Agentic AI Course | Lecture 7 Part 1 – AI Agents Explained (LLM, Memory, Tools & RAG)

Описание: Lecture 7 – AI Agents Deep Dive (Part 1)
1. What is an AI Agent?

An AI Agent is not just a chatbot or a single AI call.
It is a decision-making system that can:

Understand inputs

Reason over information

Use tools

Store and recall memory

Take actions

Produce structured outputs

In simple terms:

An AI agent is an intelligent system that can think, decide, remember, and act to achieve a goal.

2. Core Components of an AI Agent

Every production-grade AI agent is built from multiple components. Removing any one of them limits the agent’s capability.

3. LLM (Large Language Model)
Role of the LLM

The LLM is the brain of the AI agent.

It is responsible for:

Understanding natural language

Reasoning and decision-making

Generating responses

Interpreting context

Examples:

OpenAI models

Gemini models

Important concept:
The LLM does not know your business by default.
It only knows general knowledge unless connected to memory or tools.

4. Tools
What are Tools?

Tools allow the AI agent to interact with the outside world.

Examples of tools:

APIs

Databases

Web scrapers

Search engines

Internal workflows (n8n nodes)

Without tools:

AI can only talk
With tools:

AI can act

Examples:

Fetch customer data

Scrape a website

Send messages

Store information

Trigger workflows

5. Memory in AI Agents
Why Memory is Needed

Without memory:

The agent forgets everything after each interaction

No personalization

No learning from past conversations

Memory allows agents to:

Remember users

Track conversations

Maintain long-term context

Improve decisions over time

6. Types of Memory
1. Short-Term Memory

Context of the current conversation

Stored temporarily

Lost after the session ends

2. Long-Term Memory

Stored permanently

Used across multiple sessions

Enables personalization and continuity

7. Supabase as Agent Memory
Why Supabase?

Supabase provides:

PostgreSQL database

Authentication

Storage

Real-time updates

It is commonly used as long-term memory for AI agents.

Use Cases:

Store user profiles

Save conversation history

Track agent decisions

Persist structured data

Supabase enables agents to:

Recall previous interactions

Maintain state

Act intelligently over time

8. PostgreSQL (Postgres) in Agentic AI
Role of PostgreSQL

PostgreSQL is a relational database used to store:

Structured memory

Logs

User data

Agent states

Why Postgres is important:

Reliable

Scalable

Widely supported

Works perfectly with Supabase

In Agentic AI:
Postgres acts as the backbone for persistent memory.

9. RAG-Based Agents
What is a RAG Agent?

A RAG (Retrieval-Augmented Generation) Agent combines:

Memory

External knowledge

AI reasoning

Instead of guessing answers, the agent:

Retrieves relevant data

Passes it to the LLM

Generates accurate responses

Why RAG is Critical

Prevents hallucinations

Keeps responses factual

Separates knowledge from the model

Allows easy updates

RAG agents are commonly used for:

Customer support

Knowledge bases

Internal company assistants

10. Output Parser
What is an Output Parser?

An output parser ensures that AI responses are:

Structured

Predictable

Machine-readable

Instead of free text, outputs can be:

JSON

Key-value pairs

Clean formatted data

Why Output Parsers Matter

Prevents messy outputs

Enables automation

Makes AI responses usable in workflows

Example:
Instead of:
“Sure, here is the answer…”

The agent returns:

{
"intent": "order_status",
"confidence": 0.92,
"action": "fetch_order"
}

11. Full AI Agent Architecture (Conceptual Flow)

User Input

LLM interprets request

Agent checks memory (Supabase/Postgres)

Agent retrieves knowledge (RAG if needed)

Agent calls tools

Output parser structures response

Final action or response is produced

This architecture allows agents to behave like real digital workers, not chatbots.

12. Key Takeaways from Lecture 7 – Part 1

AI agents are multi-component systems

LLMs provide reasoning, not business logic

Tools enable real-world actions

Memory makes agents intelligent over time

Supabase + PostgreSQL provide long-term memory

RAG improves accuracy and reliability

Output parsers make AI usable in automation