Building AI Agents

🤖 Agentic AI: Building Your Own AI Helper Team

The Big Picture: What if Your AI Could DO Things?

Imagine you have a super-smart friend who knows EVERYTHING but can only talk. They can tell you the weather will be sunny, but they can’t check the actual forecast. They can explain how to order pizza, but they can’t actually call the pizza place.

That’s regular AI.

Now imagine giving that friend a phone, a computer, and permission to DO things for you. They don’t just TELL you about the weather—they CHECK it and tell you to grab an umbrella!

That’s Agentic AI. 🎉

🛠️ Part 1: Agents and Tool Use

What’s an AI Agent?

Think of an AI Agent like a helpful robot assistant in a kitchen:

• Regular AI = A recipe book (gives information)
• AI Agent = A chef who reads recipes AND cooks the food!

An agent has THREE superpowers:

graph TD
    A["🧠 THINK"] --> B["🔧 ACT"]
    B --> C["👀 OBSERVE"]
    C --> A

1. THINK: Understand what you want
2. ACT: Use tools to get things done
3. OBSERVE: See what happened and decide what’s next

What Are Tools?

Tools are like giving your AI helper a toolbox! 🧰

Simple Example:

Real-Life Story:

You ask: “What’s the weather tomorrow in Paris?”

Without tools:

“I don’t have access to current weather data…”

With a weather tool:

“Tomorrow in Paris: 22°C and sunny! Perfect for a walk! ☀️”

The agent USED a tool to help you!

📞 Part 2: Function Calling

What’s Function Calling?

Function calling is HOW the AI uses its tools. It’s like teaching your robot friend to dial a phone number!

Everyday Example:

When you want pizza, you:

1. Know the pizza shop’s number
2. Know what to say (size, toppings)
3. Make the call

Function calling works the same way:

AI thinks: "I need weather info"
AI calls: get_weather(city="Paris", date="tomorrow")
Tool returns: {temp: 22, condition: "sunny"}
AI says: "It'll be 22°C and sunny tomorrow!"

How Does It Work?

graph TD
    A["You Ask a Question"] --> B["AI Understands Intent"]
    B --> C{Need a Tool?}
    C -->|Yes| D["AI Picks the Right Function"]
    D --> E["AI Fills In the Details"]
    E --> F["Function Runs!"]
    F --> G["AI Gets Results"]
    G --> H["AI Tells You the Answer"]
    C -->|No| H

The Key Parts:

• Function Name: WHAT tool to use (get_weather)
• Parameters: The details needed (city, date)
• Return Value: What comes back (the weather data)

🔄 Part 3: The ReAct Pattern

Reason + Act = ReAct!

ReAct is a special recipe that makes AI agents super smart. It stands for:

• Reason: Think about what to do
• Act: Do something

And it repeats! Think, do, think, do…

Story Time: The Lost Keys 🔑

Imagine you lost your keys. Here’s how you’d find them the ReAct way:

1. THOUGHT: “Where did I last have them?”
2. ACTION: Check my jacket pockets
3. OBSERVATION: Not there
4. THOUGHT: “Maybe I left them in the kitchen”
5. ACTION: Check the kitchen counter
6. OBSERVATION: Found them! 🎉

An AI agent does the EXACT same thing:

User: "Find me a good Italian restaurant nearby"

THOUGHT: I need to know the user's location first
ACTION: get_user_location()
OBSERVATION: User is in downtown Seattle

THOUGHT: Now I can search for Italian restaurants
ACTION: search_restaurants(cuisine="Italian",
                          location="Seattle")
OBSERVATION: Found 5 restaurants

THOUGHT: I should pick the best-rated one
ACTION: sort_by_rating(restaurants)
OBSERVATION: "Bella Luna" has 4.8 stars

ANSWER: "Bella Luna is an excellent Italian
         restaurant nearby with 4.8 stars!"

🗺️ Part 4: Planning with LLMs

Why Do Agents Need Plans?

Imagine building a LEGO castle without looking at the instructions. Chaos! 🏰

AI agents need plans too. For big tasks, they break things into smaller steps.

Simple Example:

Task: “Plan a birthday party for next Saturday”

Without planning:

“Um… buy cake? Invite people? I don’t know where to start!”

With planning:

graph TD
    A["🎂 Plan Birthday Party"] --> B["📋 Make Guest List"]
    A --> C["📍 Choose Location"]
    A --> D["🍰 Order Cake"]
    A --> E["📧 Send Invitations"]
    B --> E
    C --> E
    D --> F["🎉 Party Day!"]
    E --> F

Types of Planning

1. Sequential Planning (One step at a time)

Step 1: Check calendar for Saturday
Step 2: Make guest list
Step 3: Find venue
Step 4: Order cake
Step 5: Send invitations

2. Parallel Planning (Some things happen together)

At the same time:
- Order cake
- Book venue
- Buy decorations

Then:
- Send invitations with all details

3. Adaptive Planning (Change plans when needed)

Original: Book outdoor park
Problem: Weather forecast shows rain!
New plan: Book indoor venue instead

The best agents can adjust their plans when things change—just like you do!

🧠 Part 5: Memory in AI Systems

Why Do Agents Need Memory?

Imagine talking to a friend who forgets everything you said 5 seconds ago:

You: “My name is Alex” Friend: “Nice to meet you!” You: “What’s my name?” Friend: “I don’t know your name…” 😅

That’s AI without memory. Frustrating, right?

Three Types of Memory

graph TD
    A["🧠 AI Memory Types"] --> B["📝 Short-Term"]
    A --> C["📚 Long-Term"]
    A --> D["💼 Working Memory"]

1. Short-Term Memory (The Conversation) What you just talked about. Like remembering someone’s name during a chat.

Example:

You: "I love Italian food"
You: "What should I eat tonight?"
AI: "Since you love Italian, how about pasta?"

The AI remembered your preference from earlier!

2. Long-Term Memory (The History Book) Important things saved forever. Like remembering that your birthday is in June.

Example:

Last month: "My dog's name is Buddy"
Today: "How's Buddy doing?"
AI: "Is Buddy feeling better after his vet visit?"

3. Working Memory (The Notepad) Temporary notes while solving a problem.

Example:

Task: "Book a flight to Tokyo"

Working memory notes:
- User wants: Tokyo
- Dates: needs to ask
- Budget: needs to ask
- Found: 3 good options

How Memory Helps

🔁 Part 6: Agent Loops

What’s an Agent Loop?

An agent loop is the heartbeat of an AI agent. It’s the cycle that runs over and over:

PERCEIVE → THINK → ACT → REPEAT

Like breathing, but for robots! 🤖

graph TD
    A["👁️ PERCEIVE"] -->|See what's happening| B["🧠 THINK"]
    B -->|Decide what to do| C["✋ ACT"]
    C -->|Do something| D["📊 OBSERVE"]
    D -->|See results| A
    D -->|Task Done?| E["✅ FINISH"]

The Steps in Detail

1. PERCEIVE 👁️ “What’s the situation?”

• Read user’s request
• Check current state
• Notice any changes

2. THINK 🧠 “What should I do?”

• Consider options
• Pick the best action
• Plan next steps

3. ACT ✋ “Let me do it!”

• Call a function
• Use a tool
• Generate a response

4. OBSERVE 📊 “What happened?”

• Check the result
• Was it successful?
• Do I need to try again?

Real Example: Booking a Restaurant

🔁 LOOP 1:
PERCEIVE: User wants to book dinner for 4
THINK: I need to know what cuisine they prefer
ACT: Ask "What type of food would you like?"
OBSERVE: User says "Italian"

🔁 LOOP 2:
PERCEIVE: User wants Italian food for 4
THINK: I need to find Italian restaurants
ACT: search_restaurants("Italian", party_size=4)
OBSERVE: Found 3 restaurants with availability

🔁 LOOP 3:
PERCEIVE: Have 3 options, need user to choose
THINK: Show options and ask for preference
ACT: Display "Bella Luna, Pasta Palace, Roma Garden"
OBSERVE: User picks "Bella Luna"

🔁 LOOP 4:
PERCEIVE: User chose Bella Luna for 4 people
THINK: Make the reservation
ACT: book_restaurant("Bella Luna", guests=4)
OBSERVE: Booking confirmed! ✅

✅ DONE!

When Does the Loop Stop?

The agent loop stops when:

• ✅ Task is complete
• ❌ Max attempts reached
• ⚠️ Error that can’t be fixed
• 🛑 User cancels

🎯 Putting It All Together

Now you know ALL the pieces! Let’s see how they work together:

graph TD
    A["🎯 User Request"] --> B["🔁 Agent Loop Starts"]
    B --> C["🧠 ReAct: Reason"]
    C --> D["📋 Make a Plan"]
    D --> E["📞 Call Functions"]
    E --> F["🛠️ Use Tools"]
    F --> G["💾 Update Memory"]
    G --> H{Done?}
    H -->|No| C
    H -->|Yes| I["✅ Final Answer"]

The Complete AI Agent = Tools + Functions + ReAct + Planning + Memory + Loops

It’s like a superhero team where:

• Tools are the superpowers 🦸
• Functions are how powers are used 📞
• ReAct is the strategy 🧠
• Planning is the battle plan 🗺️
• Memory is experience & wisdom 📚
• Loops keep everything running 🔄

🌟 Key Takeaways

1. AI Agents DO things, not just talk about them
2. Tools give agents abilities (search, calculate, email)
3. Function Calling is how agents use tools
4. ReAct = Think, Act, Observe, Repeat
5. Planning breaks big tasks into manageable steps
6. Memory helps agents remember and personalize
7. Agent Loops are the engine that powers everything

🎉 You Did It!

You now understand how to build AI that can actually help in the real world! These agents are powering:

• Personal assistants that book your appointments
• Customer service that solves problems
• Research tools that find and summarize information
• And so much more!

The future is agentic. And now you know how it works! 🚀