From Chatbots to Agents
A regular chatbot generates text responses. An AI Agent goes further — it can observe its environment, reason about what to do, take actions (call tools, write code, browse the web), and learn from the results. It's an LLM with hands and eyes.
Analogy: Think of an AI agent like a smart assistant with a toolbox. You say "book me a flight to Tokyo." The assistant thinks about what's needed (reasoning), checks flight APIs (tool use), compares options (observation), and books the best one (action). A chatbot would just tell you how to book a flight.
The Agent Loop
Every agent follows a perception-reasoning-action cycle:
Observe — Perceive environment, read inputs
Think — Reason about what to do next
Act — Execute an action or call a tool
Observe Result — See the outcome of the action
This loop repeats until the agent has accomplished the goal or decides it cannot proceed.
The ReAct Pattern
ReAct (Reasoning + Acting) is the dominant pattern for LLM agents. The model alternates between thinking (chain-of-thought reasoning) and acting (calling tools), using observations from tool results to inform the next step.
# Simplified ReAct agent loop
def react_agent(user_query: str, tools: dict, llm) -> str:
"""
ReAct loop: Thought → Action → Observation → Thought → ...
"""
messages = [{"role": "user", "content": user_query}]
max_steps = 10
for step in range(max_steps):
# LLM generates thought + action (or final answer)
response = llm.generate(messages, tools=tools)
if response.has_tool_call:
# Agent decided to use a tool
tool_name = response.tool_call.name
tool_args = response.tool_call.arguments
print(f" Step {step}: Calling {tool_name}({tool_args})")
# Execute the tool
result = tools[tool_name].execute(**tool_args)
# Feed result back as observation
messages.append({"role": "tool", "content": str(result)})
else:
# Agent generated final answer
return response.text
return "Agent exceeded max steps."
# Example tools an agent might use
tools = {
"search_web": SearchTool(),
"run_code": CodeExecutor(),
"read_file": FileReader(),
"write_file": FileWriter(),
}
Tool Use Flow
When an agent needs to take action in the world, it uses tools — functions it can call to interact with external systems:
User Request — "What's the weather in Tokyo?"
Agent Thinks — "I need to check a weather API"
Tool Call — get_weather(city="Tokyo")
Tool Response — {"temp": 22, "condition": "sunny"}
Agent Thinks — "Now I can answer the user"
Final Answer — "It's 22°C and sunny in Tokyo"
Agent vs Chatbot
Chatbot (LLM)
- Generates text responses only
- No access to external systems
- Single turn — no persistent state
- Knowledge limited to training data
- Cannot take real-world actions
AI Agent
- Reasons, plans, and takes actions
- Uses tools (APIs, code, files, web)
- Multi-step — maintains context across steps
- Can access live data via tools
- Executes real-world tasks autonomously
Real-World Agent Examples
- Claude Code: Reads files, writes code, runs tests, makes git commits
- Devin: Autonomous software engineer — plans, codes, debugs, deploys
- AutoGPT: General-purpose agent that breaks goals into sub-tasks
- Customer Support Agents: Look up orders, process refunds, escalate issues
- Research Agents: Search papers, summarize findings, generate reports
Agents are powerful but need guardrails. An agent with unrestricted tool access could make irreversible mistakes. Always implement human-in-the-loop confirmation for high-stakes actions (sending emails, making purchases, deleting data).
Key Takeaways
- An AI agent is an LLM that can observe, reason, and take actions — not just generate text
- The ReAct pattern alternates between reasoning (thoughts) and acting (tool calls)
- Tools give agents hands — search, code execution, file operations, API calls
- The agent loop repeats until the goal is achieved or max steps are reached
- Always add guardrails for high-stakes agent actions