When LLMs can DO things — not just talk about them.
I KNOW how
Knowledge exists
"To book a flight, search airline websites..."
"Use a weather API to get forecasts..."
"Run SELECT * FROM sales..."
But CAN'T do it
No ability to act
The Gap: Knowledge without Action
The LLM THINKS, your code ACTS.
LLM outputs:
Your Code
→Result:
Agent
Loop
Step-by-step walkthrough
"What's the weather in Paris?"
"I need the weather tool"
get_weather("Paris")
{ "temp": "22°C", "conditions": "Sunny" }
"I have the data, time to respond"
"It's 22°C and sunny in Paris!"
Final Response
"I found a $340 round-trip on Air France, Hotel Le Marais for $120/night. March weather averages 15°C with light rain -- pack a jacket!"
3 loop iterations, each adding to the response
Real Tool Examples
Command:
Googled "flights to Paris March"
Output:
3 results: Air France $340, Delta $420...
Command:
Ran python analyze.py
Output:
Analysis complete: 847 rows processed
Command:
SELECT revenue FROM sales WHERE quarter='Q3'
Output:
Result: $4.2M (+18% YoY)
name:
get_weather
description:
"Get current weather for a city name. Returns temperature in Celsius and conditions."
name:
do_stuff
description:
"Does things"
The LLM reads your descriptions to decide WHEN to use each tool.
Bad descriptions = wrong tool choices = broken agents
Retry
Try the same tool again
Fallback
Use a different tool
Tell User
"Sorry, weather service is down"
Best choice here: Retry. Timeouts are often temporary, so trying again usually works.
What do you need?
Just a summary or answer
Simple API Call
Cheaper & faster
Real-time data or actions
Use an Agent
Tools + reasoning
Multiple steps with decisions
Definitely Agent
Loop + multi-tool
Don't over-engineer: pick the simplest approach that works
RAG Becomes a Tool in the Agent Loop
RAG is just another tool
The agent decides when to search documents, just like it decides when to call any other tool.
Simple Calculator Agent
Your first agent with add, multiply tools
Math Tutor with 4 Tools
Calculator + explainer + hint generator + quiz
Study Buddy + Knowledge
Agent with RAG-powered document lookup
Terminal Assistant
Real file operations, system commands
$ List all Python files in /src
Calling list_files(path="/src", pattern="*.py")...
Found 12 files: main.py, utils.py, api.py...
$ Show me the largest one
Calling file_info(path="/src/api.py")...
api.py (2.4KB, 89 lines) - REST API endpoints