Agentic AI Workflow Tutorial for Beginners: Building a Smart Customer Service Assistant

Welcome to the exciting world of Agentic AI! This expanded tutorial will delve deeper into the core concepts and provide more detailed explanations for each component, including illustrative (but not executable) code snippets and conceptual datasets. We’ll continue with our goal of building a basic Smart Customer Service Assistant for a fictional online electronics store.

What is Agentic AI? Revisited

An Agentic AI system is characterized by its ability to take a high-level goal and autonomously figure out the steps to achieve it. It’s not just about responding; it’s about acting. Key elements include:

• Perception: Understanding the user’s input and the environment.
• Planning: Breaking down complex tasks into sub-tasks and determining the sequence of actions.
• Reasoning: Making decisions based on information, constraints, and current state.
• Action/Execution: Utilizing tools to interact with external systems or retrieve data.
• Memory: Retaining context, past interactions, and knowledge gained (often using Vector DBs and Graph DBs).
• Learning: Improving over time through experience and feedback (though this tutorial focuses on a static setup).

Core Components of Our Agentic Workflow (Deeper Dive)

1. Large Language Model (LLM) – The “Brain” for Reasoning and Planning

The LLM is the central intelligence unit. It interprets natural language, decides which tools to use, constructs queries for those tools, and synthesizes information into coherent responses. Using a custom LLM from Hugging Face means we’re leveraging pre-trained models that can be fine-tuned or directly used for specific domain knowledge, offering more control and privacy than general-purpose API-based LLMs.

• Role in our Agent:
  • User Intent Understanding: Parsing conversational input to discern the user’s underlying need (e.g., “order status,” “product details,” “technical support”).
  • Tool Selection & Argument Generation: Given a list of available tools and their descriptions, the LLM decides which tool is most appropriate and extracts the necessary parameters from the user’s query.
  • Intermediate Reasoning: If a task requires multiple steps, the LLM plans the sequence of tool calls and adapts based on tool outputs.
  • Response Generation: Synthesizing the gathered information into a natural, user-friendly answer.

Conceptual Python Setup for a Hugging Face LLM (using transformers):

from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
import torch

# 1. Define the model name. This would typically be a specific model
#    from the Hugging Face Hub, potentially one you've fine-tuned.
#    Example: "mistralai/Mistral-7B-Instruct-v0.2" or a custom one.
model_name = "your_org/your_fine_tuned_electronics_model" # Placeholder for a fine-tuned model
# For beginners, you might start with a smaller, readily available model like:
# model_name = "distilbert/distilgpt2" # A much smaller model for quick testing (less capable)

# 2. Load the tokenizer associated with the chosen model.
#    The tokenizer converts text into numerical IDs (tokens) that the model understands.
tokenizer = AutoTokenizer.from_pretrained(model_name)

# 3. Load the pre-trained causal language model.
#    'AutoModelForCausalLM' is for models that predict the next token in a sequence.
#    'torch_dtype=torch.float16' uses half-precision floating points, saving memory.
#    'device_map="auto"' intelligently distributes the model across available GPUs.
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto" # Automatically put model on GPU if available, else CPU
)

# 4. Create a Hugging Face pipeline for text generation.
#    This simplifies the process of tokenizing input, running inference, and decoding output.
llm_pipeline = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.float16,
    device=model.device # Use the device the model was loaded onto
)

def query_llm(prompt: str, max_tokens: int = 200, stop_sequences: list = None) -> str:
    """
    Simulates querying our custom Hugging Face LLM.
    'stop_sequences' are crucial for agentic LLMs to signal when they've finished
    generating a tool call or a final response.
    """
    if stop_sequences is None:
        stop_sequences = []

    # In a real agent, the prompt would be carefully crafted (e.g., using a prompt template)
    # to instruct the LLM on tool usage and response format.
    
    # We might add a custom stopping criterion if the pipeline doesn't support it directly.
    # For simplicity here, we assume it generates a full response or tool call.
    response = llm_pipeline(
        prompt,
        max_new_tokens=max_tokens,
        num_return_sequences=1,
        # Common parameters for controlling generation:
        do_sample=True,      # Enable sampling (less deterministic but more creative)
        temperature=0.7,     # Controls randomness (lower = more deterministic)
        top_k=50,            # Limits sampling to top-k tokens
        top_p=0.95,          # Limits sampling to tokens comprising top-p probability mass
        # You'd implement stop sequence logic manually if pipeline doesn't handle it
        # by checking generated text for stop_sequences and truncating.
    )
    generated_text = response[0]['generated_text']

    # Remove the initial prompt if it's echoed in the response
    if generated_text.startswith(prompt):
        generated_text = generated_text[len(prompt):].strip()

    # Apply stop sequences manually if the pipeline doesn't handle them
    for seq in stop_sequences:
        if seq in generated_text:
            generated_text = generated_text.split(seq)[0].strip()
            break
            
    return generated_text

# Example usage within an agent's reasoning loop:
# llm_output = query_llm(
#     "User query: 'What is the price of the new XYZ smartphone?' \nAvailable tools: [get_product_info, get_order_status]",
#     stop_sequences=["\n"] # Stop after first line for tool calls
# )
# This output would be parsed by the agent to decide which tool to use.

2. Vector Database (Vector DB) – Semantic Search Memory

Vector databases are specialized databases for storing and querying high-dimensional vectors (embeddings). These embeddings capture the semantic meaning of text, images, or other data, enabling you to find data that is *similar in meaning* rather than just keyword matches. This is fundamental for Retrieval Augmented Generation (RAG).

• Role in our Agent:
  • Product Descriptions: When a user asks about a product, the agent embeds the query and searches the Vector DB for product descriptions or FAQs that are semantically similar.
  • Knowledge Base Articles: For general questions, retrieve relevant articles or troubleshooting guides.
  • Contextual Information: Provide the LLM with relevant, factual information from your data sources to ground its responses and prevent “hallucinations.”
• Conceptual Sample Dataset (for Vector DB):

  Imagine a collection of product descriptions. Each description would be converted into a vector (embedding) and stored with its original text.

  [
      {
          "id": "prod_001",
          "text": "XYZ Smartphone: Featuring a stunning 6.7-inch OLED display, powered by the A17 Bionic chip for unparalleled performance. Capture breathtaking photos with its triple 48MP camera system. Comes with 256GB storage. Available in sleek Black and elegant Silver. Price: $999.",
          "metadata": {"type": "smartphone", "brand": "XYZTech", "model": "XYZ Smartphone"}
      },
      {
          "id": "prod_002",
          "text": "ABC Laptop: A powerhouse for professionals, boasting a vivid 14-inch retina display, 32GB of RAM, and a lightning-fast 1TB SSD. Equipped with an Intel i9 processor. Perfect for video editing and demanding software. Price: $1899.",
          "metadata": {"type": "laptop", "brand": "ABCTech", "model": "ABC Laptop"}
      },
      {
          "id": "prod_003",
          "text": "DEF Headphones: Immerse yourself in pure sound with advanced noise-cancelling technology. Enjoy up to 20 hours of battery life on a single charge. Features Bluetooth 5.2 for seamless connectivity. Lightweight and comfortable, ideal for travel and daily commute. Price: $299.",
          "metadata": {"type": "audio", "brand": "DEF Audio", "model": "DEF Headphones"}
      },
      {
          "id": "faq_001",
          "text": "How do I initiate a return? All returns must be initiated within 30 days of purchase with the original receipt. Products must be in their original packaging.",
          "metadata": {"type": "faq", "topic": "returns"}
      }
  ]
  

  Each “text” field would be converted into an embedding using an embedding model.

Conceptual Python Setup for a Vector DB (using a simplified client and a SentenceTransformer for embeddings):

# For a real project, you'd install: pip install sentence-transformers
from sentence_transformers import SentenceTransformer
import numpy as np # For numerical operations

# Initialize an embedding model. This converts text into numerical vectors.
# 'all-MiniLM-L6-v2' is a good balance of size and performance for many tasks.
embedding_model = SentenceTransformer('all-MiniLM-L6-v2')

class MockVectorDB:
    def __init__(self):
        self.vectors = {} # Stores {id: {"text": "...", "vector": [...], "metadata": {...}}}
        self.next_id = 0

    def add_document(self, text: str, metadata: dict = None):
        """
        Adds a document to the mock Vector DB.
        The text is converted into an embedding.
        """
        embedding = embedding_model.encode(text).tolist() # Convert numpy array to list for storage
        doc_id = f"doc_{self.next_id}"
        self.vectors[doc_id] = {"text": text, "vector": embedding, "metadata": metadata or {}}
        self.next_id += 1
        return doc_id

    def query_similar(self, query_text: str, top_k: int = 3) -> list:
        """
        Queries the Vector DB for documents semantically similar to the query.
        Uses cosine similarity for ranking.
        """
        query_vec = embedding_model.encode(query_text) # Returns numpy array
        similarities = []

        if not self.vectors:
            return [] # No documents to query

        for doc_id, doc_data in self.vectors.items():
            doc_vec = np.array(doc_data["vector"]) # Convert stored list back to numpy array
            
            # Calculate Cosine Similarity: dot(A, B) / (norm(A) * norm(B))
            # Ensures similarity is between -1 and 1, regardless of vector magnitude.
            similarity = np.dot(query_vec, doc_vec) / (np.linalg.norm(query_vec) * np.linalg.norm(doc_vec))
            similarities.append((similarity, doc_data["text"], doc_data["metadata"]))

        similarities.sort(key=lambda x: x[0], reverse=True) # Sort by similarity, highest first
        
        # Return only the text and metadata of the top_k results
        return [(text, metadata) for sim, text, metadata in similarities[:top_k]]

# Populate our mock Vector DB with product info
vector_db = MockVectorDB()
vector_db.add_document("XYZ Smartphone: Featuring a stunning 6.7-inch OLED display, powered by the A17 Bionic chip for unparalleled performance. Capture breathtaking photos with its triple 48MP camera system. Comes with 256GB storage. Available in sleek Black and elegant Silver. Price: $999.", metadata={"type": "smartphone", "brand": "XYZTech"})
vector_db.add_document("ABC Laptop: A powerhouse for professionals, boasting a vivid 14-inch retina display, 32GB of RAM, and a lightning-fast 1TB SSD. Equipped with an Intel i9 processor. Perfect for video editing and demanding software. Price: $1899.", metadata={"type": "laptop", "brand": "ABCTech"})
vector_db.add_document("DEF Headphones: Immerse yourself in pure sound with advanced noise-cancelling technology. Enjoy up to 20 hours of battery life on a single charge. Features Bluetooth 5.2 for seamless connectivity. Lightweight and comfortable, ideal for travel and daily commute. Price: $299.", metadata={"type": "audio", "brand": "DEF Audio"})
vector_db.add_document("How do I initiate a return? All returns must be initiated within 30 days of purchase with the original receipt. Products must be in their original packaging.", metadata={"type": "faq", "topic": "returns"})


def retrieve_info_from_vector_db(query: str, type_filter: str = None) -> str:
    """Tool to retrieve information from Vector DB, potentially filtered by type."""
    results = vector_db.query_similar(query, top_k=3) # Get top 3
    
    filtered_results = []
    for text, metadata in results:
        if type_filter and metadata.get("type") != type_filter:
            continue # Skip if filter doesn't match
        filtered_results.append(text)

    if filtered_results:
        return "\n".join([f"Found relevant info: {res}" for res in filtered_results])
    return "No relevant information found."

3. Graph Database (Graph DB) – Relational Memory & Complex Queries

Graph databases excel at representing and querying highly interconnected data. Data is stored as nodes (entities) and edges (relationships) with properties. This structure is ideal for understanding relationships between customers, orders, products, and even tracking complex event sequences.

• Role in our Agent:
  • Order Status: Find a customer’s order, then traverse relationships to find the order status and the products within that order.
  • Customer History: Query a customer’s past purchases, support interactions, and associated accounts.
  • Related Products/Cross-Sell: Discover products frequently bought together or products that complement a specific item.
• Conceptual Sample Dataset (for Graph DB – illustrating nodes and edges):

  Nodes (Entities):

  • Customer (id: “cust101”, name: “Alice Johnson”, email: “alice@example.com”)
  • Customer (id: “cust102”, name: “Bob Williams”, email: “bob@example.com”)
  • Product (id: “prodXYZ”, name: “XYZ Smartphone”, category: “Electronics”)
  • Product (id: “prodABC”, name: “ABC Laptop”, category: “Electronics”)
  • Product (id: “prodDEF”, name: “DEF Headphones”, category: “Audio”)
  • Order (id: “order001”, status: “Shipped”, order_date: “2025-05-20”)
  • Order (id: “order002”, status: “Processing”, order_date: “2025-05-25”)

  Edges (Relationships):

  • (cust101)-[:PLACED]->(order001)
  • (cust102)-[:PLACED]->(order002)
  • (order001)-[:CONTAINS {quantity: 1}]->(prodXYZ)
  • (order002)-[:CONTAINS {quantity: 1}]->(prodABC)
  • (order002)-[:CONTAINS {quantity: 1}]->(prodXYZ)
  • (prodXYZ)-[:BOUGHT_TOGETHER_WITH]->(prodDEF) (often inferred from purchase data)
  • (prodDEF)-[:BOUGHT_TOGETHER_WITH]->(prodXYZ)

Conceptual Python Setup for a Graph DB (using a simplified client for Neo4j’s Cypher-like queries):

# For a real project, you'd install: pip install neo4j
# from neo4j import GraphDatabase

# driver = GraphDatabase.driver("bolt://localhost:7687", auth=("neo4j", "password"))

class MockGraphDB:
    def __init__(self):
        # Extremely simplified in-memory representation for demonstration.
        # A real Graph DB client would connect to a persistent database.
        self.data = {
            "customers": {
                "cust101": {"name": "Alice Johnson", "email": "alice@example.com"},
                "cust102": {"name": "Bob Williams", "email": "bob@example.com"},
            },
            "products": {
                "prodXYZ": {"name": "XYZ Smartphone", "category": "Electronics"},
                "prodABC": {"name": "ABC Laptop", "category": "Electronics"},
                "prodDEF": {"name": "DEF Headphones", "category": "Audio"},
            },
            "orders": {
                "order001": {"status": "Shipped", "date": "2025-05-20", "customer_id": "cust101", "items": [{"product_id": "prodXYZ", "qty": 1}]},
                "order002": {"status": "Processing", "date": "2025-05-25", "customer_id": "cust102", "items": [{"product_id": "prodABC", "qty": 1}, {"product_id": "prodXYZ", "qty": 1}]},
            },
            "relationships": {
                "bought_together": {
                    "prodXYZ": ["prodDEF"], # XYZ is bought with DEF
                    "prodDEF": ["prodXYZ"]  # DEF is bought with XYZ
                }
            }
        }

    def execute_cypher_like_query(self, query: str) -> dict:
        """
        Simulates executing a Cypher-like query.
        In a real scenario, this would send the query to a Neo4j driver.
        We're just parsing simple patterns here for demonstration.
        """
        query = query.strip()

        if query.startswith("GET_ORDER_STATUS"):
            parts = query.split()
            if len(parts) == 2:
                order_id = parts[1].replace("order_id=", "").strip("'")
                order_data = self.data["orders"].get(order_id)
                if order_data:
                    customer_data = self.data["customers"].get(order_data["customer_id"])
                    products_in_order = []
                    for item in order_data["items"]:
                        prod_info = self.data["products"].get(item["product_id"])
                        if prod_info:
                            products_in_order.append(f"{item['qty']} x {prod_info['name']}")
                    return {
                        "order_id": order_id,
                        "status": order_data["status"],
                        "date": order_data["date"],
                        "customer_name": customer_data["name"] if customer_data else "Unknown",
                        "products": ", ".join(products_in_order)
                    }
            return {"error": "Invalid GET_ORDER_STATUS query or order not found."}

        elif query.startswith("GET_RELATED_PRODUCTS"):
            parts = query.split("product_name=")
            if len(parts) > 1:
                product_name_query = parts[1].strip().replace("'", "").strip()
                # Find product_id by name (very basic search)
                target_prod_id = None
                for prod_id, prod_info in self.data["products"].items():
                    if prod_info["name"].lower() == product_name_query.lower():
                        target_prod_id = prod_id
                        break
                
                if target_prod_id and target_prod_id in self.data["relationships"]["bought_together"]:
                    related_prod_ids = self.data["relationships"]["bought_together"][target_prod_id]
                    related_names = [self.data["products"].get(pid, {}).get("name", "Unknown Product") for pid in related_prod_ids]
                    return {"related_products": ", ".join(related_names)}
            return {"error": "Invalid GET_RELATED_PRODUCTS query or no related products found."}

        return {"error": "Unknown query format"}

graph_db = MockGraphDB()

def get_order_status_tool(order_id: str) -> str:
    """Tool to get order status from Graph DB."""
    # LLM would generate a simplified query that the mock DB understands
    cypher_like_query = f"GET_ORDER_STATUS order_id='{order_id}'"
    result = graph_db.execute_cypher_like_query(cypher_like_query)
    if not result.get("error"):
        return f"Order {result['order_id']} status: {result['status']}. Ordered by {result['customer_name']} on {result['date']}. Products: {result['products']}."
    return f"Order {order_id} not found: {result.get('error', 'unknown error')}."

def get_related_products_tool(product_name: str) -> str:
    """Tool to get related products from Graph DB."""
    cypher_like_query = f"GET_RELATED_PRODUCTS product_name='{product_name}'"
    result = graph_db.execute_cypher_like_query(cypher_like_query)
    if not result.get("error") and result.get("related_products"):
        return f"Customers who bought {product_name} also often bought: {result['related_products']}."
    return f"No common related products found for {product_name}."

The Agentic Workflow: Step-by-Step with Deeper Explanation

Let’s trace how our Smart Customer Service Assistant handles a user query, “What is the status of my order 001?” and then “Tell me about the XYZ Smartphone and any accessories.”

Initial Setup (Conceptual Agent Orchestration)

Before any query, the agent framework (e.g., LangChain, LlamaIndex) needs to know about the tools. This is often done by providing a list of tool objects, each with a name, description, and the Python function it calls.

# This is a conceptual representation of how tools are registered with an agent framework
available_tools = [
    {
        "name": "get_product_info_tool",
        "description": "Use this tool to get detailed information about a product, such as features, price, colors, or specifications. Input should be a specific product name or descriptive query.",
        "function": retrieve_info_from_vector_db # Points to our Vector DB tool
    },
    {
        "name": "get_order_status_tool",
        "description": "Use this tool to check the current status of a customer's order. Input should be the exact order ID (e.g., 'order001').",
        "function": get_order_status_tool # Points to our Graph DB tool
    },
    {
        "name": "get_related_products_tool",
        "description": "Use this tool to find other products that are often bought together with a specific product. Input should be the exact product name.",
        "function": get_related_products_tool # Points to our Graph DB tool
    }
    # Add more tools here (e.g., send_email_tool, update_address_tool, get_warranty_info_tool)
]

# The agent's main loop:
# def run_agent(user_query: str):
#     thought_history = []
#     while True:
#         # Step 1: LLM decides on a plan/tool
#         llm_prompt = build_planning_prompt(user_query, available_tools, thought_history)
#         llm_decision = query_llm(llm_prompt, stop_sequences=["\n"])
#         thought_history.append(f"LLM Decision: {llm_decision}")

#         # Step 2: Parse LLM decision and execute tool
#         # This parsing logic would be robust in a real framework
#         if llm_decision.startswith("CALL_TOOL("):
#             tool_call_str = llm_decision.replace("CALL_TOOL(", "").rstrip(')')
#             tool_name, args_str = tool_call_str.split('(', 1)
#             args_str = args_str.rstrip(')')
#             # Simple parsing for demonstration; real parsing needs `ast.literal_eval` or JSON
#             args = {}
#             for arg_pair in args_str.split(','):
#                 key, value = arg_pair.split('=', 1)
#                 args[key.strip()] = value.strip().strip("'") # Remove quotes
            
#             # Find and execute the tool
#             selected_tool_func = None
#             for tool in available_tools:
#                 if tool["name"] == tool_name:
#                     selected_tool_func = tool["function"]
#                     break
            
#             if selected_tool_func:
#                 tool_output = selected_tool_func(**args)
#                 thought_history.append(f"Tool {tool_name} Output: {tool_output}")
#             else:
#                 tool_output = f"Error: Tool '{tool_name}' not found."
#                 thought_history.append(tool_output)
            
#             # Step 3: LLM generates final response (or next step)
#             final_prompt = build_response_prompt(user_query, thought_history, tool_output)
#             final_response = query_llm(final_prompt)
#             return final_response
        
#         elif llm_decision.startswith("FINAL_RESPONSE("):
#             return llm_decision.replace("FINAL_RESPONSE(", "").rstrip(')')

#         else:
#             return "Error: Agent could not determine a valid action."

Scenario 1: “What is the status of my order 001?”

Step 1: User Query

User inputs: "What is the status of my order 001?"

Step 2: Intent Recognition & Tool Selection (LLM’s Role)

The agent framework sends a prompt to the LLM. This prompt clearly defines the available tools and their expected usage. The LLM’s goal is to select the correct tool and extract the `order_id`.

Example Prompt to LLM:

You are a helpful customer service AI.
Here are the tools you can use:

Tool 1:
Name: get_product_info_tool
Description: Use this tool to get detailed information about a product, such as features, price, colors, or specifications. Input should be a specific product name or descriptive query.
Arguments: `query` (string)

Tool 2:
Name: get_order_status_tool
Description: Use this tool to check the current status of a customer's order. Input should be the exact order ID (e.g., 'order001').
Arguments: `order_id` (string)

Tool 3:
Name: get_related_products_tool
Description: Use this tool to find other products that are often bought together with a specific product. Input should be the exact product name.
Arguments: `product_name` (string)

User Query: "What is the status of my order 001?"

Think step-by-step:
1. Identify the user's core intent.
2. Determine which tool can fulfill this intent.
3. Extract the necessary arguments for that tool from the user query.
4. Format the tool call correctly.

Respond with ONLY the tool call in the format: CALL_TOOL(tool_name='TOOL_NAME', arg1='VALUE', arg2='VALUE')
If the query does not require a tool, respond with: FINAL_RESPONSE("Your response here")

Simulated LLM Output:

CALL_TOOL(tool_name='get_order_status_tool', order_id='order001')

Step 3: Tool Execution

The agent’s executor parses `CALL_TOOL(tool_name=’get_order_status_tool’, order_id=’order001′)`. It identifies `get_order_status_tool` and calls the corresponding Python function with `order_id=’order001’`.

# Execution
tool_output = get_order_status_tool(order_id='order001')
print(f"Tool Output: {tool_output}")

Simulated Tool Output (from our MockGraphDB):

Tool Output: Order order001 status: Shipped. Ordered by Alice Johnson on 2025-05-20. Products: 1 x XYZ Smartphone.

Step 4: Response Generation (LLM’s Role, again)

The tool’s output is fed back to the LLM along with the original query and the context of what just happened. The LLM’s task is now to synthesize this information into a polite, comprehensive answer for the user.

Example Prompt to LLM:

You are a helpful customer service AI.
User Query: "What is the status of my order 001?"
Tool Executed: get_order_status_tool(order_id='order001')
Tool Output: "Order order001 status: Shipped. Ordered by Alice Johnson on 2025-05-20. Products: 1 x XYZ Smartphone."

Based on the user's query and the information gathered from the tool, provide a clear and helpful response to the user.

Simulated LLM Output (final response to user):

"Your order '001', placed by Alice Johnson on May 20, 2025, has been shipped! It contains one XYZ Smartphone."

Scenario 2: “Tell me about the XYZ Smartphone and any accessories.” (Multi-step reasoning)

Step 1: User Query

User inputs: "Tell me about the XYZ Smartphone and any accessories."

Step 2: Initial Intent & Tool Selection (LLM’s Role)

The LLM receives the prompt. It understands there are two distinct parts to the query: product details and related accessories.

Simulated LLM Output (first decision):

CALL_TOOL(tool_name='get_product_info_tool', query='XYZ Smartphone')

Step 3: First Tool Execution

The agent calls `get_product_info_tool`.

# Execution
tool_output_1 = retrieve_info_from_vector_db(query='XYZ Smartphone')
print(f"Tool Output 1: {tool_output_1}")

Simulated Tool Output 1 (from Vector DB):

Tool Output 1: Found relevant info: XYZ Smartphone: Featuring a stunning 6.7-inch OLED display, powered by the A17 Bionic chip for unparalleled performance. Capture breathtaking photos with its triple 48MP camera system. Comes with 256GB storage. Available in sleek Black and elegant Silver. Price: $999.

Step 4: Subsequent Reasoning & Second Tool Selection (LLM’s Role)

The LLM receives the output from the first tool. It then re-evaluates the original query and its own internal “plan.” It realizes the “accessories” part hasn’t been addressed. It uses the `get_related_products_tool`.

Example Prompt to LLM (after first tool call):

You are a helpful customer service AI.
User Query: "Tell me about the XYZ Smartphone and any accessories."
History:
- LLM Decision: CALL_TOOL(tool_name='get_product_info_tool', query='XYZ Smartphone')
- Tool get_product_info_tool Output: "Found relevant info: XYZ Smartphone: Featuring a stunning 6.7-inch OLED display, powered by the A17 Bionic chip for unparalleled performance. Capture breathtaking photos with its triple 48MP camera system. Comes with 256GB storage. Available in sleek Black and elegant Silver. Price: $999."

Considering the full User Query and the steps already taken, what is the NEXT step?
Respond with ONLY the tool call in the format: CALL_TOOL(tool_name='TOOL_NAME', arg1='VALUE', arg2='VALUE')
If no more tools are needed and you can provide a FINAL_RESPONSE("Your response here")

Simulated LLM Output (second decision):

CALL_TOOL(tool_name='get_related_products_tool', product_name='XYZ Smartphone')

Step 5: Second Tool Execution

The agent calls `get_related_products_tool`.

# Execution
tool_output_2 = get_related_products_tool(product_name='XYZ Smartphone')
print(f"Tool Output 2: {tool_output_2}")

Simulated Tool Output 2 (from Graph DB):

Tool Output 2: Customers who bought XYZ Smartphone also often bought: DEF Headphones.

Step 6: Final Response Generation (LLM’s Role)

All parts of the query have been addressed. The LLM takes all the gathered information and generates a comprehensive final response.

Example Prompt to LLM (after all tool calls):

You are a helpful customer service AI.
User Query: "Tell me about the XYZ Smartphone and any accessories."
History:
- LLM Decision: CALL_TOOL(tool_name='get_product_info_tool', query='XYZ Smartphone')
- Tool get_product_info_tool Output: "Found relevant info: XYZ Smartphone: Featuring a stunning 6.7-inch OLED display, powered by the A17 Bionic chip for unparalleled performance. Capture breathtaking photos with its triple 48MP camera system. Comes with 256GB storage. Available in sleek Black and elegant Silver. Price: $999."
- LLM Decision: CALL_TOOL(tool_name='get_related_products_tool', product_name='XYZ Smartphone')
- Tool get_related_products_tool Output: "Customers who bought XYZ Smartphone also often bought: DEF Headphones."

Based on the user's query and ALL the information gathered from the tools, provide a clear and helpful final response to the user.

Simulated LLM Output (final response to user):

"The XYZ Smartphone is a powerful device featuring a 6.7-inch OLED display, A17 Bionic chip, and a 48MP camera system with 256GB storage, priced at $999. It's available in Black and Silver. Additionally, customers who purchased the XYZ Smartphone often also bought DEF Headphones, which are a popular accessory."

Where to Find Existing Tutorials and Links

While I cannot provide direct clickable links, here’s how you can find excellent resources to implement this type of Agentic AI workflow:

1. For Large Language Models (LLMs) & Hugging Face:

• Hugging Face Transformers Documentation: Search for “Hugging Face Transformers getting started” or “Hugging Face pipelines tutorial.” This is the official and most comprehensive source.
• Hugging Face Hub: Explore models by searching for “LLMs,” “text generation models,” or specific architectures like “Llama 2,” “Mistral,” “Gemma.” Many models have example usage code.
• Fine-tuning Tutorials: Search “Hugging Face fine-tune custom dataset” or “fine-tune LLM for specific task.”
• Online Courses & Blogs: Look for “LLM tutorial Python,” “Hugging Face LLM tutorial.” Platforms like freeCodeCamp, Towards Data Science, and official model blogs often have great guides.

2. For Vector Databases:

• Official Documentation: Each Vector DB (Pinecone, Weaviate, Milvus, Chroma, Qdrant, etc.) has excellent “getting started” guides and Python client libraries. Search for “[Vector DB Name] Python client tutorial.”
• Embedding Models: Search for “Sentence Transformers documentation” or “text embedding models Python.” The `sentence-transformers` library is highly recommended for ease of use.
• RAG (Retrieval Augmented Generation) Tutorials: Search for “RAG LLM tutorial,” “LangChain RAG,” or “LlamaIndex RAG.” These will show you how to integrate Vector DBs with LLMs.

3. For Graph Databases:

• Neo4j Documentation: Neo4j is a very popular choice. Search for “Neo4j Python driver tutorial” or “Neo4j Cypher tutorial.” They have excellent interactive guides.
• Graph Database Concepts: Search for “what is a graph database” or “graph data modeling tutorial” to understand the core principles.
• Graph RAG: For advanced use cases, search for “Graph RAG” or “Knowledge Graph LLM integration” to see how Graph DBs can provide structured knowledge to LLMs.

4. For Agentic AI Frameworks:

• LangChain Documentation: This is currently one of the most popular frameworks. Search for “LangChain Agents tutorial,” “LangChain tools,” “LangChain memory.”
• LlamaIndex Documentation: Another powerful framework, especially strong in data ingestion and RAG. Search for “LlamaIndex agents,” “LlamaIndex tools.”
• OpenAI Assistants API (if applicable): If you plan to use OpenAI’s models, their Assistants API provides a more managed agent-like experience. Search “OpenAI Assistants API tutorial.”

By leveraging these search terms and exploring the official documentation and community tutorials, you’ll find abundant resources to turn this conceptual tutorial into a working Agentic AI system. Happy building!