What Are AI Agents?#
Simple Definition#
An AI Agent is a smart program that can:
Observe
Think
Act
Think of It Like This#
A calculator is NOT an agent
A smart home assistant IS an agent
WHY!
AI Tool vs AI Agent#
Traditional AI Tool#
You ask → AI answers → Done
Example: You ask ChatGPT a question, it answers, conversation ends.
AI Agent#
Agent watches → Thinks → Acts(repeat)
Example: A calendar assistant.
Key Difference#
Types of AI Agents#
Level 1: Simple Agents#
Thermostat:
Sense: Is it too cold?
Think: Temperature below setting
Act: Turn on heat
Level 2: Smarter Agents#
Netflix Recommendations:
Tracks what you watch
Learns your preferences
Suggests new shows
Adapts over time
Level 3: Advanced Agents#
Self-Driving Cars:
Constantly sensing environment
Planning routes in real-time
Safe navigation
Adapting to traffic
How Do Agents Work?#
The Basic Cycle#
1. OBSERVE (What's happening?)
↓
2. THINK (What should I do?)
↓
3. ACT (Do something!)
↓
4. OBSERVE RESULTS (What happened?)
↓
(Repeat continuously)
Real Example: Weather Alert Agent#
OBSERVE: Checks weather forecast every hour
THINK: “Will it rain tomorrow?”
ACT: Sends you a notification
OBSERVE: Continues monitoring
Key Components of Agents#
Architecture Overview#
┌─────────────────────────────────────┐
│ ENVIRONMENT │
└──────────┬──────────────▲───────────┘
│ │
┌──────▼────┐ ┌────┴──────┐
│ SENSORS │ │ ACTUATORS │
│ │ │ │
│ │ │ │
└──────┬────┘ └────▲──────┘
│ │
┌──────▼──────────────┴──────┐
│ BRAIN │
│ ┌─────────────────────┐ │
│ │ MEMORY │ │
│ └─────────────────────┘ │
│ ┌─────────────────────┐ │
│ │ REASONING │ │
│ └─────────────────────┘ │
└────────────────────────────┘
The Three Parts#
1. Sensors (How it gathers information)
Read emails
Check databases
Monitor weather
Listen to voice
2. Brain (How it thinks and remembers)
Processes information
Makes decisions
Remembers past events
3. Actuators (How it takes action)
Send messages
Update databases
Control devices
Display information
ReAct: How Modern Agents Think#
ReAct = Reasoning + Acting
Modern AI agents combine thinking (reasoning) and doing (acting) in a loop — just like humans solving problems.
The ReAct Pattern#
Thought → “I need to find out the weather.”
Action → Search for weather in New York.
Observation → “It’s 72°F and sunny.”
Thought → “Now I can answer the user.”
Action → Reply: “It’s 72°F and sunny in New York!”
Building Blocks: LLMs and Tools#
What’s an LLM?#
Large Language Model = The “brain” of modern AI agents
Think of it as:
A really smart text processor
Can understand and generate language
Examples: ChatGPT, Claude, Gemini
What it does well:
Understanding questions
Planning steps
Explaining things
What it CAN’T do alone:
Search the internet
Send emails
Access databases
What Are Tools?#
Tools give agents capabilities they don’t have naturally.
Examples of tools:
- Calculator (for math)
- Web search (for finding information)
- Database query (for getting data)
- Email sender (for communication)
- File reader (for documents)
- Weather API (for forecasts)
The magic: Agent decides WHICH tool to use WHEN
Putting It Together#
User Question
↓
LLM (Brain) reads question
↓
LLM decides
↓
Tool runs and gives result
↓
LLM reads result
↓
LLM generates final answer
Connecting Agents to the World: MCP#
What is MCP?#
Model Context Protocol = A standardized way for AI agents to access tools and data
Think of it as:
Universal adapters for AI agents
Like USB ports for your computer - one standard way to connect many things
The Problem MCP Solves#
Before MCP: Each tool needs custom code → Hard to reuse or share
With MCP: Standardized connections → Plug-and-play tools
MCP Servers Are Bridges#
MCP servers give LLMs access to:
Real-time data
Actions in external systems
How It Works#
User Question
↓
LLM Agent (decides what it needs)
↓
MCP Protocol (standard language)
↓
MCP Server (specialized connector)
↓
External System (gets/sends data)
↓
LLM Agent (uses data to respond)
Expandable Toolkit#
Key Benefits:
Secure: Standardized authentication and permissions
Flexible: Add new tools without rewriting your agent
Community-driven: Open-source servers you can use and contribute to
Future-proof: As new systems emerge, new MCP servers connect them
Repository:
https://github.com/modelcontextprotocol/servers
The Role of Memory#
Why Memory Matters#
Without memory:
User: "My name is Alex"
Agent: "Nice to meet you!"
User: "What's my name?"
Agent: "I don't know" ❌
With memory:
User: "My name is Alex"
Agent: "Nice to meet you!" [stores: name=Alex]
User: "What's my name?"
Agent: "Your name is Alex!" ✅
Types of Memory#
Short-term (Working) Memory:
Remembers current conversation
Like your brain during a single conversation
Lost when conversation ends
Long-term (Persistent) Memory:
Remembers across conversations
Like your brain remembering a friend’s birthday
Stored permanently (until deleted)
Semantic Memory:
General knowledge
Facts about the world
Built into the model
Memory Makes Agents Better#
Example: Shopping Assistant
Without memory:
“Show me shoes” every time
With memory:
Remembers you wear size 42
Knows you prefer Nike
Recalls you’re looking for running shoes
Shows relevant options immediately
Real-World Examples#
1. Customer Service Agent#
What it does:
Reads customer message
Understands the question
Checks order database
Finds order status
Responds with helpful information
Creates support ticket if needed
Tools it uses:
Order tracking system
Customer database
Email sender
Ticket creation system
2. Research Assistant#
What it does:
Reads your question
Searches multiple websites
Summarizes information
Gives you an answer with sources
Tools it uses:
Web search engine
Document reader
Summarization tool
Citation formatter
3. Personal Scheduler#
What it does:
Monitors your calendar
Suggests meeting times
Sends reminders
Reschedules when conflicts arise
Tools it uses:
Calendar API
Email client
Reminder system
Time zone converter
Common Challenges#
What Can Go Wrong?#
Problem 1: Agents Get Confused
Unclear tool descriptions
Too many options
Solution: Give clear tool descriptions, be specific
What Can Go Wrong?#
Problem 2: Too Many Steps
Complex tasks take too long
Agent loses track
Solution: Keep tasks simple, break into smaller ones
What Can Go Wrong?#
Problem 3: Agents Make Mistakes
Wrong tool selection
Incorrect reasoning
Solution: Always test thoroughly, have human review
Key Takeaways#
Remember These 5 Things#
Agents are autonomous - they can work without constant instructions
Agents use tools - like calculators, search engines, databases
Agents think in steps - observe, think, act, repeat
Memory makes agents better - helps them understand context
Agents are still learning - they’re not perfect and need supervision
The Big Picture#
We’re just starting to explore what AI agents can do.
As they get better, they’ll help us with more complex tasks.
The key is to build them responsibly and understand both what they can and cannot do.
Quick Reference#
Agent vs Traditional AI#
Traditional AI |
Agent |
|---|---|
Input → Output (done) |
Continuous cycle of sensing and acting |
Waits for commands |
Proactive |
No memory |
Has memory |
Single-purpose |
Multi-step tasks |
Main Components#
Sensors: Gather information
Brain: Think and remember
Actuators: Take actions
When to Use Agents#
✅ Good use cases:
Tasks that need multiple steps
When you need memory/context
Automated workflows
Continuous monitoring
❌ Not ideal for:
Simple one-time questions
When you need guaranteed accuracy
Real-time critical decisions (without human oversight)
Tasks with unclear goals
Popular Frameworks#
LangChain
AutoGPT
OpenAI Assistants API
Final Thoughts#
AI agents represent a shift from tools that wait for commands to systems that can work alongside us.