How to Build AI Agents with Skills and Tools: Complete 2026 Beginner Guide

Published on January 15, 2026 by Remy

What Are AI Agent Skills?

AI agent skills (also called “tools” or “functions”) are capabilities that enable language models to interact with the real world and perform actions beyond text generation. Instead of just responding with text, agents equipped with skills can:

Execute code to perform calculations or data analysis
Access files to read, write, and manage documents
Search the web for real-time information
Interact with APIs to integrate with external services
Control computers to automate desktop tasks
Manage databases to store and retrieve data

In 2026, major AI providers like Anthropic (Claude), OpenAI (GPT), and Google (Gemini) all support agent skills through various mechanisms:

Claude: Computer use, bash tool, text editor tool
OpenAI: Function calling, code interpreter, file search
Open-source: LangChain tools, AutoGPT, custom implementations

Why Agent Skills Matter in 2026

The Evolution from Chatbots to Agents

Traditional chatbots (2022-2023):

✅ Answer questions with pre-trained knowledge
✅ Generate human-like text
❌ Cannot access current information
❌ Cannot perform actions
❌ Limited to conversational responses

Modern AI agents (2024-2026):

✅ Everything chatbots can do
✅ Execute real-world tasks
✅ Access real-time data
✅ Integrate with your tools and workflows
✅ Operate autonomously with minimal supervision

Real-World Impact

Consider these practical scenarios:

Before agent skills:

User: “Analyze this CSV file and create a summary report”
AI: “I can’t actually open files, but here’s how you could do it…”

With agent skills:

User: “Analyze this CSV file and create a summary report”
AI: Opens file, reads data, performs analysis, generates charts, creates PDF report
Result: Complete report delivered in seconds

Core Concepts: Understanding AI Agents and Tools

The Agent Loop

AI agents follow a continuous decision-making loop:

1. Perceive → Understand user request and context
2. Plan    → Decide what actions to take
3. Act     → Execute using available tools
4. Observe → Evaluate results
5. Repeat  → Continue until task complete

Example conversation:

User: "Find the latest AI news and email me a summary"

Agent thinking:
1. Perceive: User wants news + email delivery
2. Plan: I need to use web_search tool, then email tool
3. Act: Execute web_search("latest AI news 2026")
4. Observe: Got 10 relevant articles
5. Plan: Summarize articles, then use email tool
6. Act: Send email with summary
7. Done: Confirm to user

Skills vs. Tools vs. Functions (Terminology)

The AI industry uses these terms somewhat interchangeably:

Term	Definition	Example
Tool	A capability the agent can invoke	`web_search`, `file_reader`, `calculator`
Function	The implementation of a tool (technical term)	JavaScript function, API endpoint
Skill	Higher-level capability (may use multiple tools)	“Research assistant” uses search + summarize + write
Action	A single invocation of a tool	Calling `web_search("AI trends")`

In this guide, we’ll use “tools” and “skills” interchangeably.

Official AI Agent Skills: What’s Available

Claude’s Official Tools (Anthropic)

As of 2026, Claude offers three powerful built-in tools:

1. Computer Use (`computer_20241022`)

Allows Claude to interact with a computer like a human:

Control mouse and keyboard
Take screenshots and analyze UI
Navigate applications
Fill forms, click buttons, browse web

Use cases:

Automated testing
Web scraping
Desktop automation
UI interaction

Example:

import anthropic

client = anthropic.Anthropic(api_key="your-api-key")

response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    tools=[
        {
            "type": "computer_20241022",
            "name": "computer",
            "display_width_px": 1920,
            "display_height_px": 1080,
        }
    ],
    messages=[
        {
            "role": "user",
            "content": "Open a browser and search for the latest AI news"
        }
    ]
)

2. Bash Tool (`bash_20241022`)

Execute bash commands in a secure environment:

Run shell scripts
Install packages
Process files with CLI tools
System operations

Use cases:

DevOps automation
File processing
Data transformation
System administration

Example:

response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    tools=[
        {
            "type": "bash_20241022",
            "name": "bash",
        }
    ],
    messages=[
        {
            "role": "user",
            "content": "List all Python files in the current directory and count lines of code"
        }
    ]
)

3. Text Editor (`text_editor_20241022`)

Create and edit files with precision:

View file contents
Edit specific lines
Create new files
String replacement

Use cases:

Code editing
Configuration management
Document generation
Automated refactoring

Example:

response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    tools=[
        {
            "type": "text_editor_20241022",
            "name": "str_replace_editor",
        }
    ],
    messages=[
        {
            "role": "user",
            "content": "Create a Python script that fetches weather data from an API"
        }
    ]
)

OpenAI’s Function Calling

OpenAI’s GPT models support custom function calling:

const functions = [
  {
    name: "get_weather",
    description: "Get current weather for a location",
    parameters: {
      type: "object",
      properties: {
        location: {
          type: "string",
          description: "City name, e.g., San Francisco"
        },
        unit: {
          type: "string",
          enum: ["celsius", "fahrenheit"]
        }
      },
      required: ["location"]
    }
  }
];

const response = await openai.chat.completions.create({
  model: "gpt-4",
  messages: [
    { role: "user", content: "What's the weather in Tokyo?" }
  ],
  functions: functions,
  function_call: "auto"
});

// If GPT wants to call a function, it returns:
// {
//   "name": "get_weather",
//   "arguments": "{\"location\": \"Tokyo\", \"unit\": \"celsius\"}"
// }

// You then execute the function and return results to GPT

Popular Open-Source Tool Frameworks

LangChain Tools

LangChain provides 100+ pre-built tools:

from langchain.agents import load_tools, initialize_agent
from langchain.llms import OpenAI

llm = OpenAI(temperature=0)

# Load built-in tools
tools = load_tools(
    ["wikipedia", "llm-math", "python_repl"],
    llm=llm
)

agent = initialize_agent(
    tools,
    llm,
    agent="zero-shot-react-description",
    verbose=True
)

# Use the agent
agent.run("What's the population of Tokyo? Calculate 10% of that number.")

Available LangChain tools:

Wikipedia search
Google search
WolframAlpha
Python REPL
Shell commands
SQL database
HTTP requests
File operations
And 90+ more…

Building Your First AI Agent with Skills

Let’s build a practical research assistant agent step by step.

Prerequisites

Python 3.8+ installed
API key from Anthropic or OpenAI
Basic Python knowledge
Install required packages:

pip install anthropic openai python-dotenv requests beautifulsoup4

Step 1: Define Your Tools

First, create custom tools for your agent:

import os
import requests
from bs4 import BeautifulSoup
from datetime import datetime

def web_search(query: str, num_results: int = 5) -> list:
    """
    Search the web for information.
    
    Args:
        query: Search query string
        num_results: Number of results to return
    
    Returns:
        List of search results with title, URL, and snippet
    """
    # Using a search API (example with DuckDuckGo)
    try:
        url = f"https://html.duckduckgo.com/html/?q={query}"
        headers = {"User-Agent": "Mozilla/5.0"}
        response = requests.get(url, headers=headers, timeout=10)
        soup = BeautifulSoup(response.text, 'html.parser')
        
        results = []
        for result in soup.find_all('div', class_='result')[:num_results]:
            title = result.find('a', class_='result__a')
            snippet = result.find('a', class_='result__snippet')
            
            if title and snippet:
                results.append({
                    'title': title.text,
                    'url': title['href'],
                    'snippet': snippet.text
                })
        
        return results
    except Exception as e:
        return [{"error": str(e)}]

def save_to_file(content: str, filename: str) -> str:
    """
    Save content to a file.
    
    Args:
        content: Text content to save
        filename: Name of file to create
    
    Returns:
        Success message with file path
    """
    try:
        with open(filename, 'w', encoding='utf-8') as f:
            f.write(content)
        return f"✅ Content saved to {filename}"
    except Exception as e:
        return f"❌ Error saving file: {str(e)}"

def calculate(expression: str) -> str:
    """
    Safely evaluate mathematical expressions.
    
    Args:
        expression: Math expression like "2 + 2" or "10 * 5"
    
    Returns:
        Result of calculation
    """
    try:
        # Safe eval - only allow numbers and basic operators
        allowed = set('0123456789+-*/(). ')
        if not all(c in allowed for c in expression):
            return "Error: Invalid characters in expression"
        
        result = eval(expression)
        return f"{expression} = {result}"
    except Exception as e:
        return f"Error: {str(e)}"

Step 2: Create the Agent with OpenAI

import openai
import json
from dotenv import load_dotenv

load_dotenv()
openai.api_key = os.getenv("OPENAI_API_KEY")

# Define function schemas
functions = [
    {
        "name": "web_search",
        "description": "Search the web for current information on a topic",
        "parameters": {
            "type": "object",
            "properties": {
                "query": {
                    "type": "string",
                    "description": "The search query"
                },
                "num_results": {
                    "type": "integer",
                    "description": "Number of results to return (default 5)"
                }
            },
            "required": ["query"]
        }
    },
    {
        "name": "save_to_file",
        "description": "Save text content to a file",
        "parameters": {
            "type": "object",
            "properties": {
                "content": {
                    "type": "string",
                    "description": "The content to save"
                },
                "filename": {
                    "type": "string",
                    "description": "Name of the file to create"
                }
            },
            "required": ["content", "filename"]
        }
    },
    {
        "name": "calculate",
        "description": "Perform mathematical calculations",
        "parameters": {
            "type": "object",
            "properties": {
                "expression": {
                    "type": "string",
                    "description": "Mathematical expression to evaluate"
                }
            },
            "required": ["expression"]
        }
    }
]

# Map function names to actual functions
available_functions = {
    "web_search": web_search,
    "save_to_file": save_to_file,
    "calculate": calculate
}

def run_agent(user_message: str) -> str:
    """
    Run the agent with tool support.
    """
    messages = [
        {
            "role": "system",
            "content": """You are a helpful research assistant with access to tools.
            When the user asks for information, use web_search to find current data.
            When asked to save information, use save_to_file.
            For calculations, use the calculate tool.
            
            Always explain what you're doing and provide clear, helpful responses."""
        },
        {
            "role": "user",
            "content": user_message
        }
    ]
    
    # Agent loop - allow up to 5 tool calls
    for _ in range(5):
        response = openai.chat.completions.create(
            model="gpt-4",
            messages=messages,
            functions=functions,
            function_call="auto"
        )
        
        message = response.choices[0].message
        
        # If no function call, we're done
        if not message.function_call:
            return message.content
        
        # Execute the function
        function_name = message.function_call.name
        function_args = json.loads(message.function_call.arguments)
        
        print(f"🔧 Calling {function_name} with args: {function_args}")
        
        function_result = available_functions[function_name](**function_args)
        
        # Add function call and result to conversation
        messages.append({
            "role": "assistant",
            "content": None,
            "function_call": {
                "name": function_name,
                "arguments": message.function_call.arguments
            }
        })
        messages.append({
            "role": "function",
            "name": function_name,
            "content": str(function_result)
        })
    
    return "Agent reached maximum iterations"

# Test the agent
if __name__ == "__main__":
    result = run_agent(
        "Search for the latest AI agent developments in 2026, "
        "summarize the top 3 findings, and save the summary to a file."
    )
    print("\n📋 Agent Response:")
    print(result)

Step 3: Create the Same Agent with Claude

import anthropic
import json

client = anthropic.Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))

# Claude uses a different format for tools
claude_tools = [
    {
        "name": "web_search",
        "description": "Search the web for current information on a topic",
        "input_schema": {
            "type": "object",
            "properties": {
                "query": {
                    "type": "string",
                    "description": "The search query"
                },
                "num_results": {
                    "type": "integer",
                    "description": "Number of results to return",
                    "default": 5
                }
            },
            "required": ["query"]
        }
    },
    {
        "name": "save_to_file",
        "description": "Save text content to a file",
        "input_schema": {
            "type": "object",
            "properties": {
                "content": {"type": "string"},
                "filename": {"type": "string"}
            },
            "required": ["content", "filename"]
        }
    },
    {
        "name": "calculate",
        "description": "Perform mathematical calculations",
        "input_schema": {
            "type": "object",
            "properties": {
                "expression": {"type": "string"}
            },
            "required": ["expression"]
        }
    }
]

def run_claude_agent(user_message: str) -> str:
    """
    Run agent with Claude.
    """
    messages = [{"role": "user", "content": user_message}]
    
    # Agent loop
    for _ in range(5):
        response = client.messages.create(
            model="claude-3-5-sonnet-20241022",
            max_tokens=4096,
            tools=claude_tools,
            messages=messages
        )
        
        # Check if Claude wants to use a tool
        if response.stop_reason == "tool_use":
            # Add assistant's response to messages
            messages.append({
                "role": "assistant",
                "content": response.content
            })
            
            # Execute each tool call
            tool_results = []
            for content_block in response.content:
                if content_block.type == "tool_use":
                    tool_name = content_block.name
                    tool_input = content_block.input
                    
                    print(f"🔧 Claude calling {tool_name} with: {tool_input}")
                    
                    # Execute the tool
                    result = available_functions[tool_name](**tool_input)
                    
                    tool_results.append({
                        "type": "tool_result",
                        "tool_use_id": content_block.id,
                        "content": str(result)
                    })
            
            # Send tool results back to Claude
            messages.append({
                "role": "user",
                "content": tool_results
            })
        else:
            # No more tool calls, return final response
            return response.content[0].text
    
    return "Agent reached maximum iterations"

# Test Claude agent
if __name__ == "__main__":
    result = run_claude_agent(
        "Research the latest developments in AI agents for 2026 and create a brief summary."
    )
    print("\n📋 Claude's Response:")
    print(result)

Advanced Agent Skills and Patterns

Multi-Step Workflows

For complex tasks, agents need to chain multiple tools together:

def research_and_publish_workflow(topic: str) -> str:
    """
    Complete workflow: research → analyze → summarize → save → email
    """
    steps = [
        f"1. Search for '{topic}' and gather information",
        f"2. Analyze and extract key insights",
        f"3. Create a comprehensive summary",
        f"4. Save summary to file: {topic.replace(' ', '-')}.md",
        f"5. Confirm completion"
    ]
    
    prompt = f"""Execute this research workflow:
    
    Topic: {topic}
    
    Steps:
    {chr(10).join(steps)}
    
    Use your tools to complete each step methodically."""
    
    return run_agent(prompt)

# Usage
result = research_and_publish_workflow("AI Agent Frameworks 2026")

Error Handling and Retries

Make your agents more robust:

def safe_tool_execution(tool_func, args, max_retries=3):
    """
    Execute a tool with automatic retries.
    """
    for attempt in range(max_retries):
        try:
            result = tool_func(**args)
            return {"success": True, "result": result}
        except Exception as e:
            if attempt == max_retries - 1:
                return {
                    "success": False,
                    "error": str(e),
                    "message": f"Failed after {max_retries} attempts"
                }
            print(f"⚠️ Attempt {attempt + 1} failed: {e}. Retrying...")
            time.sleep(2 ** attempt)  # Exponential backoff

Tool Access Control

Limit what your agent can do for security:

class SafeAgent:
    def __init__(self, allowed_tools=None, restricted_paths=None):
        self.allowed_tools = allowed_tools or []
        self.restricted_paths = restricted_paths or ['/etc', '/sys', '/root']
    
    def can_use_tool(self, tool_name: str) -> bool:
        """Check if tool is allowed."""
        return tool_name in self.allowed_tools
    
    def validate_file_path(self, path: str) -> bool:
        """Check if file path is allowed."""
        abs_path = os.path.abspath(path)
        for restricted in self.restricted_paths:
            if abs_path.startswith(restricted):
                return False
        return True
    
    def execute_tool(self, tool_name: str, args: dict):
        """Safely execute a tool with validation."""
        if not self.can_use_tool(tool_name):
            return {"error": f"Tool '{tool_name}' is not allowed"}
        
        # Validate file paths if present
        if 'filename' in args or 'path' in args:
            path = args.get('filename') or args.get('path')
            if not self.validate_file_path(path):
                return {"error": f"Access to '{path}' is restricted"}
        
        # Execute the tool
        return available_functions[tool_name](**args)

# Usage
safe_agent = SafeAgent(
    allowed_tools=['web_search', 'calculate'],
    restricted_paths=['/etc', '/sys', '~/.ssh']
)

Logging and Observability

Track your agent’s actions:

import logging
from datetime import datetime

class AgentLogger:
    def __init__(self, agent_name: str):
        self.agent_name = agent_name
        self.log_file = f"agent_{agent_name}_{datetime.now().strftime('%Y%m%d')}.log"
        
        logging.basicConfig(
            level=logging.INFO,
            format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
            handlers=[
                logging.FileHandler(self.log_file),
                logging.StreamHandler()
            ]
        )
        self.logger = logging.getLogger(agent_name)
    
    def log_tool_call(self, tool_name: str, args: dict, result: any):
        """Log each tool execution."""
        self.logger.info(f"Tool: {tool_name} | Args: {args}")
        self.logger.info(f"Result: {result}")
    
    def log_user_request(self, request: str):
        """Log user requests."""
        self.logger.info(f"User Request: {request}")
    
    def log_agent_response(self, response: str):
        """Log agent responses."""
        self.logger.info(f"Agent Response: {response}")
    
    def log_error(self, error: Exception):
        """Log errors."""
        self.logger.error(f"Error: {str(error)}", exc_info=True)

# Usage
logger = AgentLogger("research_assistant")
logger.log_user_request("Search for AI news")
logger.log_tool_call("web_search", {"query": "AI news"}, search_results)

Real-World Use Cases and Examples

1. Customer Support Agent

def customer_support_agent():
    """
    Agent that can search knowledge base, create tickets, and send emails.
    """
    tools = [
        {
            "name": "search_knowledge_base",
            "description": "Search internal documentation for solutions"
        },
        {
            "name": "create_ticket",
            "description": "Create a support ticket for escalation"
        },
        {
            "name": "send_email",
            "description": "Send email to customer"
        },
        {
            "name": "check_order_status",
            "description": "Look up order status in database"
        }
    ]
    
    system_prompt = """You are a customer support agent. Your goals:
    1. Understand the customer's issue clearly
    2. Search the knowledge base for solutions
    3. If you can solve it, provide clear steps
    4. If you can't, create a ticket and notify the customer
    5. Always be empathetic and professional
    """
    
    return create_agent(tools, system_prompt)

2. Data Analysis Agent

def data_analyst_agent():
    """
    Agent that can query databases, create visualizations, and generate reports.
    """
    tools = [
        {
            "name": "query_database",
            "description": "Execute SQL queries on the database"
        },
        {
            "name": "create_chart",
            "description": "Generate charts and visualizations"
        },
        {
            "name": "calculate_statistics",
            "description": "Compute statistical measures"
        },
        {
            "name": "export_to_excel",
            "description": "Export data to Excel spreadsheet"
        }
    ]
    
    system_prompt = """You are a data analyst. When asked to analyze data:
    1. Query the database to retrieve relevant data
    2. Perform statistical analysis
    3. Create appropriate visualizations
    4. Summarize insights in plain language
    5. Export results if requested
    """
    
    return create_agent(tools, system_prompt)

3. DevOps Automation Agent

def devops_agent():
    """
    Agent that can monitor servers, deploy code, and manage infrastructure.
    """
    tools = [
        {
            "name": "check_server_health",
            "description": "Monitor server metrics (CPU, memory, disk)"
        },
        {
            "name": "deploy_application",
            "description": "Deploy code to production"
        },
        {
            "name": "restart_service",
            "description": "Restart a system service"
        },
        {
            "name": "analyze_logs",
            "description": "Search and analyze log files"
        },
        {
            "name": "send_alert",
            "description": "Send alerts to on-call team"
        }
    ]
    
    system_prompt = """You are a DevOps agent. Your responsibilities:
    1. Monitor system health continuously
    2. Detect anomalies and potential issues
    3. Take corrective actions when possible
    4. Escalate critical issues to humans
    5. Document all actions taken
    """
    
    return create_agent(tools, system_prompt)

Best Practices for Production Agents

1. Design Clear Tool Descriptions

# ❌ Bad: Vague description
{
    "name": "search",
    "description": "Search for stuff"
}

# ✅ Good: Clear, specific description
{
    "name": "web_search",
    "description": """Search the internet for current information using a web search engine.
    Use this when the user asks for recent news, current events, or information
    that may have changed since your training data cutoff.
    
    Returns: List of search results with title, URL, and snippet.""",
    "parameters": {
        "query": "Specific search query (be precise)",
        "num_results": "Number of results to return (1-10)"
    }
}

2. Implement Rate Limiting

from datetime import datetime, timedelta
from collections import defaultdict

class RateLimiter:
    def __init__(self, max_calls: int, time_window: int):
        self.max_calls = max_calls
        self.time_window = time_window  # seconds
        self.calls = defaultdict(list)
    
    def can_call(self, tool_name: str) -> bool:
        """Check if tool can be called."""
        now = datetime.now()
        cutoff = now - timedelta(seconds=self.time_window)
        
        # Remove old calls
        self.calls[tool_name] = [
            call_time for call_time in self.calls[tool_name]
            if call_time > cutoff
        ]
        
        return len(self.calls[tool_name]) < self.max_calls
    
    def record_call(self, tool_name: str):
        """Record a tool call."""
        self.calls[tool_name].append(datetime.now())

# Usage
rate_limiter = RateLimiter(max_calls=10, time_window=60)

if rate_limiter.can_call("web_search"):
    result = web_search("AI news")
    rate_limiter.record_call("web_search")
else:
    print("Rate limit exceeded. Please wait.")

3. Add Caching for Expensive Operations

from functools import lru_cache
import hashlib
import json

class ToolCache:
    def __init__(self, ttl: int = 3600):
        self.cache = {}
        self.ttl = ttl  # Time to live in seconds
    
    def _make_key(self, tool_name: str, args: dict) -> str:
        """Create cache key from tool name and arguments."""
        args_str = json.dumps(args, sort_keys=True)
        return hashlib.md5(f"{tool_name}:{args_str}".encode()).hexdigest()
    
    def get(self, tool_name: str, args: dict):
        """Get cached result if available and not expired."""
        key = self._make_key(tool_name, args)
        if key in self.cache:
            result, timestamp = self.cache[key]
            if datetime.now().timestamp() - timestamp < self.ttl:
                print(f"✨ Cache hit for {tool_name}")
                return result
        return None
    
    def set(self, tool_name: str, args: dict, result):
        """Cache a result."""
        key = self._make_key(tool_name, args)
        self.cache[key] = (result, datetime.now().timestamp())

# Usage
cache = ToolCache(ttl=300)  # 5 minutes

def cached_web_search(query: str):
    # Check cache first
    cached = cache.get("web_search", {"query": query})
    if cached:
        return cached
    
    # Execute search
    result = web_search(query)
    
    # Cache result
    cache.set("web_search", {"query": query}, result)
    return result

4. Monitor Agent Performance

import time
from dataclasses import dataclass, field
from typing import List

@dataclass
class AgentMetrics:
    total_requests: int = 0
    successful_requests: int = 0
    failed_requests: int = 0
    tool_calls: dict = field(default_factory=dict)
    average_response_time: float = 0.0
    response_times: List[float] = field(default_factory=list)
    
    def record_request(self, success: bool, response_time: float, tools_used: List[str]):
        """Record metrics for a request."""
        self.total_requests += 1
        
        if success:
            self.successful_requests += 1
        else:
            self.failed_requests += 1
        
        self.response_times.append(response_time)
        self.average_response_time = sum(self.response_times) / len(self.response_times)
        
        for tool in tools_used:
            self.tool_calls[tool] = self.tool_calls.get(tool, 0) + 1
    
    def get_success_rate(self) -> float:
        """Calculate success rate."""
        if self.total_requests == 0:
            return 0.0
        return (self.successful_requests / self.total_requests) * 100
    
    def print_summary(self):
        """Print metrics summary."""
        print("\n📊 Agent Metrics Summary")
        print(f"Total Requests: {self.total_requests}")
        print(f"Success Rate: {self.get_success_rate():.2f}%")
        print(f"Average Response Time: {self.average_response_time:.2f}s")
        print(f"\nTool Usage:")
        for tool, count in sorted(self.tool_calls.items(), key=lambda x: x[1], reverse=True):
            print(f"  {tool}: {count} calls")

# Usage
metrics = AgentMetrics()

def monitored_agent_run(user_message: str):
    start_time = time.time()
    tools_used = []
    success = False
    
    try:
        result = run_agent(user_message)
        success = True
        return result
    except Exception as e:
        print(f"Error: {e}")
        raise
    finally:
        response_time = time.time() - start_time
        metrics.record_request(success, response_time, tools_used)

# After running many requests
metrics.print_summary()

Troubleshooting Common Issues

Issue 1: Agent Not Using Tools

Symptoms:

Agent responds with text only
No function calls are made
Agent says “I don’t have access to that”

Solutions:

Check system prompt - Explicitly instruct the agent to use tools:

system_prompt = """You MUST use the available tools to complete tasks.
When the user asks for information you don't have, use web_search.
When asked to save data, use save_to_file.
Do NOT make up information or refuse tasks you have tools for."""

Verify function descriptions - Make them clear and specific:

# Bad
"description": "Search"

# Good
"description": "Search the internet for current information. Use this when you need real-time data."

Check model capabilities - Ensure your model supports function calling:

✅ GPT-4, GPT-3.5-turbo (June 2023+)
✅ Claude 3 Opus, Sonnet, Haiku
✅ Gemini Pro
❌ Older models may not support tools

Issue 2: Tool Execution Errors

Symptoms:

Functions fail with exceptions
Timeouts or network errors
Invalid parameters

Solutions:

def robust_tool_execution(tool_func, args):
    """Execute tool with comprehensive error handling."""
    try:
        # Validate parameters
        if not args:
            return {"error": "No arguments provided"}
        
        # Set timeout
        import signal
        signal.alarm(30)  # 30 second timeout
        
        # Execute
        result = tool_func(**args)
        
        # Cancel timeout
        signal.alarm(0)
        
        return {"success": True, "result": result}
        
    except TypeError as e:
        return {"error": f"Invalid parameters: {e}"}
    except requests.Timeout:
        return {"error": "Request timed out"}
    except Exception as e:
        return {"error": f"Unexpected error: {e}"}

Issue 3: Agent Loops Infinitely

Symptoms:

Agent keeps calling the same tool
Never reaches a conclusion
Exceeds max iterations

Solutions:

Limit iterations in your agent loop:

MAX_ITERATIONS = 10  # Prevent infinite loops

for iteration in range(MAX_ITERATIONS):
    if iteration == MAX_ITERATIONS - 1:
        return "Task too complex. Breaking into simpler steps may help."

Track tool calls and prevent repetition:

recent_calls = []

def should_allow_call(tool_name, args):
    """Prevent repeated identical calls."""
    call_signature = (tool_name, json.dumps(args, sort_keys=True))
    
    if call_signature in recent_calls[-3:]:  # Last 3 calls
        return False, "Tool called too recently with same arguments"
    
    recent_calls.append(call_signature)
    return True, None

Improve prompts to be more decisive:

system_prompt = """Complete the task efficiently:
1. Gather information with ONE tool call
2. Process and analyze
3. Provide final answer
4. Do NOT repeat tool calls with the same parameters"""

Security Best Practices

1. Input Sanitization

import re

def sanitize_input(user_input: str) -> str:
    """Remove potentially dangerous input."""
    # Remove shell command characters
    dangerous_chars = [';', '|', '&', '$', '`', '\n', '\r']
    for char in dangerous_chars:
        user_input = user_input.replace(char, '')
    
    # Limit length
    if len(user_input) > 10000:
        raise ValueError("Input too long")
    
    return user_input

def validate_file_path(path: str) -> bool:
    """Ensure file path is safe."""
    # Prevent directory traversal
    if '..' in path or path.startswith('/'):
        return False
    
    # Only allow certain extensions
    allowed_extensions = ['.txt', '.md', '.json', '.csv']
    if not any(path.endswith(ext) for ext in allowed_extensions):
        return False
    
    return True

2. Credential Management

import os
from pathlib import Path

class SecureConfig:
    """Manage API keys and secrets securely."""
    
    def __init__(self):
        self.secrets = {}
        self._load_from_env()
    
    def _load_from_env(self):
        """Load secrets from environment variables."""
        required_keys = [
            'OPENAI_API_KEY',
            'ANTHROPIC_API_KEY',
            'DATABASE_URL'
        ]
        
        for key in required_keys:
            value = os.getenv(key)
            if value:
                self.secrets[key] = value
            else:
                print(f"⚠️ Warning: {key} not found in environment")
    
    def get(self, key: str) -> str:
        """Safely get a secret."""
        if key not in self.secrets:
            raise ValueError(f"Secret '{key}' not configured")
        return self.secrets[key]
    
    def mask_secret(self, secret: str) -> str:
        """Mask secret for logging."""
        if len(secret) <= 8:
            return "****"
        return f"{secret[:4]}...{secret[-4:]}"

# Usage
config = SecureConfig()
api_key = config.get('OPENAI_API_KEY')
print(f"Using API key: {config.mask_secret(api_key)}")

3. Sandboxing Tool Execution

import subprocess
import tempfile
import os

class SandboxedExecutor:
    """Execute code in a sandboxed environment."""
    
    def __init__(self):
        self.temp_dir = tempfile.mkdtemp()
    
    def execute_python(self, code: str, timeout: int = 30):
        """Execute Python code safely."""
        # Create temporary file
        code_file = os.path.join(self.temp_dir, 'script.py')
        with open(code_file, 'w') as f:
            f.write(code)
        
        try:
            # Execute in subprocess with restrictions
            result = subprocess.run(
                ['python3', code_file],
                capture_output=True,
                text=True,
                timeout=timeout,
                cwd=self.temp_dir,  # Restrict to temp directory
                env={'HOME': self.temp_dir}  # Isolated environment
            )
            
            return {
                'stdout': result.stdout,
                'stderr': result.stderr,
                'returncode': result.returncode
            }
        except subprocess.TimeoutExpired:
            return {'error': 'Execution timeout'}
        except Exception as e:
            return {'error': str(e)}
        finally:
            # Cleanup
            if os.path.exists(code_file):
                os.remove(code_file)

Performance Optimization Tips

1. Parallel Tool Execution

For independent tools, execute in parallel:

import asyncio

async def parallel_agent_tasks(tasks: List[dict]):
    """Execute multiple tool calls in parallel."""
    
    async def execute_task(task):
        tool_name = task['tool']
        args = task['args']
        return await async_tool_execution(tool_name, args)
    
    # Run all tasks concurrently
    results = await asyncio.gather(*[execute_task(t) for t in tasks])
    return results

# Usage
tasks = [
    {'tool': 'web_search', 'args': {'query': 'AI news'}},
    {'tool': 'web_search', 'args': {'query': 'ML news'}},
    {'tool': 'calculate', 'args': {'expression': '100 * 2'}}
]

results = asyncio.run(parallel_agent_tasks(tasks))

2. Streaming Responses

For better UX, stream agent responses:

def stream_agent_response(user_message: str):
    """Stream agent responses token by token."""
    
    response = openai.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": user_message}],
        stream=True
    )
    
    print("🤖 Agent: ", end="", flush=True)
    for chunk in response:
        if chunk.choices[0].delta.content:
            print(chunk.choices[0].delta.content, end="", flush=True)
    print()

# Usage
stream_agent_response("Explain AI agents in simple terms")

3. Optimize Tool Response Size

def efficient_web_search(query: str, max_results: int = 5):
    """Return concise search results."""
    results = web_search(query, max_results)
    
    # Truncate long snippets
    for result in results:
        if len(result['snippet']) > 200:
            result['snippet'] = result['snippet'][:200] + "..."
    
    # Return only essential fields
    return [
        {
            'title': r['title'],
            'url': r['url'],
            'snippet': r['snippet']
        }
        for r in results
    ]

Conclusion: The Future of AI Agents

AI agent skills are transforming how we interact with AI systems. In 2026 and beyond, we’ll see:

Emerging Trends

Multi-modal agents: Combining vision, audio, and text
Long-running agents: Agents that work on tasks for hours or days
Agent collaboration: Multiple agents working together
Improved reasoning: Better planning and decision-making
Domain-specific agents: Specialized agents for medicine, law, engineering

Getting Started Today

Start simple: Pick 2-3 tools and build a basic agent
Choose your tools: Use Claude’s built-in tools or OpenAI’s function calling
Test thoroughly: Ensure reliability before production
Monitor closely: Track performance and errors
Iterate rapidly: Improve based on real usage

Key Takeaways

✅ Agent skills = Real-world actions: Moving beyond text generation ✅ Multiple approaches available: Claude, OpenAI, LangChain all support tools ✅ Start with existing tools: Don’t build everything from scratch ✅ Security is critical: Sanitize inputs, limit permissions, sandbox execution ✅ Monitor and optimize: Track metrics and improve continuously

The future is agentic - AI systems that can actually help us accomplish tasks, not just answer questions. Start building your intelligent agents today!

Additional Resources

Official Documentation

Claude Agent Skills: docs.anthropic.com/en/docs/agents-and-tools
OpenAI Function Calling: platform.openai.com/docs/guides/function-calling
LangChain Tools: python.langchain.com/docs/modules/agents/tools

Open Source Projects

AutoGPT: Autonomous AI agent framework
LangChain: Building applications with LLMs
Semantic Kernel: Microsoft’s agent framework
CrewAI: Multi-agent orchestration

Community

r/artificial: Reddit community for AI discussion
LangChain Discord: Active developer community
Anthropic Claude Discord: Official Claude community

Happy building! 🤖✨

What Are AI Agent Skills?

Why Agent Skills Matter in 2026

The Evolution from Chatbots to Agents

Real-World Impact

Core Concepts: Understanding AI Agents and Tools

The Agent Loop

Skills vs. Tools vs. Functions (Terminology)

Official AI Agent Skills: What’s Available

Claude’s Official Tools (Anthropic)

1. Computer Use (computer_20241022)

2. Bash Tool (bash_20241022)

3. Text Editor (text_editor_20241022)

OpenAI’s Function Calling

Popular Open-Source Tool Frameworks

LangChain Tools

Building Your First AI Agent with Skills

Prerequisites

Step 1: Define Your Tools

Step 2: Create the Agent with OpenAI

Step 3: Create the Same Agent with Claude

Advanced Agent Skills and Patterns

Multi-Step Workflows

Error Handling and Retries

Tool Access Control

Logging and Observability

Real-World Use Cases and Examples

1. Customer Support Agent

2. Data Analysis Agent

3. DevOps Automation Agent

Best Practices for Production Agents

1. Design Clear Tool Descriptions

2. Implement Rate Limiting

3. Add Caching for Expensive Operations

4. Monitor Agent Performance

Troubleshooting Common Issues

Issue 1: Agent Not Using Tools

Issue 2: Tool Execution Errors

Issue 3: Agent Loops Infinitely

Security Best Practices

1. Input Sanitization

2. Credential Management

3. Sandboxing Tool Execution

Performance Optimization Tips

1. Parallel Tool Execution

2. Streaming Responses

3. Optimize Tool Response Size

Conclusion: The Future of AI Agents

Emerging Trends

Getting Started Today

Key Takeaways

Additional Resources

Official Documentation

Open Source Projects

Community

1. Computer Use (`computer_20241022`)

2. Bash Tool (`bash_20241022`)

3. Text Editor (`text_editor_20241022`)