Generative AI
Creating AI AGENTS Building Costs Will Construction Business Hoping Business Flowers, Ochesteste, and Scabilities
In this lesson, we visit the construction and use of the customs agency designed in Pyterch and Key Python tooling, from web intelligence and data scientists in Advanced Code Manufacturers. We will learn how to threaten the basic performance in the CastomTOOL Customstooltools, orchestrates agents associated with the full work travel, and explains the complete effective functioning of competitive tasks and data functioning. On the way, we show the real examples of the world, eliminate the logic reply, vaccinations, and metrics that work, to send up with the agents within your existing organization infrastructure.
!pip install -q torch transformers datasets pillow requests beautifulsoup4 pandas numpy scikit-learn openai
import os, json, asyncio, threading, time
import torch, pandas as pd, numpy as np
from PIL import Image
import requests
from io import BytesIO, StringIO
from concurrent.futures import ThreadPoolExecutor
from functools import wraps, lru_cache
from typing import Dict, List, Optional, Any, Callable, Union
import logging
from dataclasses import dataclass
import inspect
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
API_TIMEOUT = 15
MAX_RETRIES = 3We start by installing and inviting all libraries, including pytors and converts, and databases such as pandas and numpy items, and equipment used for machines. We are preparing for a limited setup of the error and error messages, and specify the Appeals of API Timeouts and the ends of trying to try.
@dataclass
class ToolResult:
"""Standardized tool result structure"""
success: bool
data: Any
error: Optional[str] = None
execution_time: float = 0.0
metadata: Dict[str, Any] = None
class CustomTool:
"""Base class for custom tools"""
def __init__(self, name: str, description: str, func: Callable):
self.name = name
self.description = description
self.func = func
self.calls = 0
self.avg_execution_time = 0.0
self.error_rate = 0.0
def execute(self, *args, **kwargs) -> ToolResult:
"""Execute tool with monitoring"""
start_time = time.time()
self.calls += 1
try:
result = self.func(*args, **kwargs)
execution_time = time.time() - start_time
self.avg_execution_time = ((self.avg_execution_time * (self.calls - 1)) + execution_time) / self.calls
return ToolResult(
success=True,
data=result,
execution_time=execution_time,
metadata={'tool_name': self.name, 'call_count': self.calls}
)
except Exception as e:
execution_time = time.time() - start_time
self.error_rate = (self.error_rate * (self.calls - 1) + 1) / self.calls
logger.error(f"Tool {self.name} failed: {str(e)}")
return ToolResult(
success=False,
data=None,
error=str(e),
execution_time=execution_time,
metadata={'tool_name': self.name, 'call_count': self.calls}
)It describes Tablersual DataClass to include all the effects of killing all the result, no matter how long, it took, any restored data, and error details if failed. Our Customtool section and wrap the individual activities of each person using the calculations calculations, time dense measures, include average running time, and logs any errors. By balancing the tools of tools and maticers that apply this way, we ensure consistency and recognition for all our custom resources.
class CustomAgent:
"""Custom agent implementation with tool management"""
def __init__(self, name: str, system_prompt: str = "", max_iterations: int = 5):
self.name = name
self.system_prompt = system_prompt
self.max_iterations = max_iterations
self.tools = {}
self.conversation_history = []
self.performance_metrics = {}
def add_tool(self, tool: CustomTool):
"""Add a tool to the agent"""
self.tools[tool.name] = tool
def run(self, task: str) -> Dict[str, Any]:
"""Execute a task using available tools"""
logger.info(f"Agent {self.name} executing task: {task}")
task_lower = task.lower()
results = []
if any(keyword in task_lower for keyword in ['analyze', 'website', 'url', 'web']):
if 'advanced_web_intelligence' in self.tools:
import re
url_pattern = r'https?://[^s]+'
urls = re.findall(url_pattern, task)
if urls:
result = self.tools['advanced_web_intelligence'].execute(urls[0])
results.append(result)
elif any(keyword in task_lower for keyword in ['data', 'analyze', 'stats', 'csv']):
if 'advanced_data_science_toolkit' in self.tools:
if 'name,age,salary' in task:
data_start = task.find('name,age,salary')
data_part = task[data_start:]
result = self.tools['advanced_data_science_toolkit'].execute(data_part, 'stats')
results.append(result)
elif any(keyword in task_lower for keyword in ['generate', 'code', 'api', 'client']):
if 'advanced_code_generator' in self.tools:
result = self.tools['advanced_code_generator'].execute(task)
results.append(result)
return {
'agent': self.name,
'task': task,
'results': [r.data if r.success else {'error': r.error} for r in results],
'execution_summary': {
'tools_used': len(results),
'success_rate': sum(1 for r in results if r.success) / len(results) if results else 0,
'total_time': sum(r.execution_time for r in results)
}
}We allow our AI logic in the traditional order, promptly, and the history of each committee. On the road in Run () Invite the function, select the appropriate tool (web syntelligence, and compiling the standard response, and includes the effects of the usual response.
print("🏗️ Building Advanced Tool Architecture")
def performance_monitor(func):
"""Decorator for monitoring tool performance"""
@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}s")
return result
except Exception as e:
logger.error(f"{func.__name__} failed: {str(e)}")
raise
return wrapper
@performance_monitor
def advanced_web_intelligence(url: str, analysis_type: str = "comprehensive") -> Dict[str, Any]:
"""
Advanced web intelligence gathering with multiple analysis modes.
Args:
url: Target URL for analysis
analysis_type: Type of analysis (comprehensive, sentiment, technical, seo)
Returns:
Dict containing structured analysis results
"""
try:
response = requests.get(url, timeout=API_TIMEOUT, headers={
'User-Agent': 'Mozilla/5.0'
})
from bs4 import BeautifulSoup
soup = BeautifulSoup(response.content, 'html.parser')
title = soup.find('title').text if soup.find('title') else 'No title'
meta_desc = soup.find('meta', attrs={'name': 'description'})
meta_desc = meta_desc.get('content') if meta_desc else 'No description'
if analysis_type == "comprehensive":
return {
'title': title,
'description': meta_desc,
'word_count': len(soup.get_text().split()),
'image_count': len(soup.find_all('img')),
'link_count': len(soup.find_all('a')),
'headers': [h.text.strip() for h in soup.find_all(['h1', 'h2', 'h3'])[:5]],
'status_code': response.status_code,
'content_type': response.headers.get('content-type', 'unknown'),
'page_size': len(response.content)
}
elif analysis_type == "sentiment":
text = soup.get_text()[:2000]
positive_words = ['good', 'great', 'excellent', 'amazing', 'wonderful', 'fantastic']
negative_words = ['bad', 'terrible', 'awful', 'horrible', 'disappointing']
pos_count = sum(text.lower().count(word) for word in positive_words)
neg_count = sum(text.lower().count(word) for word in negative_words)
return {
'sentiment_score': pos_count - neg_count,
'positive_indicators': pos_count,
'negative_indicators': neg_count,
'text_sample': text[:200],
'analysis_type': 'sentiment'
}
except Exception as e:
return {'error': f"Analysis failed: {str(e)}"}
@performance_monitor
def advanced_data_science_toolkit(data: str, operation: str) -> Dict[str, Any]:
"""
Comprehensive data science operations with statistical analysis.
Args:
data: CSV-like string or JSON data
operation: Type of analysis (stats, correlation, forecast, clustering)
Returns:
Dict with analysis results
"""
try:
if data.startswith('{') or data.startswith('['):
parsed_data = json.loads(data)
df = pd.DataFrame(parsed_data)
else:
df = pd.read_csv(StringIO(data))
if operation == "stats":
numeric_columns = df.select_dtypes(include=[np.number]).columns.tolist()
result = {
'shape': df.shape,
'columns': df.columns.tolist(),
'dtypes': {col: str(dtype) for col, dtype in df.dtypes.items()},
'missing_values': df.isnull().sum().to_dict(),
'numeric_columns': numeric_columns
}
if len(numeric_columns) > 0:
result['summary_stats'] = df[numeric_columns].describe().to_dict()
if len(numeric_columns) > 1:
result['correlation_matrix'] = df[numeric_columns].corr().to_dict()
return result
elif operation == "clustering":
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
numeric_df = df.select_dtypes(include=[np.number])
if numeric_df.shape[1] < 2:
return {'error': 'Need at least 2 numeric columns for clustering'}
scaler = StandardScaler()
scaled_data = scaler.fit_transform(numeric_df.fillna(0))
n_clusters = min(3, max(2, len(numeric_df) // 2))
kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)
clusters = kmeans.fit_predict(scaled_data)
return {
'n_clusters': n_clusters,
'cluster_centers': kmeans.cluster_centers_.tolist(),
'cluster_labels': clusters.tolist(),
'inertia': float(kmeans.inertia_),
'feature_names': numeric_df.columns.tolist()
}
except Exception as e:
return {'error': f"Data analysis failed: {str(e)}"}
@performance_monitor
def advanced_code_generator(task_description: str, language: str = "python") -> Dict[str, str]:
"""
Advanced code generation with multiple language support and optimization.
Args:
task_description: Description of coding task
language: Target programming language
Returns:
Dict with generated code and metadata
"""
templates = {
'python': {
'api_client': '''
import requests
import json
import time
from typing import Dict, Any, Optional
class APIClient:
"""Production-ready API client with retry logic and error handling"""
def __init__(self, base_url: str, api_key: Optional[str] = None, timeout: int = 30):
self.base_url = base_url.rstrip('/')
self.timeout = timeout
self.session = requests.Session()
if api_key:
self.session.headers.update({'Authorization': f'Bearer {api_key}'})
self.session.headers.update({
'Content-Type': 'application/json',
'User-Agent': 'CustomAPIClient/1.0'
})
def _make_request(self, method: str, endpoint: str, **kwargs) -> Dict[str, Any]:
"""Make HTTP request with retry logic"""
url = f'{self.base_url}/{endpoint.lstrip("/")}'
for attempt in range(3):
try:
response = self.session.request(method, url, timeout=self.timeout, **kwargs)
response.raise_for_status()
return response.json() if response.content else {}
except requests.exceptions.RequestException as e:
if attempt == 2: # Last attempt
raise
time.sleep(2 ** attempt) # Exponential backoff
def get(self, endpoint: str, params: Optional[Dict] = None) -> Dict[str, Any]:
return self._make_request('GET', endpoint, params=params)
def post(self, endpoint: str, data: Optional[Dict] = None) -> Dict[str, Any]:
return self._make_request('POST', endpoint, json=data)
def put(self, endpoint: str, data: Optional[Dict] = None) -> Dict[str, Any]:
return self._make_request('PUT', endpoint, json=data)
def delete(self, endpoint: str) -> Dict[str, Any]:
return self._make_request('DELETE', endpoint)
''',
'data_processor': '''
import pandas as pd
import numpy as np
from typing import List, Dict, Any, Optional
import logging
logger = logging.getLogger(__name__)
class DataProcessor:
"""Advanced data processor with comprehensive cleaning and analysis"""
def __init__(self, data: pd.DataFrame):
self.original_data = data.copy()
self.processed_data = data.copy()
self.processing_log = []
def clean_data(self, strategy: str="auto") -> 'DataProcessor':
"""Clean data with configurable strategies"""
initial_shape = self.processed_data.shape
# Remove duplicates
self.processed_data = self.processed_data.drop_duplicates()
# Handle missing values based on strategy
if strategy == 'auto':
# For numeric columns, use mean
numeric_cols = self.processed_data.select_dtypes(include=[np.number]).columns
self.processed_data[numeric_cols] = self.processed_data[numeric_cols].fillna(
self.processed_data[numeric_cols].mean()
)
# For categorical columns, use mode
categorical_cols = self.processed_data.select_dtypes(include=['object']).columns
for col in categorical_cols:
mode_value = self.processed_data[col].mode()
if len(mode_value) > 0:
self.processed_data[col] = self.processed_data[col].fillna(mode_value[0])
final_shape = self.processed_data.shape
self.processing_log.append(f"Cleaned data: {initial_shape} -> {final_shape}")
return self
def normalize(self, method: str="minmax", columns: Optional[List[str]] = None) -> 'DataProcessor':
"""Normalize numerical columns"""
cols = columns or self.processed_data.select_dtypes(include=[np.number]).columns.tolist()
if method == 'minmax':
# Min-max normalization
for col in cols:
col_min, col_max = self.processed_data[col].min(), self.processed_data[col].max()
if col_max != col_min:
self.processed_data[col] = (self.processed_data[col] - col_min) / (col_max - col_min)
elif method == 'zscore':
# Z-score normalization
for col in cols:
mean_val, std_val = self.processed_data[col].mean(), self.processed_data[col].std()
if std_val != 0:
self.processed_data[col] = (self.processed_data[col] - mean_val) / std_val
self.processing_log.append(f"Normalized columns {cols} using {method}")
return self
def get_insights(self) -> Dict[str, Any]:
"""Generate comprehensive data insights"""
insights = {
'basic_info': {
'shape': self.processed_data.shape,
'columns': self.processed_data.columns.tolist(),
'dtypes': {col: str(dtype) for col, dtype in self.processed_data.dtypes.items()}
},
'data_quality': {
'missing_values': self.processed_data.isnull().sum().to_dict(),
'duplicate_rows': self.processed_data.duplicated().sum(),
'memory_usage': self.processed_data.memory_usage(deep=True).to_dict()
},
'processing_log': self.processing_log
}
# Add statistical summary for numeric columns
numeric_data = self.processed_data.select_dtypes(include=[np.number])
if len(numeric_data.columns) > 0:
insights['statistical_summary'] = numeric_data.describe().to_dict()
return insights
'''
}
}
task_lower = task_description.lower()
if any(keyword in task_lower for keyword in ['api', 'client', 'http', 'request']):
code = templates[language]['api_client']
description = "Production-ready API client with retry logic and comprehensive error handling"
elif any(keyword in task_lower for keyword in ['data', 'process', 'clean', 'analyze']):
code = templates[language]['data_processor']
description = "Advanced data processor with cleaning, normalization, and insight generation"
else:
code = f'''# Generated code template for: {task_description}
# Language: {language}
class CustomSolution:
"""Auto-generated solution template"""
def __init__(self):
self.initialized = True
def execute(self, *args, **kwargs):
"""Main execution method - implement your logic here"""
return {{"message": "Implement your custom logic here", "task": "{task_description}"}}
# Usage example:
# solution = CustomSolution()
# result = solution.execute()
'''
description = f"Custom template for {task_description}"
return {
'code': code,
'language': language,
'description': description,
'complexity': 'production-ready',
'estimated_lines': len(code.split('n')),
'features': ['error_handling', 'logging', 'type_hints', 'documentation']
}We threaten each basic work on @ Performance_Nonitor Recorator to access work tools, and use three web tools, Advalted_Web Monitor performance and maintain conformity to all our Analytics and generators.
print("🤖 Setting up Custom Agent Framework")
class AgentOrchestrator:
"""Manages multiple specialized agents with workflow coordination"""
def __init__(self):
self.agents = {}
self.workflows = {}
self.results_cache = {}
self.performance_metrics = {}
def create_specialist_agent(self, name: str, tools: List[CustomTool], system_prompt: str = None):
"""Create domain-specific agents"""
agent = CustomAgent(
name=name,
system_prompt=system_prompt or f"You are a specialist {name} agent.",
max_iterations=5
)
for tool in tools:
agent.add_tool(tool)
self.agents[name] = agent
return agent
def execute_workflow(self, workflow_name: str, inputs: Dict) -> Dict:
"""Execute multi-step workflows across agents"""
if workflow_name not in self.workflows:
raise ValueError(f"Workflow {workflow_name} not found")
workflow = self.workflows[workflow_name]
results = {}
workflow_start = time.time()
for step in workflow['steps']:
agent_name = step['agent']
task = step['task'].format(**inputs, **results)
if agent_name in self.agents:
step_start = time.time()
result = self.agents[agent_name].run(task)
step_time = time.time() - step_start
results[step['output_key']] = result
results[f"{step['output_key']}_time"] = step_time
total_time = time.time() - workflow_start
return {
'workflow': workflow_name,
'inputs': inputs,
'results': results,
'metadata': {
'total_execution_time': total_time,
'steps_completed': len(workflow['steps']),
'success': True
}
}
def get_system_status(self) -> Dict[str, Any]:
"""Get comprehensive system status"""
return {
'agents': {name: {'tools': len(agent.tools)} for name, agent in self.agents.items()},
'workflows': list(self.workflows.keys()),
'cache_size': len(self.results_cache),
'total_tools': sum(len(agent.tools) for agent in self.agents.values())
}
orchestrator = AgentOrchestrator()
web_tool = CustomTool(
name="advanced_web_intelligence",
description="Advanced web analysis and intelligence gathering",
func=advanced_web_intelligence
)
data_tool = CustomTool(
name="advanced_data_science_toolkit",
description="Comprehensive data science and statistical analysis",
func=advanced_data_science_toolkit
)
code_tool = CustomTool(
name="advanced_code_generator",
description="Advanced code generation and architecture",
func=advanced_code_generator
)
web_agent = orchestrator.create_specialist_agent(
"web_analyst",
[web_tool],
"You are a web analysis specialist. Provide comprehensive website analysis and insights."
)
data_agent = orchestrator.create_specialist_agent(
"data_scientist",
[data_tool],
"You are a data science expert. Perform statistical analysis and machine learning tasks."
)
code_agent = orchestrator.create_specialist_agent(
"code_architect",
[code_tool],
"You are a senior software architect. Generate optimized, production-ready code."
)
We start the agentrchestrator to manage our agents AGents AI, register with each Web syntegucts, data science, and rotate three special domain agents: Web_analyst, data. Each agent is sowed by its appropriate floors and the clear plan. This reset enables us to link and issue a multi-step movement across special artificial areas within one framework, combined.
print("⚡ Defining Advanced Workflows")
orchestrator.workflows['competitive_analysis'] = {
'steps': [
{
'agent': 'web_analyst',
'task': 'Analyze website {target_url} with comprehensive analysis',
'output_key': 'website_analysis'
},
{
'agent': 'code_architect',
'task': 'Generate monitoring code for website analysis automation',
'output_key': 'monitoring_code'
}
]
}
orchestrator.workflows['data_pipeline'] = {
'steps': [
{
'agent': 'data_scientist',
'task': 'Analyze the following CSV data with stats operation: {data_input}',
'output_key': 'data_analysis'
},
{
'agent': 'code_architect',
'task': 'Generate data processing pipeline code',
'output_key': 'pipeline_code'
}
]
}
It describes several significant work flows that are important then our Cape Cope Crafting code of the corresponding ETL Pipeline Code code. These funny sustainable areas allow sophisticated jobs to keep the complex duties of the end of a small boelplate.
print("🚀 Running Production Examples")
print("n📊 Advanced Web Intelligence Demo")
try:
web_result = web_agent.run("Analyze with comprehensive analysis type")
print(f"✅ Web Analysis Success: {json.dumps(web_result, indent=2)}")
except Exception as e:
print(f"❌ Web analysis error: {e}")
print("n🔬 Data Science Pipeline Demo")
sample_data = """name,age,salary,department
Alice,25,50000,Engineering
Bob,30,60000,Engineering
Carol,35,70000,Marketing
David,28,55000,Engineering
Eve,32,65000,Marketing"""
try:
data_result = data_agent.run(f"Analyze this data with stats operation: {sample_data}")
print(f"✅ Data Analysis Success: {json.dumps(data_result, indent=2)}")
except Exception as e:
print(f"❌ Data analysis error: {e}")
print("n💻 Code Architecture Demo")
try:
code_result = code_agent.run("Generate an API client for data processing tasks")
print(f"✅ Code Generation Success: Generated {len(code_result['results'][0]['code'].split())} lines of code")
except Exception as e:
print(f"❌ Code generation error: {e}")
print("n🔄 Multi-Agent Workflow Demo")
try:
workflow_inputs = {'target_url': ''}
workflow_result = orchestrator.execute_workflow('competitive_analysis', workflow_inputs)
print(f"✅ Workflow Success: Completed in {workflow_result['metadata']['total_execution_time']:.2f}s")
except Exception as e:
print(f"❌ Workflow error: {e}")
We use a Suite production suite to ensure each part: First, our web is our Web_analyst makes full analysis; Next, Our_scientistism Crunches sample of CSV Stats; Then our_arringectyp is producing API client; And finally planning the end of the final ending work, photographing the success, the results, and the period of time.
print("n📈 System Performance Metrics")
system_status = orchestrator.get_system_status()
print(f"System Status: {json.dumps(system_status, indent=2)}")
print("nTool Performance:")
for agent_name, agent in orchestrator.agents.items():
print(f"n{agent_name}:")
for tool_name, tool in agent.tools.items():
print(f" - {tool_name}: {tool.calls} calls, {tool.avg_execution_time:.3f}s avg, {tool.error_rate:.1%} error rate")
print("n✅ Advanced Custom Agent Framework Complete!")
print("🚀 Production-ready implementation with full monitoring and error handling!")
We end by returning and printing our system of organization orchestrator, listing subscribers, functional flow, and cache size, and you receive each agent size to display telecommunications, flaws. This gives us a real-time view of working and trust before installing the final confirmation of our framework for the production.
In conclusion, we now have a BLUEPRINT for building special AI agents making a complex analysis and generate production quality code, and they can monitor their health and use of resources. Agentrchestrator is tying everything together, allows you to connect to the flow of many steps and capture the granular performance to work on all agents. Whether you change market research, ETL activities, or API Client Generation, the framework provides consideration, reliability and views needed for Enterprise-grade-grade-grade.
Look Codes. All credit for this study goes to research for this project. Also, feel free to follow it Sane and don't forget to join ours 100K + ml subreddit Then sign up for Our newspaper.
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