not the engine, wheels, or transmission. Similarly, a chatbot interface provides controls and feedback for the agent’s functional core.

What Agency Does NOT Require

It’s important to clarify what the definition of an agent, in this context, does not include, to avoid common misconceptions:

• Autonomy: An agent does not need to be fully autonomous. It can be supervised, semi-autonomous, or even require constant human approval for each tool call. The ability to act in a loop, not the level of independence, makes it an agent. Many effective agents operate under significant human oversight, providing recommendations or drafting responses for human review. Their agency comes from their ability to orchestrate tools to reach a goal, not from their ability to act without permission.
• Memory: While memory (persistent context or a knowledge base) can significantly enhance an agent’s capabilities, it is not a fundamental requirement for agency itself. An LLM that calls tools in a loop to complete a task, even if it has no memory of past interactions, is still an agent. For example, a simple agent designed to find the current weather in a user-specified location would call a weather API, retrieve the data, and present it. This task doesn’t require remembering past conversations or long-term facts, yet it clearly involves tool-calling in a loop to achieve a goal.
• Reasoning: Even reasoning is not fundamental. If it’s an instruct model that doesn’t do any complex reasoning but immediately calls a tool based on a direct instruction, as long as it can do that in a loop to achieve a goal, then that’s still an agent. The level of sophistication in processing the goal or tool outputs can vary widely. An agent can operate on a very direct, literal interpretation of instructions, using tools sequentially without deep inferential steps. The key is the iterative application of tools towards an objective.

The simplicity of this definition is its strength. It focuses on the core functional behavior: an LLM iteratively making use of external functions. This broad definition allows for a wide range of AI agents, from simple assistants to highly complex systems. This minimalist definition helps to cut through the noise and focus on what truly matters for building capable AI systems. It simplifies the design process by emphasizing tool integration and iterative execution over more abstract concepts like consciousness or advanced intelligence, which are often mistakenly conflated with agency.

The Evolution Towards Proactive, Steerable AI

Fidji Simo’s discussion of a new paradigm of proactive, steerable AI highlights this shift. The movement is from reactive chatbots, where users have to explicitly prompt the AI for every step, to proactive agents that can anticipate needs, act autonomously (within defined limits), and make progress towards goals without constant hand-holding. These agents are also ‘steerable,’ meaning users can provide feedback and guidance to shape their behavior and outcomes. This aligns with the concepts for Model Routers for LLMs which help direct traffic for the models that they are serving.

This evolution is not about doing away with chatbots. Instead, it’s about embedding agentic capabilities within or behind conversational interfaces. The chatbot becomes the user’s friendly portal to a powerful, goal-oriented agent. For instance, a proactive AI assistant could learn your daily routine, anticipate your travel needs, and then, acting as an agent, book flights and accommodations using various tools, all while communicating its progress and seeking your approval via a chat interface.

The goal can be minimal. An agent could simply aim to provide a good response to a user. If, to achieve that ‘good response,’ the LLM needs to call internal or external tools (like a web search for current information or a calculator for a quick sum) and does so iteratively, then it’s functioning as an agent. The chat interface is merely the delivery mechanism for the agent’s output. The shift towards proactive AI means that agents are not just waiting for explicit commands but are initiating actions based on learned patterns, predictive analysis, and contextual understanding. This demands robust tool-calling capabilities and a clear definition of goals, even if those goals are simple and narrowly defined. The user’s role shifts from a direct commander to a supervisor and steersman, guiding the agent’s actions and providing feedback to refine its behavior over time.

Practical Implications for Development and Adoption

Understanding the difference between an agent and a chatbot has practical implications for anyone developing or deploying AI systems:

1. Focus on Functional Design: When building AI, prioritize designing for agency – the ability to call tools in a loop to achieve goals. This means thinking about available APIs, data sources, and how the LLM can orchestrate these to perform complex tasks. This architectural focus ensures that the AI system can actually perform actions in the real world, rather than just generating text.
2. Interface Flexibility: Don’t limit agent capabilities to a chat interface. While conversational UIs are powerful, agents can also operate through GUIs, voice interfaces, or entirely headless in automated workflows. The choice of interface should be driven by user experience and functional requirements, not by a misunderstanding of what an agent is.
3. Manage Expectations: Educate users on what an AI system can truly do. If it’s a simple chatbot, set expectations for its conversational abilities. If it’s an agent, highlight its capacity for goal achievement through tool use, irrespective of its level of autonomy or memory. Clear communication avoids disappointment and builds trust.
4. Modular Architecture: Encouraging a modular approach where specific ‘tools’ can be developed and plugged into the LLM enables greater flexibility and scalability for agent development. This allows for easier expansion of capabilities and integration with existing systems, fostering a more robust and adaptable AI ecosystem.

The Future is Agentic, With or Without a Chat Interface

The long-term trajectory of AI is towards more capable, goal-oriented systems. This means that the ‘agent’ paradigm – LLMs calling tools in a loop to achieve goals – will become increasingly dominant. Whether these agents are presented via a chat window, a GUI, or operate invisibly in the background, their underlying functional definition remains consistent.

The key for developers and businesses is to focus on building agentic capabilities, which directly translates to creating more powerful and useful AI solutions. The interface can always be adapted to suit the user’s needs, but the ability of the AI to perform complex, multi-step actions is what ultimately delivers value.

For example, my AI systems focus on leveraging these agentic principles to automate content creation. It’s not just about generating text (which a basic chatbot can do); it’s about the LLM interacting with various tools—like a web search for up-to-date information, a database for specific facts, or an API to format and publish—all in a loop to produce high-quality, valuable content. The interface I use to direct this might be conversational, but the power comes from the underlying agency.

Ultimately, separating these concepts helps in clear communication and better product development. We are moving beyond just talking to AI; we are moving towards AI that takes action, and that action happens regardless of the conversational wrapper. This distinction is not just academic; it has profound implications for how we design, implement, and interact with AI systems. By focusing on the agentic capabilities, we can build AIs that go beyond mere conversation to truly assist, automate, and innovate. The chat interface then becomes a powerful, accessible front-end to these deep functional capabilities, making advanced AI accessible to a wider audience without compromising on the underlying power. The potential for these agents to transform industries, from customer service to scientific research, is immense, provided we understand their core nature and design them effectively.

Agentic AI in Action: Real-World Scenarios

To further clarify the concept, let’s consider a few real-world scenarios where agentic AI is already making an impact, or where its potential is clear:

Content Creation Automation

My own systems for content automation exemplify the agent paradigm. It’s not enough for an LLM to just write text. For a blog post to be truly valuable, it needs to incorporate current data, adhere to SEO best practices, and be published effectively. An agentic system accomplishes this by:

• Web Search Tool: To gather up-to-date information, perform keyword research, and identify trending topics.
• Data Retrieval Tool: To access internal databases for specific facts or previous content.
• Writing/Editing Tool: The LLM itself generates and refines the text.
• SEO Tool: To check keyword density, readability, and suggest improvements.
• Publishing API: To directly post the content to a blog or social media platform.

The LLM orchestrates these tools in a loop, iteratively refining the content until it meets all specified criteria and is ready for publication. The interaction with me might be through a simple prompt, but the underlying process is a complex dance of tool calls.

Financial Assistant Agents

Imagine an AI agent designed to manage personal finances. Its goal might be to optimize savings or identify investment opportunities. This agent could:

• Banking API: To access transaction history and current balances.
• Market Data API: To fetch real-time stock prices or economic indicators.
• Calendar Tool: To track upcoming bill payments.
• Alerting Tool: To notify the user of unusual spending patterns or investment opportunities.

The agent continuously monitors financial data, analyzes trends, and uses these tools to execute strategies or provide timely advice. While a user might interact with it via a chat interface to ask about their balance, the core work of monitoring and analysis happens through tool calls.

Customer Support Agents

While many customer service chatbots are still rule-based, the next generation will be agentic. A sophisticated customer support agent could:

• CRM Tool: To access customer history and previous interactions.
• Knowledge Base Search: To find relevant articles or FAQs.
• Troubleshooting Tool: To guide the customer through diagnostic steps.
• Order Management System: To check order status or initiate returns.
• Human Handoff Tool: To seamlessly transfer complex issues to a human agent.

This agent doesn’t just respond; it acts. It can diagnose problems, process requests, and even initiate workflows, all by calling various internal and external systems in response to customer queries. The chat interface is just the front door to this powerful operational capacity. The ability to integrate with diverse enterprise systems is crucial here, and it highlights why a modular approach to tool development is so important. This also underscores the point that tools can be internal systems or external APIs, as long as the LLM can call them to achieve a goal.

The Broader Impact: From Reactive to Proactive AI

The distinction between chatbots and agents is fundamental to understanding the shift from reactive to proactive AI. A reactive system waits for a command and responds. A proactive system anticipates needs, initiates actions, and works towards a goal, often without explicit prompting at every step. This is where the true power of agentic AI lies, as highlighted by Fidji Simo’s vision of steerable AI.

Proactive agents, by their nature, require the ability to interact with the external world through tools. They need to fetch data, execute code, send messages, or manipulate systems to achieve their objectives. This is a significant leap beyond merely generating text or answering questions. It moves AI from being a conversational partner to an active participant in workflows and decision-making processes. The ability to ‘steer’ these agents means users maintain control, providing high-level directives and feedback rather than micro-managing every action. This balance of autonomy and oversight is crucial for successful AI deployment in real-world applications. The concept of Model Routers for LLMs becomes particularly relevant here, as it allows for directing agent traffic to the most appropriate model, balancing reliability and quality based on the specific task at hand.

The Role of Modularity and Open Standards

The development of agentic AI heavily relies on modularity. Tools must be easily integrated and swapped out. This necessitates open standards and well-documented APIs, allowing developers to extend agent capabilities without being locked into proprietary ecosystems. The more accessible and diverse the tool ecosystem, the more powerful and versatile agents can become. This is similar to how operating systems support various applications through standardized APIs; agents need a robust ‘tool-operating system’ to thrive. The growth of open-source models and tool frameworks will further accelerate this trend, giving developers greater flexibility and reducing barriers to entry for building sophisticated agents.

The Need for Clear Terminology

Finally, the conversation about chatbots and agents underscores the need for clear terminology in the rapidly evolving field of AI. Misunderstandings can lead to misaligned expectations, flawed system designs, and ultimately, failed projects. By clearly defining an agent as an LLM that calls tools in a loop to achieve a goal, we provide a precise, functional benchmark that is independent of interface, autonomy, memory, or reasoning capabilities. This clarity allows for more productive discussions, better research, and more effective development of AI systems that truly deliver value.

The ability to call tools in a loop is the defining characteristic that separates an agent from a simple chatbot. While a chatbot might be an agent’s interface, the agent itself is a functional entity capable of performing complex, multi-step actions in pursuit of a goal. This understanding is crucial for anyone involved in the design, development, or deployment of AI solutions today.