Advanced Tool Integration: Workflows, Security, and Optimization

Overview

Imagine a master conductor leading a symphony orchestra. While individual musicians are skilled, the conductor's expertise lies in orchestrating complex interactions—knowing when the strings should take the lead, when to bring in the brass section for emphasis, and how to manage the entire performance as a cohesive, beautiful whole.

This is the challenge of advanced tool integration in AI agents. Beyond basic function calling lies the sophisticated world of tool orchestration, where agents must chain multiple tools together, handle complex workflows, ensure security across tool boundaries, and optimize performance for real-world applications.

In this lesson, we'll explore the advanced patterns that separate simple tool-using agents from sophisticated autonomous systems capable of handling complex, multi-step tasks in production environments.

Learning Objectives

After completing this lesson, you will be able to:

• Design and implement complex tool chaining workflows
• Build parallel and sequential tool execution patterns
• Implement comprehensive security controls for tool access
• Optimize tool performance through caching and monitoring
• Handle sophisticated error recovery and fallback strategies
• Build production-ready tool integration systems

Tool Chaining and Workflows

Interactive Tool Workflow Explorer

Sequential Tool Execution

Many tasks require multiple tools working together in sequence. This is where the ReAct pattern truly shines, allowing agents to reason about what tool to use next based on previous results.

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# Advanced Tool Chaining Implementation
from typing import List, Dict, Any, Optional
from dataclasses import dataclass
from enum import Enum

class WorkflowStep:
    """Represents a single step in a tool workflow"""
    
    def __init__(self, tool_name: str, arguments: Dict[str, Any], 
                 depends_on: List[str] = None, condition: str = None):
        self.tool_name = tool_name
        self.arguments = arguments
        self.depends_on = depends_on or []
        self.condition = condition  # Optional condition for execution
        self.result = None
        self.status = "pending"

class WorkflowOrchestrator:
    """Orchestrates complex tool workflows"""
    
    def __init__(self, tool_registry):
        self.registry = tool_registry
        self.workflow_history = []
    
    def execute_workflow(self, steps: List[WorkflowStep]) -> Dict[str, Any]:
        """Execute a workflow with dependency management"""
        workflow_context = {}
        completed_steps = set()
        
        while len(completed_steps) < len(steps):
            progress_made = False
            
            for i, step in enumerate(steps):
                if i in completed_steps:
                    continue
                
                # Check if dependencies are satisfied
                if self._dependencies_satisfied(step, completed_steps, steps):
                    # Check execution condition
                    if step.condition and not self._evaluate_condition(
                        step.condition, workflow_context
                    ):
                        step.status = "skipped"
                        completed_steps.add(i)
                        continue
                    
                    # Execute the step
                    try:
                        # Resolve argument references to previous results
                        resolved_args = self._resolve_arguments(
                            step.arguments, workflow_context
                        )
                        
                        result = self.registry.execute_with_retry(
                            step.tool_name, resolved_args
                        )
                        
                        step.result = result.result
                        step.status = "completed"
                        workflow_context[f"step_{i}"] = result.result
                        completed_steps.add(i)
                        progress_made = True
                        
                    except Exception as e:
                        step.status = "failed"
                        step.result = str(e)
                        workflow_context[f"step_{i}"] = None
                        # Could implement retry or fallback logic here
            
            if not progress_made:
                # Deadlock - circular dependencies or missing dependencies
                raise ValueError("Workflow deadlock: unable to make progress")
        
        return workflow_context
    
    def _dependencies_satisfied(self, step: WorkflowStep, completed: set, 
                              all_steps: List[WorkflowStep]) -> bool:
        """Check if step dependencies are satisfied"""
        for dep in step.depends_on:
            dep_index = next(
                (i for i, s in enumerate(all_steps) if f"step_{i}" == dep), 
                None
            )
            if dep_index is None or dep_index not in completed:
                return False
        return True
    
    def _resolve_arguments(self, arguments: Dict, context: Dict) -> Dict:
        """Resolve argument references to previous step results"""
        resolved = {}
        
        for key, value in arguments.items():
            if isinstance(value, str) and value.startswith("$"):
                # Reference to previous step result
                ref_key = value[1:]  # Remove the $ prefix
                resolved[key] = context.get(ref_key, value)
            else:
                resolved[key] = value
        
        return resolved
    
    def _evaluate_condition(self, condition: str, context: Dict) -> bool:
        """Evaluate a simple condition (in production, use proper expression parser)"""
        # Simple condition evaluation - in production, use a proper expression parser
        try:
            return eval(condition, {"__builtins__": {}}, context)
        except:
            return False

# Example workflow usage
def create_research_workflow() -> List[WorkflowStep]:
    """Create a workflow for researching and summarizing a topic"""
    return [
        WorkflowStep(
            tool_name="search_web",
            arguments={"query": "latest AI developments 2024"}
        ),
        WorkflowStep(
            tool_name="extract_article_text", 
            arguments={"urls": "$step_0"},
            depends_on=["step_0"]
        ),
        WorkflowStep(
            tool_name="analyze_sentiment",
            arguments={"text": "$step_1"},
            depends_on=["step_1"],
            condition="step_1 is not None"
        ),
        WorkflowStep(
            tool_name="generate_summary",
            arguments={
                "articles": "$step_1",
                "sentiment": "$step_2"
            },
            depends_on=["step_1", "step_2"]
        )
    ]

# Usage example
orchestrator = WorkflowOrchestrator(registry)
workflow_steps = create_research_workflow()
results = orchestrator.execute_workflow(workflow_steps)
print("Workflow completed:", results["step_3"])

Parallel Tool Execution

For independent tasks, tools can be executed in parallel to improve performance and responsiveness:

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# Parallel Tool Execution System
import asyncio
from concurrent.futures import ThreadPoolExecutor, as_completed
from typing import List, Tuple, Dict, Any

class ParallelToolExecutor:
    """Execute multiple tools in parallel for improved performance"""
    
    def __init__(self, tool_registry, max_workers: int = 5):
        self.registry = tool_registry
        self.max_workers = max_workers
    
    async def execute_parallel_async(self, tool_calls: List[Tuple[str, Dict]]) -> Dict[str, Any]:
        """Execute multiple tools in parallel using asyncio"""
        tasks = []
        
        for tool_name, arguments in tool_calls:
            task = asyncio.create_task(
                self._execute_tool_async(tool_name, arguments)
            )
            tasks.append((tool_name, task))
        
        results = {}
        for tool_name, task in tasks:
            try:
                result = await task
                results[tool_name] = result
            except Exception as e:
                results[tool_name] = {"error": str(e)}
        
        return results
    
    def execute_parallel_threads(self, tool_calls: List[Tuple[str, Dict]]) -> Dict[str, Any]:
        """Execute multiple tools in parallel using thread pool"""
        results = {}
        
        with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
            # Submit all tasks
            future_to_tool = {
                executor.submit(self._execute_tool_sync, tool_name, args): tool_name
                for tool_name, args in tool_calls
            }
            
            # Collect results as they complete
            for future in as_completed(future_to_tool):
                tool_name = future_to_tool[future]
                try:
                    result = future.result()
                    results[tool_name] = result
                except Exception as e:
                    results[tool_name] = {"error": str(e)}
        
        return results
    
    async def _execute_tool_async(self, tool_name: str, arguments: Dict) -> Any:
        """Execute a single tool asynchronously"""
        # Wrap synchronous tool execution in async
        loop = asyncio.get_event_loop()
        return await loop.run_in_executor(
            None, 
            lambda: self.registry.execute_with_retry(tool_name, arguments)
        )
    
    def _execute_tool_sync(self, tool_name: str, arguments: Dict) -> Any:
        """Execute a single tool synchronously"""
        return self.registry.execute_with_retry(tool_name, arguments)

# Example: Gather user data from multiple sources
async def gather_comprehensive_user_data(user_id: str):
    """Gather user data from multiple sources in parallel"""
    
    parallel_executor = ParallelToolExecutor(registry)
    
    # Define parallel tool calls
    tool_calls = [
        ("get_user_profile", {"user_id": user_id}),
        ("get_user_preferences", {"user_id": user_id}),
        ("get_user_activity", {"user_id": user_id, "days": 30}),
        ("get_user_social_graph", {"user_id": user_id}),
        ("calculate_user_score", {"user_id": user_id})
    ]
    
    # Execute all tools in parallel
    results = await parallel_executor.execute_parallel_async(tool_calls)
    
    # Combine successful results
    user_data = {
        "user_id": user_id,
        "timestamp": time.time()
    }
    
    for tool_name, result in results.items():
        if "error" not in result:
            user_data[tool_name.replace("get_user_", "")] = result.result
        else:
            print(f"Warning: {tool_name} failed: {result['error']}")
    
    return user_data

# Usage
user_data = asyncio.run(gather_comprehensive_user_data("user123"))
print("Complete user data:", user_data)

Security and Safety Considerations

Tool Access Control

Not all tools should be available to all agents or in all contexts. Implementing proper access control is crucial for production systems:

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# Comprehensive Security Framework for Tools
from enum import Enum
from typing import Set, List, Dict, Optional
import hashlib
import jwt
import time

class Permission(Enum):
    READ_FILES = "read_files"
    WRITE_FILES = "write_files"
    NETWORK_ACCESS = "network_access"
    SYSTEM_COMMANDS = "system_commands"
    USER_DATA_ACCESS = "user_data_access"
    FINANCIAL_OPERATIONS = "financial_operations"
    EMAIL_SEND = "email_send"
    DATABASE_READ = "database_read"
    DATABASE_WRITE = "database_write"

class SecurityContext:
    """Security context for tool execution"""
    
    def __init__(self, user_id: str, permissions: Set[Permission], 
                 session_token: str = None, ip_address: str = None):
        self.user_id = user_id
        self.permissions = permissions
        self.session_token = session_token
        self.ip_address = ip_address
        self.created_at = time.time()
    
    def has_permission(self, permission: Permission) -> bool:
        return permission in self.permissions
    
    def is_valid(self, max_age: int = 3600) -> bool:
        """Check if security context is still valid"""
        age = time.time() - self.created_at
        return age < max_age

class SecureToolRegistry:
    """Tool registry with comprehensive security controls"""
    
    def __init__(self):
        self.tools = {}
        self.tool_permissions = {}  # tool_name -> required permissions
        self.audit_log = []
        self.rate_limits = {}  # user_id -> tool usage tracking
        self.blocked_ips = set()
    
    def register_secure_tool(self, name: str, func, description: str, 
                           parameters: Dict, required_permissions: List[Permission],
                           rate_limit: Optional[int] = None):
        """Register a tool with security requirements"""
        self.tools[name] = {
            "function": func,
            "description": description,
            "parameters": parameters,
            "rate_limit": rate_limit  # calls per minute
        }
        self.tool_permissions[name] = set(required_permissions)
    
    def execute_secure(self, tool_name: str, arguments: Dict, 
                      security_context: SecurityContext) -> Any:
        """Execute tool with comprehensive security checks"""
        
        # 1. Validate security context
        if not security_context.is_valid():
            raise SecurityError("Security context expired")
        
        # 2. Check IP blocking
        if security_context.ip_address in self.blocked_ips:
            raise SecurityError("IP address blocked")
        
        # 3. Check tool existence
        if tool_name not in self.tools:
            raise ValueError(f"Tool '{tool_name}' not found")
        
        # 4. Check permissions
        required_perms = self.tool_permissions.get(tool_name, set())
        for perm in required_perms:
            if not security_context.has_permission(perm):
                self._log_security_violation(
                    security_context, tool_name, f"Missing permission: {perm.value}"
                )
                raise SecurityError(f"Permission denied: {perm.value}")
        
        # 5. Check rate limits
        if not self._check_rate_limit(security_context.user_id, tool_name):
            raise SecurityError("Rate limit exceeded")
        
        # 6. Sanitize inputs
        sanitized_args = self._sanitize_inputs(tool_name, arguments)
        
        # 7. Execute tool
        try:
            result = self.tools[tool_name]["function"](**sanitized_args)
            
            # 8. Log successful execution
            self._log_tool_execution(security_context, tool_name, sanitized_args, True)
            
            return result
            
        except Exception as e:
            # 9. Log failed execution
            self._log_tool_execution(security_context, tool_name, sanitized_args, False, str(e))
            raise
        
        # 10. Update rate limiting counters
        self._update_rate_limit(security_context.user_id, tool_name)
    
    def _check_rate_limit(self, user_id: str, tool_name: str) -> bool:
        """Check if user has exceeded rate limit for tool"""
        tool_config = self.tools[tool_name]
        rate_limit = tool_config.get("rate_limit")
        
        if not rate_limit:
            return True
        
        current_time = time.time()
        user_limits = self.rate_limits.setdefault(user_id, {})
        tool_usage = user_limits.setdefault(tool_name, [])
        
        # Remove old entries (older than 1 minute)
        tool_usage[:] = [t for t in tool_usage if current_time - t < 60]
        
        return len(tool_usage) < rate_limit
    
    def _update_rate_limit(self, user_id: str, tool_name: str):
        """Update rate limiting counters"""
        current_time = time.time()
        self.rate_limits.setdefault(user_id, {}).setdefault(tool_name, []).append(current_time)
    
    def _sanitize_inputs(self, tool_name: str, arguments: Dict) -> Dict:
        """Sanitize tool inputs based on tool-specific rules"""
        sanitized = {}
        
        for key, value in arguments.items():
            if isinstance(value, str):
                # Basic XSS prevention
                value = value.replace("<script", "&lt;script")
                value = value.replace("javascript:", "")
                
                # Path traversal prevention
                if "path" in key.lower() or "file" in key.lower():
                    value = value.replace("..", "")
                    value = value.replace("/", "")
            
            sanitized[key] = value
        
        return sanitized
    
    def _log_security_violation(self, context: SecurityContext, 
                              tool_name: str, violation: str):
        """Log security violations for monitoring"""
        log_entry = {
            "timestamp": time.time(),
            "type": "security_violation",
            "user_id": context.user_id,
            "ip_address": context.ip_address,
            "tool_name": tool_name,
            "violation": violation
        }
        self.audit_log.append(log_entry)
        
        # Could trigger alerts, IP blocking, etc.
        print(f"SECURITY VIOLATION: {violation} by {context.user_id}")
    
    def _log_tool_execution(self, context: SecurityContext, tool_name: str, 
                          arguments: Dict, success: bool, error: str = None):
        """Log tool execution for audit trail"""
        log_entry = {
            "timestamp": time.time(),
            "type": "tool_execution",
            "user_id": context.user_id,
            "tool_name": tool_name,
            "arguments": str(arguments),
            "success": success,
            "error": error
        }
        self.audit_log.append(log_entry)

class SecurityError(Exception):
    """Custom exception for security-related errors"""
    pass

# Example secure tool registration
secure_registry = SecureToolRegistry()

def secure_file_read(file_path: str) -> str:
    """Securely read a file with comprehensive validation"""
    import os
    
    # Validate file path is in allowed directory
    allowed_directories = ["/safe/data/", "/public/files/"]
    if not any(file_path.startswith(d) for d in allowed_directories):
        raise SecurityError("Access to this directory is not allowed")
    
    # Prevent directory traversal
    normalized_path = os.path.normpath(file_path)
    if ".." in normalized_path:
        raise SecurityError("Directory traversal not allowed")
    
    # Check file exists and is readable
    if not os.path.isfile(normalized_path):
        raise ValueError("File does not exist")
    
    try:
        with open(normalized_path, 'r', encoding='utf-8') as f:
            return f.read()
    except PermissionError:
        raise SecurityError("Permission denied")

# Register the secure tool
secure_registry.register_secure_tool(
    name="secure_file_read",
    func=secure_file_read,
    description="Read a file with security controls",
    parameters={
        "type": "object",
        "properties": {
            "file_path": {"type": "string", "description": "Path to file to read"}
        },
        "required": ["file_path"]
    },
    required_permissions=[Permission.READ_FILES],
    rate_limit=10  # 10 calls per minute
)

# Usage with security context
user_context = SecurityContext(
    user_id="user123",
    permissions={Permission.READ_FILES, Permission.NETWORK_ACCESS},
    ip_address="192.168.1.100"
)

try:
    content = secure_registry.execute_secure(
        "secure_file_read",
        {"file_path": "/safe/data/document.txt"},
        user_context
    )
    print("File content:", content)
except SecurityError as e:
    print("Security error:", e)

Input Sanitization

Comprehensive input validation and sanitization is essential for preventing security vulnerabilities:

Tool Performance Optimization

Performance Monitoring and Optimization

Monitor tool performance and optimize based on usage patterns:

Caching Implementation

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# Advanced Caching System for Tools
from functools import wraps
import hashlib
import json
import time
from typing import Optional, Any, Dict, Callable
from dataclasses import dataclass
import threading

@dataclass
class CacheEntry:
    """Represents a cached tool result"""
    result: Any
    timestamp: float
    ttl: int
    hit_count: int = 0
    last_accessed: float = None

class IntelligentToolCache:
    """Advanced caching system with TTL, LRU eviction, and statistics"""
    
    def __init__(self, max_size: int = 1000, default_ttl: int = 300):
        self.cache: Dict[str, CacheEntry] = {}
        self.max_size = max_size
        self.default_ttl = default_ttl
        self.lock = threading.RLock()
        self.stats = {
            "hits": 0,
            "misses": 0,
            "evictions": 0,
            "errors": 0
        }
    
    def get_cache_key(self, tool_name: str, arguments: Dict) -> str:
        """Generate cache key from tool name and arguments"""
        # Sort arguments for consistent keys
        sorted_args = json.dumps(arguments, sort_keys=True, default=str)
        content = f"{tool_name}:{sorted_args}"
        return hashlib.sha256(content.encode()).hexdigest()[:32]
    
    def get(self, tool_name: str, arguments: Dict) -> Optional[Any]:
        """Get cached result if available and not expired"""
        cache_key = self.get_cache_key(tool_name, arguments)
        
        with self.lock:
            if cache_key not in self.cache:
                self.stats["misses"] += 1
                return None
            
            entry = self.cache[cache_key]
            current_time = time.time()
            
            # Check if expired
            if current_time - entry.timestamp > entry.ttl:
                del self.cache[cache_key]
                self.stats["misses"] += 1
                return None
            
            # Update access statistics
            entry.hit_count += 1
            entry.last_accessed = current_time
            self.stats["hits"] += 1
            
            return entry.result
    
    def set(self, tool_name: str, arguments: Dict, result: Any, 
            ttl: Optional[int] = None):
        """Cache a tool result with intelligent eviction"""
        cache_key = self.get_cache_key(tool_name, arguments)
        ttl = ttl or self.default_ttl
        current_time = time.time()
        
        with self.lock:
            # Check if we need to evict entries
            if len(self.cache) >= self.max_size:
                self._evict_least_valuable()
            
            # Store the result
            self.cache[cache_key] = CacheEntry(
                result=result,
                timestamp=current_time,
                ttl=ttl,
                last_accessed=current_time
            )
    
    def _evict_least_valuable(self):
        """Evict the least valuable cache entry using a scoring algorithm"""
        if not self.cache:
            return
        
        current_time = time.time()
        worst_score = float('inf')
        worst_key = None
        
        for key, entry in self.cache.items():
            # Calculate value score based on:
            # - Age (older = less valuable)
            # - Hit count (more hits = more valuable)
            # - Time since last access (recently accessed = more valuable)
            age = current_time - entry.timestamp
            time_since_access = current_time - (entry.last_accessed or entry.timestamp)
            
            # Higher score = less valuable
            score = (age + time_since_access) / max(entry.hit_count, 1)
            
            if score < worst_score:
                worst_score = score
                worst_key = key
        
        if worst_key:
            del self.cache[worst_key]
            self.stats["evictions"] += 1
    
    def invalidate(self, tool_name: str, arguments: Dict = None):
        """Invalidate cached results for a tool"""
        with self.lock:
            if arguments:
                # Invalidate specific cache entry
                cache_key = self.get_cache_key(tool_name, arguments)
                self.cache.pop(cache_key, None)
            else:
                # Invalidate all entries for this tool
                keys_to_remove = [
                    key for key in self.cache.keys() 
                    if key.startswith(tool_name)
                ]
                for key in keys_to_remove:
                    del self.cache[key]
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get cache performance statistics"""
        with self.lock:
            total_requests = self.stats["hits"] + self.stats["misses"]
            hit_rate = self.stats["hits"] / total_requests if total_requests > 0 else 0
            
            return {
                "hit_rate": hit_rate,
                "total_entries": len(self.cache),
                "max_size": self.max_size,
                **self.stats
            }
    
    def clear(self):
        """Clear all cached entries"""
        with self.lock:
            self.cache.clear()

# Caching decorator for tools
def cacheable(ttl: int = 300, cache_instance: IntelligentToolCache = None):
    """Decorator to make tools cacheable"""
    if cache_instance is None:
        cache_instance = IntelligentToolCache()
    
    def decorator(func: Callable):
        @wraps(func)
        def wrapper(*args, **kwargs):
            # Convert args to a dict for consistent caching
            arg_dict = {}
            if args:
                arg_dict.update({f"arg_{i}": arg for i, arg in enumerate(args)})
            arg_dict.update(kwargs)
            
            # Check cache first
            cached_result = cache_instance.get(func.__name__, arg_dict)
            if cached_result is not None:
                return cached_result
            
            # Execute function and cache result
            try:
                result = func(*args, **kwargs)
                cache_instance.set(func.__name__, arg_dict, result, ttl)
                return result
            except Exception as e:
                cache_instance.stats["errors"] += 1
                raise
        
        # Add cache management methods to the function
        wrapper.cache = cache_instance
        wrapper.invalidate_cache = lambda **kwargs: cache_instance.invalidate(func.__name__, kwargs)
        wrapper.get_cache_stats = lambda: cache_instance.get_statistics()
        
        return wrapper
    return decorator

# Example cached tools
@cacheable(ttl=600)  # Cache for 10 minutes
def get_stock_price(symbol: str) -> Dict[str, Any]:
    """Get current stock price (expensive API call)"""
    import random
    import time
    
    # Simulate expensive API call
    time.sleep(1)
    
    return {
        "symbol": symbol,
        "price": round(100 + random.random() * 50, 2),
        "timestamp": time.time()
    }

@cacheable(ttl=3600)  # Cache for 1 hour
def analyze_market_data(timeframe: str, assets: List[str]) -> Dict[str, Any]:
    """Analyze market data (very expensive computation)"""
    import time
    
    # Simulate expensive computation
    time.sleep(3)
    
    return {
        "timeframe": timeframe,
        "assets": assets,
        "analysis": "Market showing bullish trends",
        "confidence": 0.85,
        "computed_at": time.time()
    }

# Usage examples
print("Stock price:", get_stock_price("AAPL"))  # Cache miss
print("Stock price again:", get_stock_price("AAPL"))  # Cache hit

# Check cache statistics
print("Cache stats:", get_stock_price.get_cache_stats())

# Invalidate specific cache entry
get_stock_price.invalidate_cache(symbol="AAPL")

Circuit Breaker Pattern

For highly reliable systems, implement circuit breakers to prevent cascade failures:

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# Circuit Breaker Pattern for Tool Reliability
from enum import Enum
import time
from typing import Callable, Any, Dict
from dataclasses import dataclass

class CircuitState(Enum):
    CLOSED = "closed"      # Normal operation
    OPEN = "open"          # Circuit breaker triggered
    HALF_OPEN = "half_open"  # Testing if service recovered

@dataclass
class CircuitBreakerConfig:
    failure_threshold: int = 5        # Failures before opening
    recovery_timeout: int = 60        # Seconds before trying half-open
    success_threshold: int = 3        # Successes in half-open to close
    timeout: float = 30.0            # Request timeout

class CircuitBreaker:
    """Circuit breaker for tool reliability"""
    
    def __init__(self, name: str, config: CircuitBreakerConfig):
        self.name = name
        self.config = config
        self.state = CircuitState.CLOSED
        self.failure_count = 0
        self.success_count = 0
        self.last_failure_time = 0
        self.last_request_time = 0
    
    def call(self, func: Callable, *args, **kwargs) -> Any:
        """Execute function with circuit breaker protection"""
        current_time = time.time()
        
        # Check if we should transition from OPEN to HALF_OPEN
        if (self.state == CircuitState.OPEN and 
            current_time - self.last_failure_time > self.config.recovery_timeout):
            self.state = CircuitState.HALF_OPEN
            self.success_count = 0
        
        # If circuit is OPEN, fail fast
        if self.state == CircuitState.OPEN:
            raise CircuitBreakerOpenError(
                f"Circuit breaker {self.name} is OPEN"
            )
        
        # Execute the function
        try:
            result = func(*args, **kwargs)
            self._record_success()
            return result
        
        except Exception as e:
            self._record_failure()
            raise
    
    def _record_success(self):
        """Record successful execution"""
        if self.state == CircuitState.HALF_OPEN:
            self.success_count += 1
            if self.success_count >= self.config.success_threshold:
                self.state = CircuitState.CLOSED
                self.failure_count = 0
        elif self.state == CircuitState.CLOSED:
            self.failure_count = 0
    
    def _record_failure(self):
        """Record failed execution"""
        self.failure_count += 1
        self.last_failure_time = time.time()
        
        if (self.state == CircuitState.CLOSED and 
            self.failure_count >= self.config.failure_threshold):
            self.state = CircuitState.OPEN
        elif self.state == CircuitState.HALF_OPEN:
            self.state = CircuitState.OPEN
    
    def get_state(self) -> Dict[str, Any]:
        """Get current circuit breaker state"""
        return {
            "name": self.name,
            "state": self.state.value,
            "failure_count": self.failure_count,
            "success_count": self.success_count,
            "last_failure_time": self.last_failure_time
        }

class CircuitBreakerOpenError(Exception):
    """Exception raised when circuit breaker is open"""
    pass

# Integration with tool registry
class CircuitBreakerToolRegistry:
    """Tool registry with circuit breaker protection"""
    
    def __init__(self):
        self.tools = {}
        self.circuit_breakers = {}
    
    def register(self, name: str, func: Callable, 
                circuit_config: CircuitBreakerConfig = None):
        """Register tool with optional circuit breaker"""
        self.tools[name] = func
        
        if circuit_config:
            self.circuit_breakers[name] = CircuitBreaker(name, circuit_config)
    
    def execute(self, name: str, *args, **kwargs) -> Any:
        """Execute tool with circuit breaker protection"""
        if name not in self.tools:
            raise ValueError(f"Tool {name} not found")
        
        func = self.tools[name]
        
        if name in self.circuit_breakers:
            # Execute with circuit breaker
            circuit_breaker = self.circuit_breakers[name]
            return circuit_breaker.call(func, *args, **kwargs)
        else:
            # Execute directly
            return func(*args, **kwargs)
    
    def get_circuit_status(self) -> Dict[str, Dict]:
        """Get status of all circuit breakers"""
        return {
            name: cb.get_state() 
            for name, cb in self.circuit_breakers.items()
        }

# Example usage
def unreliable_api_call(data: str) -> str:
    """Simulate an unreliable external API"""
    import random
    
    if random.random() < 0.4:  # 40% failure rate
        raise Exception("External API temporarily unavailable")
    
    return f"API processed: {data}"

# Set up circuit breaker registry
cb_registry = CircuitBreakerToolRegistry()
cb_registry.register(
    "unreliable_api",
    unreliable_api_call,
    CircuitBreakerConfig(
        failure_threshold=3,
        recovery_timeout=30,
        success_threshold=2
    )
)

# Test circuit breaker behavior
for i in range(10):
    try:
        result = cb_registry.execute("unreliable_api", f"test_data_{i}")
        print(f"Success: {result}")
    except CircuitBreakerOpenError as e:
        print(f"Circuit breaker open: {e}")
    except Exception as e:
        print(f"API error: {e}")
    
    # Check circuit breaker status
    status = cb_registry.get_circuit_status()
    print(f"Circuit status: {status['unreliable_api']['state']}\n")
    
    time.sleep(1)

Summary and Best Practices

Advanced Tool Integration Best Practices

1. Design for Reliability

   • Implement proper error handling and retries
   • Use circuit breakers for external dependencies
   • Monitor tool health and performance metrics

2. Ensure Security

   • Implement comprehensive access controls
   • Validate and sanitize all inputs thoroughly
   • Maintain detailed audit logs for compliance

3. Optimize Performance

   • Use intelligent caching for expensive operations
   • Implement parallel execution for independent tasks
   • Monitor and optimize based on usage patterns

4. Plan for Scale

   • Design tools to be stateless when possible
   • Implement connection pooling for external services
   • Use async patterns for I/O-bound operations

Production Deployment Checklist

• Security: All tools have proper permission checks
• Monitoring: Performance and error metrics are tracked
• Rate Limiting: Prevent abuse and resource exhaustion
• Caching: Expensive operations are cached appropriately
• Error Handling: Graceful degradation for tool failures
• Documentation: Clear tool usage and security guidelines
• Testing: Comprehensive integration and security tests

Next Steps

You now have the knowledge to build production-ready tool integration systems. In our next lesson, we'll explore the Model Context Protocol (MCP) and how it standardizes tool discovery and usage across different AI systems, enabling unprecedented interoperability and extensibility.

Practice Exercises

1. Workflow Builder: Create a visual workflow builder for complex tool chains
2. Security Audit: Implement a security audit system for tool usage
3. Performance Dashboard: Build a real-time dashboard for tool performance metrics
4. Circuit Breaker Implementation: Implement circuit breakers for a set of unreliable tools

Additional Resources

• Microservices Patterns - Relevant architectural patterns
• Site Reliability Engineering - Google's SRE practices
• OWASP API Security - Security best practices
• Distributed Systems Observability - Monitoring and debugging