Welcome to the fascinating world of Agentic AI! If you're a software engineer who's been hearing buzzwords like "AI agents," "tool calling," and "RAG" but haven't quite figured out what they mean or how to build them, you're in the right place. Think of this tutorial as your friendly guide through what might seem like a complex maze but is actually a series of logical, interconnected components that you can master step by step.
WHAT EXACTLY IS AGENTIC AI AND WHY SHOULD YOU CARE?
Imagine you have a really smart assistant who not only understands what you're asking but can also use various tools to get things done. This assistant can search through documents, call APIs, perform calculations, and even write code. Most importantly, this assistant can reason about which tools to use and when to use them, just like a human would approach a complex problem by breaking it down into smaller tasks and using different resources.
That's essentially what an Agentic AI system is. Unlike traditional chatbots that can only respond with text, an AI agent can take actions in the real world through tools. It's the difference between asking someone for directions and having them actually drive you to your destination.
The "agentic" part refers to the AI's ability to act autonomously, make decisions, and use tools to accomplish goals. It's not just responding to prompts; it's actively working toward solving problems by choosing appropriate actions from a toolkit you provide.
UNDERSTANDING THE CORE BUILDING BLOCKS
Before we start coding, let's understand the fundamental concepts that make Agentic AI possible. Think of these as the LEGO blocks we'll use to build our system.
Large Language Models, or LLMs, are the brain of our operation. These are AI systems trained on vast amounts of text that can understand and generate human-like responses. Popular examples include GPT-4, Claude, or open-source models like Llama. The key thing to understand is that LLMs are incredibly good at understanding context, following instructions, and reasoning about problems, but they're limited to generating text responses.
Tool calling is what transforms a text-generating LLM into an action-taking agent. It's a mechanism that allows the LLM to indicate that it wants to use a specific tool with specific parameters. For example, instead of just saying "I would search for information about Python," the LLM can actually call a search tool with the query "Python programming language."
RAG stands for Retrieval-Augmented Generation, which is a fancy way of saying "look up relevant information before answering." Instead of relying solely on the LLM's training data, RAG allows your agent to search through your own documents, databases, or knowledge bases to find current and specific information to include in its responses.
The agent framework is the orchestrator that ties everything together. It receives user requests, decides which tools to use, executes those tools, and synthesizes the results into coherent responses.
HOW ALL THE PIECES FIT TOGETHER
Picture this workflow: A user asks your agent "What were our sales figures for Q3, and how do they compare to industry benchmarks?" Your agent breaks this down into steps. First, it uses a database tool to query your internal sales data. Then, it uses a web search tool to find industry benchmark data. Finally, it uses a calculation tool to perform the comparison and presents a comprehensive answer.
This is the power of combining LLMs with tools and knowledge retrieval. The LLM provides the reasoning and communication abilities, tools provide the action capabilities, and RAG provides access to specific, up-to-date information.
SETTING UP YOUR DEVELOPMENT ENVIRONMENT
Let's start building! First, you'll need Python installed on your system. We'll be using several key libraries that make building Agentic AI systems much easier than doing everything from scratch.
You'll need to install the OpenAI library for LLM integration, LangChain for agent frameworks, ChromaDB for vector storage (used in RAG), and a few other supporting libraries. Create a new Python project and install these dependencies:
pip install openai langchain langchain-openai chromadb requests beautifulsoup4 python-dotenv
Create a new file called main.py where we'll build our agent step by step. Also create a .env file to store your API keys securely.
BUILDING THE FOUNDATION WITH LLM INTEGRATION
Let's start with the most basic component: connecting to an LLM. We'll use OpenAI's GPT models, but the concepts apply to any LLM provider.
import os
from dotenv import load_dotenv
from openai import OpenAI
load_dotenv()
class BasicLLMClient:
def __init__(self):
self.client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
def generate_response(self, prompt):
response = self.client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
temperature=0.7
)
return response.choices[0].message.content
# Test our basic LLM connection
llm = BasicLLMClient()
response = llm.generate_response("Hello, how are you?")
print(response)
This basic setup establishes communication with the LLM. The temperature parameter controls how creative or deterministic the responses are. Lower values make responses more focused and consistent, while higher values introduce more creativity and variation.
IMPLEMENTING YOUR FIRST TOOLS
Now comes the exciting part: giving your AI the ability to use tools. A tool in this context is simply a Python function that the LLM can call with specific parameters. Let's create some basic tools that demonstrate different types of capabilities.
import requests
import json
from datetime import datetime
import math
class ToolRegistry:
def __init__(self):
self.tools = {}
def register_tool(self, name, function, description, parameters):
self.tools[name] = {
"function": function,
"description": description,
"parameters": parameters
}
def get_tool_definitions(self):
tool_definitions = []
for name, tool in self.tools.items():
tool_definitions.append({
"type": "function",
"function": {
"name": name,
"description": tool["description"],
"parameters": tool["parameters"]
}
})
return tool_definitions
def execute_tool(self, name, arguments):
if name in self.tools:
return self.tools[name]["function"](**arguments)
else:
raise ValueError(f"Tool {name} not found")
# Define our first tool: web search
def web_search(query):
"""Search the web for information about a given query"""
# This is a simplified example. In production, you'd use a proper search API
try:
# Using a simple HTTP request to demonstrate the concept
url = f"https://api.duckduckgo.com/?q={query}&format=json&no_html=1&skip_disambig=1"
response = requests.get(url, timeout=10)
data = response.json()
if data.get("AbstractText"):
return f"Search result for '{query}': {data['AbstractText']}"
else:
return f"No specific results found for '{query}'. Try a more specific search term."
except Exception as e:
return f"Search failed: {str(e)}"
# Define a calculator tool
def calculate(expression):
"""Safely evaluate mathematical expressions"""
try:
# Only allow safe mathematical operations
allowed_names = {
k: v for k, v in math.__dict__.items() if not k.startswith("__")
}
allowed_names.update({"abs": abs, "round": round})
result = eval(expression, {"__builtins__": {}}, allowed_names)
return f"Calculation result: {expression} = {result}"
except Exception as e:
return f"Calculation error: {str(e)}"
# Define a current time tool
def get_current_time():
"""Get the current date and time"""
return f"Current time: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
# Set up our tool registry
tools = ToolRegistry()
# Register our tools with their descriptions and parameter schemas
tools.register_tool(
"web_search",
web_search,
"Search the web for information about a topic",
{
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "The search query"
}
},
"required": ["query"]
}
)
tools.register_tool(
"calculate",
calculate,
"Perform mathematical calculations",
{
"type": "object",
"properties": {
"expression": {
"type": "string",
"description": "Mathematical expression to evaluate"
}
},
"required": ["expression"]
}
)
tools.register_tool(
"get_current_time",
get_current_time,
"Get the current date and time",
{
"type": "object",
"properties": {}
}
)
The tool registry acts as a central hub for managing all available tools. Each tool is registered with a description and parameter schema that tells the LLM what the tool does and how to use it. The parameter schema follows JSON Schema format, which is a standard way to describe the structure of JSON data.
CREATING THE TOOL-CALLING AGENT
Now we need to create an agent that can decide when and how to use these tools. This is where the magic happens - the LLM will analyze user requests and determine which tools to call.
class ToolCallingAgent:
def __init__(self, llm_client, tool_registry):
self.llm_client = llm_client
self.tool_registry = tool_registry
def process_request(self, user_message):
# Get tool definitions for the LLM
tool_definitions = self.tool_registry.get_tool_definitions()
# Create the initial message with tool information
messages = [
{
"role": "system",
"content": "You are a helpful assistant that can use tools to answer questions. When you need to use a tool, call the appropriate function with the required parameters."
},
{
"role": "user",
"content": user_message
}
]
# Make the initial request to the LLM
response = self.llm_client.client.chat.completions.create(
model="gpt-4",
messages=messages,
tools=tool_definitions,
tool_choice="auto"
)
# Process the response
return self._handle_response(response, messages)
def _handle_response(self, response, messages):
message = response.choices[0].message
# Check if the LLM wants to use tools
if message.tool_calls:
# Add the assistant's message to our conversation
messages.append({
"role": "assistant",
"content": message.content,
"tool_calls": [
{
"id": tool_call.id,
"type": tool_call.type,
"function": {
"name": tool_call.function.name,
"arguments": tool_call.function.arguments
}
}
for tool_call in message.tool_calls
]
})
# Execute each tool call
for tool_call in message.tool_calls:
function_name = tool_call.function.name
function_args = json.loads(tool_call.function.arguments)
try:
# Execute the tool
tool_result = self.tool_registry.execute_tool(function_name, function_args)
# Add the tool result to our conversation
messages.append({
"role": "tool",
"tool_call_id": tool_call.id,
"content": str(tool_result)
})
except Exception as e:
# Handle tool execution errors
messages.append({
"role": "tool",
"tool_call_id": tool_call.id,
"content": f"Error executing tool: {str(e)}"
})
# Get the final response from the LLM after tool execution
final_response = self.llm_client.client.chat.completions.create(
model="gpt-4",
messages=messages
)
return final_response.choices[0].message.content
else:
# No tools needed, return the direct response
return message.content
# Create our agent
llm_client = BasicLLMClient()
agent = ToolCallingAgent(llm_client, tools)
# Test the agent
response = agent.process_request("What's 15 * 23 + 47?")
print(response)
response = agent.process_request("Search for information about artificial intelligence")
print(response)
response = agent.process_request("What time is it?")
print(response)
This agent implementation handles the complete tool-calling workflow. When the LLM decides it needs to use a tool, it returns a special response indicating which tool to call and with what parameters. Our agent executes these tools and feeds the results back to the LLM, which then formulates a final response incorporating the tool results.
IMPLEMENTING RAG FOR KNOWLEDGE RETRIEVAL
RAG allows your agent to access and search through your own documents and knowledge bases. This is incredibly powerful because it means your agent can answer questions about your specific data, not just general knowledge from its training.
The core concept behind RAG involves converting text into numerical representations called embeddings, storing these embeddings in a vector database, and then searching for similar content when needed.
import chromadb
from chromadb.utils import embedding_functions
class RAGSystem:
def __init__(self):
# Initialize ChromaDB client
self.client = chromadb.Client()
# Create or get a collection for our documents
self.collection = self.client.get_or_create_collection(
name="knowledge_base",
embedding_function=embedding_functions.OpenAIEmbeddingFunction(
api_key=os.getenv("OPENAI_API_KEY"),
model_name="text-embedding-ada-002"
)
)
def add_document(self, content, document_id, metadata=None):
"""Add a document to the knowledge base"""
if metadata is None:
metadata = {}
# Split the document into chunks for better retrieval
chunks = self._split_text(content)
# Add each chunk to the collection
for i, chunk in enumerate(chunks):
chunk_id = f"{document_id}_chunk_{i}"
self.collection.add(
documents=[chunk],
ids=[chunk_id],
metadatas=[{**metadata, "source_document": document_id, "chunk_index": i}]
)
def _split_text(self, text, chunk_size=1000, overlap=200):
"""Split text into overlapping chunks"""
chunks = []
start = 0
while start < len(text):
end = start + chunk_size
chunk = text[start:end]
# Try to break at a sentence boundary
if end < len(text):
last_period = chunk.rfind('.')
last_newline = chunk.rfind('\n')
break_point = max(last_period, last_newline)
if break_point > start + chunk_size // 2:
chunk = text[start:break_point + 1]
end = break_point + 1
chunks.append(chunk.strip())
start = end - overlap
if start >= len(text):
break
return chunks
def search(self, query, n_results=3):
"""Search for relevant documents"""
results = self.collection.query(
query_texts=[query],
n_results=n_results
)
# Format the results
retrieved_docs = []
for i in range(len(results['documents'][0])):
retrieved_docs.append({
'content': results['documents'][0][i],
'metadata': results['metadatas'][0][i],
'distance': results['distances'][0][i]
})
return retrieved_docs
# Create a RAG-enabled search tool
def rag_search(query):
"""Search through the knowledge base for relevant information"""
try:
results = rag_system.search(query)
if not results:
return "No relevant information found in the knowledge base."
response = f"Found {len(results)} relevant documents for '{query}':\n\n"
for i, result in enumerate(results, 1):
response += f"Result {i}:\n{result['content']}\n"
if result['metadata'].get('source_document'):
response += f"Source: {result['metadata']['source_document']}\n"
response += "\n"
return response
except Exception as e:
return f"RAG search failed: {str(e)}"
# Initialize RAG system
rag_system = RAGSystem()
# Add some sample documents
sample_docs = [
{
"id": "company_policy_1",
"content": "Our company vacation policy allows for 20 days of paid vacation per year for full-time employees. Vacation days must be requested at least two weeks in advance and approved by your direct supervisor. Unused vacation days can be carried over to the next year, up to a maximum of 5 days.",
"metadata": {"type": "policy", "department": "HR"}
},
{
"id": "product_manual_1",
"content": "The XR-2000 device features advanced AI processing capabilities with a quad-core neural processing unit. It supports real-time image recognition and can process up to 1000 images per second. The device requires a minimum of 8GB RAM and supports both WiFi and Bluetooth connectivity.",
"metadata": {"type": "manual", "product": "XR-2000"}
}
]
for doc in sample_docs:
rag_system.add_document(doc["content"], doc["id"], doc["metadata"])
# Register the RAG search tool
tools.register_tool(
"rag_search",
rag_search,
"Search through the company knowledge base for relevant information",
{
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "The search query for the knowledge base"
}
},
"required": ["query"]
}
)
The RAG system works by converting both your documents and user queries into embeddings (numerical representations that capture semantic meaning). When you search, it finds documents with embeddings similar to your query's embedding, meaning it can find relevant content even if the exact words don't match.
CREATING AN ADVANCED AGENT WITH MEMORY AND CONTEXT
Now let's enhance our agent with memory capabilities and better context management. This allows the agent to maintain conversation history and make more informed decisions based on previous interactions.
class AdvancedAgent:
def __init__(self, llm_client, tool_registry, max_history=10):
self.llm_client = llm_client
self.tool_registry = tool_registry
self.conversation_history = []
self.max_history = max_history
self.system_prompt = """You are an intelligent AI assistant with access to various tools.
You can search the web, perform calculations, check the current time, and search through
a knowledge base. Always use the most appropriate tools to provide accurate and helpful responses.
When using tools:
1. Choose the right tool for the task
2. Provide clear parameters
3. Interpret the results and provide a comprehensive answer
4. If a tool fails, try alternative approaches or explain the limitation
Be conversational and helpful while being precise with tool usage."""
def chat(self, user_message):
# Add user message to history
self.conversation_history.append({
"role": "user",
"content": user_message
})
# Trim history if it gets too long
if len(self.conversation_history) > self.max_history * 2:
self.conversation_history = self.conversation_history[-self.max_history * 2:]
# Prepare messages for the LLM
messages = [{"role": "system", "content": self.system_prompt}]
messages.extend(self.conversation_history)
# Get tool definitions
tool_definitions = self.tool_registry.get_tool_definitions()
# Process the conversation
response = self._process_with_tools(messages, tool_definitions)
# Add assistant response to history
self.conversation_history.append({
"role": "assistant",
"content": response
})
return response
def _process_with_tools(self, messages, tool_definitions):
max_iterations = 5 # Prevent infinite loops
iteration = 0
while iteration < max_iterations:
response = self.llm_client.client.chat.completions.create(
model="gpt-4",
messages=messages,
tools=tool_definitions,
tool_choice="auto",
temperature=0.7
)
message = response.choices[0].message
if not message.tool_calls:
# No more tools to call, return the response
return message.content
# Add the assistant's message with tool calls
messages.append({
"role": "assistant",
"content": message.content,
"tool_calls": [
{
"id": tool_call.id,
"type": tool_call.type,
"function": {
"name": tool_call.function.name,
"arguments": tool_call.function.arguments
}
}
for tool_call in message.tool_calls
]
})
# Execute tool calls
for tool_call in message.tool_calls:
function_name = tool_call.function.name
try:
function_args = json.loads(tool_call.function.arguments)
tool_result = self.tool_registry.execute_tool(function_name, function_args)
messages.append({
"role": "tool",
"tool_call_id": tool_call.id,
"content": str(tool_result)
})
except Exception as e:
messages.append({
"role": "tool",
"tool_call_id": tool_call.id,
"content": f"Error executing {function_name}: {str(e)}"
})
iteration += 1
return "I apologize, but I reached the maximum number of tool iterations. Please try rephrasing your request."
def reset_conversation(self):
"""Clear the conversation history"""
self.conversation_history = []
def get_conversation_summary(self):
"""Get a summary of the current conversation"""
if not self.conversation_history:
return "No conversation history."
summary_prompt = "Please provide a brief summary of this conversation:\n\n"
for message in self.conversation_history[-6:]: # Last 6 messages
role = message["role"].title()
content = message["content"][:200] + "..." if len(message["content"]) > 200 else message["content"]
summary_prompt += f"{role}: {content}\n"
response = self.llm_client.generate_response(summary_prompt)
return response
# Create our advanced agent
advanced_agent = AdvancedAgent(llm_client, tools)
TESTING YOUR AGENTIC AI SYSTEM
Now let's create a comprehensive testing framework to ensure our agent works correctly across different scenarios.
def test_agent():
"""Test the agent with various scenarios"""
print("=== Testing Agentic AI System ===\n")
test_cases = [
"What's the current time?",
"Calculate 25 * 17 + 100",
"Search for information about machine learning",
"What's our company vacation policy?",
"Tell me about the XR-2000 device specifications",
"What time is it and what's 50 divided by 2?",
"Search for Python programming tips and calculate 15% of 200"
]
for i, test_case in enumerate(test_cases, 1):
print(f"Test {i}: {test_case}")
print("-" * 50)
try:
response = advanced_agent.chat(test_case)
print(f"Response: {response}")
except Exception as e:
print(f"Error: {str(e)}")
print("\n" + "="*60 + "\n")
# Test conversation continuity
print("Testing conversation continuity:")
print("-" * 50)
advanced_agent.reset_conversation()
response1 = advanced_agent.chat("Calculate 10 * 5")
print(f"First: {response1}")
response2 = advanced_agent.chat("Now add 25 to that result")
print(f"Follow-up: {response2}")
print(f"Conversation summary: {advanced_agent.get_conversation_summary()}")
# Run the tests
if __name__ == "__main__":
test_agent()
ADDING ERROR HANDLING AND RESILIENCE
A production-ready agent needs robust error handling and graceful degradation when things go wrong.
import logging
from functools import wraps
import time
# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def retry_on_failure(max_retries=3, delay=1):
"""Decorator to retry functions on failure"""
def decorator(func):
@wraps(func)
def wrapper(*args, **kwargs):
for attempt in range(max_retries):
try:
return func(*args, **kwargs)
except Exception as e:
if attempt == max_retries - 1:
logger.error(f"Function {func.__name__} failed after {max_retries} attempts: {str(e)}")
raise
else:
logger.warning(f"Attempt {attempt + 1} failed for {func.__name__}: {str(e)}. Retrying in {delay} seconds...")
time.sleep(delay)
return None
return wrapper
return decorator
class RobustAgent(AdvancedAgent):
def __init__(self, llm_client, tool_registry, max_history=10):
super().__init__(llm_client, tool_registry, max_history)
self.error_count = 0
self.max_errors = 5
@retry_on_failure(max_retries=2)
def chat(self, user_message):
try:
return super().chat(user_message)
except Exception as e:
self.error_count += 1
logger.error(f"Chat error ({self.error_count}/{self.max_errors}): {str(e)}")
if self.error_count >= self.max_errors:
return "I'm experiencing technical difficulties. Please try again later or contact support."
# Try to provide a fallback response
return f"I encountered an error while processing your request: {str(e)}. Please try rephrasing your question."
def _safe_tool_execution(self, tool_name, arguments):
"""Safely execute a tool with proper error handling"""
try:
return self.tool_registry.execute_tool(tool_name, arguments)
except Exception as e:
logger.error(f"Tool execution error for {tool_name}: {str(e)}")
return f"Tool {tool_name} encountered an error: {str(e)}"
def health_check(self):
"""Perform a health check on the agent"""
health_status = {
"llm_connection": False,
"tools_available": 0,
"rag_system": False,
"error_count": self.error_count
}
try:
# Test LLM connection
test_response = self.llm_client.generate_response("Hello")
health_status["llm_connection"] = bool(test_response)
except:
pass
# Check available tools
health_status["tools_available"] = len(self.tool_registry.tools)
# Test RAG system
try:
rag_system.search("test query", n_results=1)
health_status["rag_system"] = True
except:
pass
return health_status
# Create a robust agent instance
robust_agent = RobustAgent(llm_client, tools)
PERFORMANCE OPTIMIZATION AND BEST PRACTICES
To make your agent production-ready, you need to consider performance, scalability, and user experience.
import asyncio
import aiohttp
from concurrent.futures import ThreadPoolExecutor
import time
class OptimizedAgent(RobustAgent):
def __init__(self, llm_client, tool_registry, max_history=10):
super().__init__(llm_client, tool_registry, max_history)
self.executor = ThreadPoolExecutor(max_workers=4)
self.response_cache = {}
self.cache_ttl = 300 # 5 minutes
def _get_cache_key(self, message):
"""Generate a cache key for responses"""
return hash(message.lower().strip())
def _is_cache_valid(self, timestamp):
"""Check if cached response is still valid"""
return time.time() - timestamp < self.cache_ttl
def chat_with_cache(self, user_message):
"""Chat with response caching for repeated queries"""
cache_key = self._get_cache_key(user_message)
# Check cache first
if cache_key in self.response_cache:
cached_response, timestamp = self.response_cache[cache_key]
if self._is_cache_valid(timestamp):
logger.info(f"Returning cached response for: {user_message[:50]}...")
return f"{cached_response}\n\n[Note: This response was retrieved from cache]"
# Generate new response
response = self.chat(user_message)
# Cache the response
self.response_cache[cache_key] = (response, time.time())
return response
async def parallel_tool_execution(self, tool_calls):
"""Execute multiple tools in parallel when possible"""
async def execute_tool_async(tool_call):
loop = asyncio.get_event_loop()
function_name = tool_call.function.name
function_args = json.loads(tool_call.function.arguments)
# Execute tool in thread pool to avoid blocking
result = await loop.run_in_executor(
self.executor,
self.tool_registry.execute_tool,
function_name,
function_args
)
return {
"tool_call_id": tool_call.id,
"result": result
}
# Execute all tools in parallel
tasks = [execute_tool_async(tool_call) for tool_call in tool_calls]
results = await asyncio.gather(*tasks, return_exceptions=True)
return results
def clear_cache(self):
"""Clear the response cache"""
self.response_cache.clear()
logger.info("Response cache cleared")
def get_performance_stats(self):
"""Get performance statistics"""
return {
"cache_size": len(self.response_cache),
"error_count": self.error_count,
"conversation_length": len(self.conversation_history),
"available_tools": len(self.tool_registry.tools)
}
# Performance monitoring decorator
def monitor_performance(func):
@wraps(func)
def wrapper(*args, **kwargs):
start_time = time.time()
try:
result = func(*args, **kwargs)
execution_time = time.time() - start_time
logger.info(f"{func.__name__} executed in {execution_time:.2f} seconds")
return result
except Exception as e:
execution_time = time.time() - start_time
logger.error(f"{func.__name__} failed after {execution_time:.2f} seconds: {str(e)}")
raise
return wrapper
CREATING A SIMPLE WEB INTERFACE
Let's create a basic web interface so you can interact with your agent through a browser.
from flask import Flask, request, jsonify, render_template_string
import threading
app = Flask(__name__)
# HTML template for the web interface
HTML_TEMPLATE = """
<!DOCTYPE html>
<html>
<head>
<title>Agentic AI Chat</title>
<style>
body { font-family: Arial, sans-serif; margin: 40px; }
.chat-container { max-width: 800px; margin: 0 auto; }
.chat-box { border: 1px solid #ccc; height: 400px; overflow-y: scroll; padding: 10px; margin-bottom: 10px; }
.message { margin: 10px 0; padding: 10px; border-radius: 5px; }
.user { background-color: #e3f2fd; text-align: right; }
.assistant { background-color: #f5f5f5; }
.input-container { display: flex; }
.input-container input { flex: 1; padding: 10px; font-size: 16px; }
.input-container button { padding: 10px 20px; font-size: 16px; }
.status { margin: 10px 0; font-size: 14px; color: #666; }
</style>
</head>
<body>
<div class="chat-container">
<h1>Agentic AI Assistant</h1>
<div id="chat-box" class="chat-box"></div>
<div class="status" id="status">Ready</div>
<div class="input-container">
<input type="text" id="user-input" placeholder="Type your message here..." onkeypress="handleKeyPress(event)">
<button onclick="sendMessage()">Send</button>
</div>
<div style="margin-top: 20px;">
<button onclick="clearChat()">Clear Chat</button>
<button onclick="getStats()">Show Stats</button>
</div>
</div>
<script>
function handleKeyPress(event) {
if (event.key === 'Enter') {
sendMessage();
}
}
function sendMessage() {
const input = document.getElementById('user-input');
const message = input.value.trim();
if (!message) return;
addMessage('user', message);
input.value = '';
document.getElementById('status').textContent = 'Thinking...';
fetch('/chat', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ message: message })
})
.then(response => response.json())
.then(data => {
addMessage('assistant', data.response);
document.getElementById('status').textContent = 'Ready';
})
.catch(error => {
addMessage('assistant', 'Sorry, I encountered an error: ' + error.message);
document.getElementById('status').textContent = 'Error';
});
}
function addMessage(role, content) {
const chatBox = document.getElementById('chat-box');
const messageDiv = document.createElement('div');
messageDiv.className = 'message ' + role;
messageDiv.textContent = content;
chatBox.appendChild(messageDiv);
chatBox.scrollTop = chatBox.scrollHeight;
}
function clearChat() {
document.getElementById('chat-box').innerHTML = '';
fetch('/clear', { method: 'POST' });
}
function getStats() {
fetch('/stats')
.then(response => response.json())
.then(data => {
alert('Performance Stats:\\n' + JSON.stringify(data, null, 2));
});
}
</script>
</body>
</html>
"""
# Global agent instance
web_agent = OptimizedAgent(llm_client, tools)
@app.route('/')
def index():
return render_template_string(HTML_TEMPLATE)
@app.route('/chat', methods=['POST'])
def chat():
try:
data = request.json
user_message = data.get('message', '')
if not user_message:
return jsonify({'error': 'No message provided'}), 400
response = web_agent.chat_with_cache(user_message)
return jsonify({'response': response})
except Exception as e:
logger.error(f"Web chat error: {str(e)}")
return jsonify({'error': 'Internal server error'}), 500
@app.route('/clear', methods=['POST'])
def clear_chat():
web_agent.reset_conversation()
web_agent.clear_cache()
return jsonify({'status': 'cleared'})
@app.route('/stats')
def get_stats():
stats = web_agent.get_performance_stats()
health = web_agent.health_check()
return jsonify({**stats, **health})
def run_web_interface():
"""Run the web interface in a separate thread"""
app.run(debug=False, host='0.0.0.0', port=5000)
# Start the web interface
if __name__ == "__main__":
print("Starting Agentic AI web interface...")
print("Open your browser and go to http://localhost:5000")
run_web_interface()
REAL-WORLD DEPLOYMENT CONSIDERATIONS
When deploying your Agentic AI system in production, you need to consider several important factors that go beyond the basic functionality.
Security is paramount when dealing with AI agents that can execute tools and access data. Always validate and sanitize tool inputs, implement proper authentication and authorization, and never expose sensitive API keys in your code. Use environment variables and secure key management systems.
Scalability becomes important as your system grows. Consider implementing connection pooling for your LLM API calls, using async processing for tool execution, and implementing proper rate limiting to prevent abuse. You might also want to implement a queue system for handling multiple concurrent requests.
Monitoring and logging are essential for maintaining a production system. Log all tool executions, track response times, monitor error rates, and set up alerts for system failures. This helps you identify issues quickly and improve your system over time.
Cost management is crucial since LLM API calls can become expensive with high usage. Implement response caching for repeated queries, optimize your prompts to reduce token usage, and consider using smaller models for simpler tasks while reserving more powerful models for complex reasoning.
TROUBLESHOOTING COMMON ISSUES
During development and deployment, you'll likely encounter several common issues. Here's how to diagnose and fix them.
If your agent isn't calling tools when expected, check that your tool descriptions are clear and specific. The LLM needs to understand exactly what each tool does and when to use it. Make sure your parameter schemas are correct and that you're providing good examples in your system prompt.
When tool execution fails, implement comprehensive error handling and logging. Tools should return meaningful error messages that help the LLM understand what went wrong and potentially try alternative approaches.
If responses are slow, consider implementing parallel tool execution for independent tools, caching responses for repeated queries, and optimizing your prompts to be more concise while maintaining clarity.
Memory issues can occur with long conversations. Implement conversation trimming to keep only recent messages, summarize older parts of conversations, and clear conversation history periodically.
EXTENDING YOUR AGENT WITH CUSTOM TOOLS
The real power of your Agentic AI system comes from creating custom tools that integrate with your specific business systems and workflows.
# Example: Database query tool
def query_database(query, table_name):
"""Execute a safe database query"""
# This is a simplified example - in production, use proper SQL sanitization
allowed_tables = ['users', 'products', 'orders']
if table_name not in allowed_tables:
return f"Access to table '{table_name}' is not allowed"
# Implement your database connection and query logic here
# For this example, we'll return a mock response
return f"Query '{query}' executed on table '{table_name}'. Results: [Mock data]"
# Example: Email sending tool
def send_email(recipient, subject, body):
"""Send an email to a recipient"""
# Implement your email sending logic here
# This could use SMTP, SendGrid, or other email services
return f"Email sent to {recipient} with subject '{subject}'"
# Example: File system tool
def read_file(file_path):
"""Safely read a file from the allowed directory"""
import os
# Security: only allow reading from specific directories
allowed_dirs = ['/app/data', '/app/uploads']
if not any(file_path.startswith(allowed_dir) for allowed_dir in allowed_dirs):
return "Access to this file path is not allowed"
try:
with open(file_path, 'r', encoding='utf-8') as file:
content = file.read()
return f"File content (first 500 chars): {content[:500]}..."
except Exception as e:
return f"Error reading file: {str(e)}"
# Register these new tools
tools.register_tool(
"query_database",
query_database,
"Query a database table with a SQL-like query",
{
"type": "object",
"properties": {
"query": {"type": "string", "description": "The query to execute"},
"table_name": {"type": "string", "description": "The table to query"}
},
"required": ["query", "table_name"]
}
)
tools.register_tool(
"send_email",
send_email,
"Send an email to a recipient",
{
"type": "object",
"properties": {
"recipient": {"type": "string", "description": "Email address of recipient"},
"subject": {"type": "string", "description": "Email subject"},
"body": {"type": "string", "description": "Email body content"}
},
"required": ["recipient", "subject", "body"]
}
)
tools.register_tool(
"read_file",
read_file,
"Read the contents of a file",
{
"type": "object",
"properties": {
"file_path": {"type": "string", "description": "Path to the file to read"}
},
"required": ["file_path"]
}
)
ADVANCED RAG TECHNIQUES
As your knowledge base grows, you'll want to implement more sophisticated RAG techniques for better retrieval accuracy.
class AdvancedRAGSystem(RAGSystem):
def __init__(self):
super().__init__()
self.reranker_enabled = True
def add_document_with_metadata_extraction(self, content, document_id):
"""Add a document with automatic metadata extraction"""
# Extract metadata like document type, key topics, etc.
metadata = self._extract_metadata(content)
self.add_document(content, document_id, metadata)
def _extract_metadata(self, content):
"""Extract metadata from document content using LLM"""
prompt = f"""Analyze this document and extract metadata in JSON format:
- document_type (e.g., policy, manual, report, email)
- key_topics (list of main topics)
- department (if applicable)
- urgency_level (low, medium, high)
Document content:
{content[:1000]}...
Return only valid JSON:"""
try:
response = llm_client.generate_response(prompt)
metadata = json.loads(response)
return metadata
except:
return {"document_type": "unknown"}
def hybrid_search(self, query, n_results=5):
"""Combine semantic search with keyword matching"""
# Get semantic search results
semantic_results = self.search(query, n_results=n_results*2)
# Perform keyword filtering
query_words = set(query.lower().split())
scored_results = []
for result in semantic_results:
content_words = set(result['content'].lower().split())
keyword_overlap = len(query_words.intersection(content_words))
# Combine semantic similarity with keyword overlap
combined_score = (1 - result['distance']) + (keyword_overlap * 0.1)
scored_results.append({
**result,
'combined_score': combined_score
})
# Sort by combined score and return top results
scored_results.sort(key=lambda x: x['combined_score'], reverse=True)
return scored_results[:n_results]
def contextual_search(self, query, conversation_history):
"""Search with conversation context for better relevance"""
# Create an enhanced query using conversation context
context_prompt = f"""Given this conversation history, enhance the search query to be more specific and contextual:
Conversation history:
{conversation_history[-3:]} # Last 3 messages
Original query: {query}
Enhanced query:"""
try:
enhanced_query = llm_client.generate_response(context_prompt)
return self.hybrid_search(enhanced_query)
except:
# Fallback to original query
return self.hybrid_search(query)
# Update the RAG search tool to use advanced features
def advanced_rag_search(query):
"""Advanced search through the knowledge base"""
try:
# Use conversation history if available
conversation_history = getattr(advanced_agent, 'conversation_history', [])
if len(conversation_history) > 2:
results = advanced_rag.contextual_search(query, conversation_history)
else:
results = advanced_rag.hybrid_search(query)
if not results:
return "No relevant information found in the knowledge base."
response = f"Found {len(results)} relevant documents:\n\n"
for i, result in enumerate(results, 1):
response += f"Result {i} (relevance: {result.get('combined_score', 0):.2f}):\n"
response += f"{result['content']}\n"
if result['metadata']:
metadata_str = ", ".join([f"{k}: {v}" for k, v in result['metadata'].items()])
response += f"Metadata: {metadata_str}\n"
response += "\n"
return response
except Exception as e:
return f"Advanced RAG search failed: {str(e)}"
# Initialize advanced RAG system
advanced_rag = AdvancedRAGSystem()
# Update tool registration
tools.tools["rag_search"]["function"] = advanced_rag_search
FINAL INTEGRATION AND TESTING
Let's put everything together and create a comprehensive test suite to validate our complete Agentic AI system.
def comprehensive_test_suite():
"""Run a comprehensive test of all system components"""
print("=== Comprehensive Agentic AI System Test ===\n")
# Initialize the final agent with all components
final_agent = OptimizedAgent(llm_client, tools)
# Test scenarios covering all capabilities
test_scenarios = [
{
"name": "Basic Tool Calling",
"query": "What's the current time and calculate 15 * 23?",
"expected_tools": ["get_current_time", "calculate"]
},
{
"name": "RAG Integration",
"query": "What's our vacation policy and how many days can I carry over?",
"expected_tools": ["rag_search"]
},
{
"name": "Web Search",
"query": "Search for the latest developments in artificial intelligence",
"expected_tools": ["web_search"]
},
{
"name": "Multi-tool Complex Query",
"query": "Search for Python programming tips, then calculate what 20% of 150 is, and tell me the current time",
"expected_tools": ["web_search", "calculate", "get_current_time"]
},
{
"name": "Conversation Continuity",
"queries": [
"Calculate 100 * 25",
"Now divide that result by 4",
"What percentage is that of 1000?"
]
}
]
results = {"passed": 0, "failed": 0, "details": []}
for scenario in test_scenarios:
print(f"Testing: {scenario['name']}")
print("-" * 40)
try:
if "queries" in scenario:
# Test conversation continuity
final_agent.reset_conversation()
for query in scenario["queries"]:
response = final_agent.chat(query)
print(f"Q: {query}")
print(f"A: {response}\n")
results["passed"] += 1
results["details"].append(f"✓ {scenario['name']}: PASSED")
else:
# Test single query
response = final_agent.chat(scenario["query"])
print(f"Q: {scenario['query']}")
print(f"A: {response}")
# Basic validation - check if response is meaningful
if len(response) > 10 and "error" not in response.lower():
results["passed"] += 1
results["details"].append(f"✓ {scenario['name']}: PASSED")
else:
results["failed"] += 1
results["details"].append(f"✗ {scenario['name']}: FAILED - Response too short or contains error")
except Exception as e:
results["failed"] += 1
results["details"].append(f"✗ {scenario['name']}: FAILED - {str(e)}")
print(f"Error: {str(e)}")
print("\n" + "="*60 + "\n")
# Performance and health checks
print("System Health Check:")
print("-" * 40)
health = final_agent.health_check()
stats = final_agent.get_performance_stats()
print(f"LLM Connection: {'✓' if health['llm_connection'] else '✗'}")
print(f"Available Tools: {health['tools_available']}")
print(f"RAG System: {'✓' if health['rag_system'] else '✗'}")
print(f"Error Count: {health['error_count']}")
print(f"Cache Size: {stats['cache_size']}")
# Final summary
print("\n" + "="*60)
print("TEST SUMMARY")
print("="*60)
print(f"Total Tests: {results['passed'] + results['failed']}")
print(f"Passed: {results['passed']}")
print(f"Failed: {results['failed']}")
print(f"Success Rate: {(results['passed'] / (results['passed'] + results['failed']) * 100):.1f}%")
print("\nDetailed Results:")
for detail in results["details"]:
print(detail)
return results
# Run the comprehensive test
if __name__ == "__main__":
test_results = comprehensive_test_suite()
CONCLUSION AND NEXT STEPS
Congratulations! You've just built a complete Agentic AI system from scratch. Your system now includes all the essential components: LLM integration, tool calling capabilities, RAG for knowledge retrieval, conversation memory, error handling, performance optimization, and even a web interface.
This system demonstrates the core principles of Agentic AI: the ability to reason about problems, choose appropriate tools, execute actions, and synthesize results into coherent responses. Your agent can now search the web, perform calculations, query knowledge bases, and maintain conversation context.
The architecture you've built is modular and extensible. You can easily add new tools by implementing functions and registering them with the tool registry. You can enhance the RAG system by adding more sophisticated document processing and retrieval techniques. You can improve the agent's reasoning by refining the system prompts and conversation management.
For production deployment, consider implementing additional features like user authentication, request rate limiting, comprehensive logging and monitoring, automated testing pipelines, and scalable infrastructure using containers and orchestration platforms.
The field of Agentic AI is rapidly evolving, with new techniques and capabilities emerging regularly. Stay updated with the latest developments in LLM capabilities, tool integration patterns, and agent frameworks. Experiment with different models, explore multi-agent systems where multiple AI agents collaborate, and investigate domain-specific applications for your particular use case.
Remember that building effective AI agents is as much about understanding your users' needs and designing appropriate workflows as it is about the technical implementation. Focus on creating agents that genuinely help users accomplish their goals more efficiently and effectively.
Your journey into Agentic AI has just begun. The foundation you've built here can be extended and customized for countless applications, from customer service automation to complex data analysis workflows. The key is to start simple, test thoroughly, and iterate based on real-world usage and feedback.
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