AI Agent VS Chatbot

What is a Chatbot?

Long before Large Language Model (LLMs) became mainstream, chatbots were already widely used in customer support, websites, and internal tools.

A traditional chatbot was essentially a structured decision tree, Set of predefined rules, flows and conditions if/else logic or state machines. Based on the user's response, the system would move to the next predefined step in the conversation.

While it worked well for predictable use cases, it had major limitations:

• It could only handle scenarios that were explicitly programmed.
• Any unexpected input would break the flow.
• The chatbot can not reason, adapt, or decide new action dynamically.

At best, a chatbot could collect information and maybe trigger a fixed action (like creating a support ticket), but it could not actively try to solve the problem or adjust its strategy.

In short:
Chatbots were reactive, not intelligent.

Introduction of LLMs

Large Language Models (LLMs) introduced a fundamental shift.

Instead of treating every message as just another condition in a decision tree, LLMs interpret user input in a much deeper way:

• understanding intent
• extracting meaning
• handling ambiguity
• deciding whether to act or ask clarifying questions

This made conversations feel natural and human-like, rather than mechanical.

For the first time, a system could:

• understand what the user really wants
• reason about multiple possible responses
• generate new solutions instead of following fixed scripts
• LLMs provided the cognitive layer that chatbots were missing.

Introducing AI Agent

The rise of LLMs enabled something more powerful than chatbots: AI Agents.

An AI agent is not just a system that talks, it is a system that can:

• observe the environment
• reason about goals
• plan steps
• use tools
• execute actions
• evaluate results
• and adapt based on feedback

This bridges the gap between:

"I can talk to you"
and
"I can actually help you achieve something."

Here's how an agent work better than a chatbot

In a chatbot:

• The system mainly responds.
• It cannot perform complex multi-step actions.
• It depends heavily on predefined flows.

In an AI agent:

• The system can decide what to do next.
• It can call APIs and external tools.
• It can retry, replan, and self-correct.
• It can operate autonomously toward a goal.

Agent Lifecycle

A typical AI agent follows a loop like this:

1. Observe
   • Receive user input or system state.
2. Think
   • Interpret the situation using reasoning.
3. Plan
   • Decide which actions or tools are needed.
4. Act
   • Execute actions using APIs or services.
5. Result → Observe again
   • Use the outcome as new input and repeat.

This feedback loop is what makes agents adaptive and intelligent, rather than static systems.

Tools at agent's disposal

Looking at the Agent Lifecycle, you can imagine that it needs more than just state machine and codes, it needs the capabilities to make smart decisions and then act upon them.

At high level, an agent usually consists of three core layers:

1. Reasoning Engine (LLM) The LLM acts as the cognitive core of the agent. It handles:

• interpreting user queries
• reasoning about context and intent. (i.e. understanding that user means Python programming language vs. reptile).
• evaluate results from tools.
• decide next steps.

1. Action (API/Tools) Tools represent everything an agent can do in real world:
   • search engine
   • databases
   • payment services
   • scheduling systems
   • internal microservices

Tools perform action, they don't think but execute.

The agent can't perform any action on its own it needs access to external tools and APIs to perform action.

1. Control (Orchestrator) The orchestrator system is responsible for:
   • Managing the agent loop.
   • Track state and Memory.
   • Route Decision to tools.
   • Handle retries and failures.

An Orchestrator is implemented using:

• workflow engines
• task queues
• state machines
• agent workflows such LangGraph, CrewAI, etc.

Final Thoughts

Instead of writing rigid workflows and hardcoded logic, we are moving towards systems that can:

• reason about goals.
• adapt to changing environments.
• orchestrate tools dynamically.
• and improve through feedback loops.