AI Agent Orchestration: Coordinating Multiple AI Systems for Maximum Efficiency

class CentralizedOrchestrator:
    def __init__(self):
        self.agents = {}
        self.resource_pool = ResourceManager()
        self.task_queue = PriorityQueue()
        self.context_store = SharedContext()
    
    async def orchestrate_task(self, task):
        # 1. Analyze task requirements
        requirements = self.analyze_requirements(task)
        
        # 2. Select optimal agents
        selected_agents = self.select_agents(requirements)
        
        # 3. Allocate resources
        resources = await self.resource_pool.allocate(requirements)
        
        # 4. Coordinate execution
        results = await self.coordinate_execution(
            selected_agents, task, resources
        )
        
        # 5. Aggregate and return results
        return self.aggregate_results(results)
    
    def select_agents(self, requirements):
        # Advanced agent selection based on:
        # - Current load
        # - Specialization match
        # - Historical performance
        # - Resource availability
        
        candidates = []
        for agent_id, agent in self.agents.items():
            if self.can_handle_task(agent, requirements):
                score = self.calculate_fitness_score(
                    agent, requirements
                )
                candidates.append((score, agent_id, agent))
        
        # Return top N agents based on score
        return sorted(candidates, reverse=True)[:requirements.agent_count]
    

class DistributedAgent:
    def __init__(self, agent_id, peers):
        self.agent_id = agent_id
        self.peers = peers
        self.message_bus = MessageBus()
        self.consensus_engine = ConsensusEngine()
    
    async def initiate_collaboration(self, task):
        # 1. Broadcast task to relevant peers
        relevant_peers = self.find_relevant_peers(task)
        proposals = await self.broadcast_collaboration_request(
            task, relevant_peers
        )
        
        # 2. Reach consensus on task distribution
        task_allocation = await self.consensus_engine.reach_consensus(
            proposals, task
        )
        
        # 3. Execute distributed task
        results = await self.execute_distributed_task(task_allocation)
        
        # 4. Aggregate results
        return self.merge_results(results)
    
    async def handle_collaboration_request(self, task, initiator):
        # Evaluate capacity and capability
        if not self.can_contribute(task):
            return None
        
        # Calculate contribution proposal
        proposal = self.create_contribution_proposal(task)
        
        # Include resource requirements and expected output
        proposal.resources = self.estimate_resources(task)
        proposal.timeline = self.estimate_completion_time(task)
        proposal.confidence = self.calculate_confidence(task)
        
        return proposal
    

class TaskDecomposer:
    def __init__(self):
        self.capability_matrix = self.build_capability_matrix()
        self.dependency_graph = DependencyGraph()
        self.optimization_engine = OptimizationEngine()
    
    def decompose_task(self, complex_task):
        # 1. Break down task into atomic operations
        atomic_operations = self.identify_atomic_operations(complex_task)
        
        # 2. Build dependency graph
        dependencies = self.analyze_dependencies(atomic_operations)
        
        # 3. Optimize for parallel execution
        execution_plan = self.optimize_execution_plan(
            atomic_operations, dependencies
        )
        
        # 4. Assign operations to optimal agents
        assignments = self.assign_operations_to_agents(execution_plan)
        
        return TaskExecutionPlan(
            operations=atomic_operations,
            dependencies=dependencies,
            assignments=assignments,
            estimated_completion=self.estimate_completion_time(execution_plan)
        )
    
    def assign_operations_to_agents(self, execution_plan):
        assignments = {}
        
        for operation in execution_plan.operations:
            # Find agents capable of handling this operation
            capable_agents = self.find_capable_agents(operation)
            
            # Score agents based on multiple factors
            scored_agents = []
            for agent in capable_agents:
                score = self.calculate_assignment_score(agent, operation)
                scored_agents.append((score, agent))
            
            # Select best agent considering load balancing
            best_agent = self.select_best_agent(
                scored_agents, current_assignments=assignments
            )
            
            assignments[operation.id] = best_agent
        
        return assignments
    
    def calculate_assignment_score(self, agent, operation):
        # Multi-factor scoring algorithm
        capability_score = agent.capability_match(operation) * 0.3
        load_score = (1 - agent.current_load) * 0.25
        performance_score = agent.historical_performance(operation.type) * 0.25
        availability_score = agent.availability_window_match(operation) * 0.2
        
        return capability_score + load_score + performance_score + availability_score
    

class DynamicLoadBalancer:
    def __init__(self):
        self.agents = {}
        self.performance_monitor = PerformanceMonitor()
        self.rebalancing_threshold = 0.8  # 80% utilization
        self.metrics_window = 300  # 5-minute sliding window
    
    async def monitor_and_rebalance(self):
        while True:
            # Collect current metrics
            metrics = await self.collect_agent_metrics()
            
            # Identify overloaded and underutilized agents
            overloaded = self.identify_overloaded_agents(metrics)
            underutilized = self.identify_underutilized_agents(metrics)
            
            if overloaded and underutilized:
                await self.rebalance_load(overloaded, underutilized)
            
            # Wait before next check
            await asyncio.sleep(30)  # Check every 30 seconds
    
    async def rebalance_load(self, overloaded_agents, underutilized_agents):
        for overloaded_agent in overloaded_agents:
            # Get pending tasks from overloaded agent
            pending_tasks = await overloaded_agent.get_pending_tasks()
            
            # Sort tasks by migration cost and priority
            migratable_tasks = self.sort_tasks_for_migration(pending_tasks)
            
            for task in migratable_tasks:
                # Find best target agent
                target_agent = self.find_best_migration_target(
                    task, underutilized_agents
                )
                
                if target_agent and self.should_migrate(task, target_agent):
                    await self.migrate_task(
                        task, overloaded_agent, target_agent
                    )
                    
                    # Update utilization tracking
                    self.update_utilization_metrics(
                        overloaded_agent, target_agent, task
                    )
                    
                    # Stop if overload resolved
                    if overloaded_agent.utilization < self.rebalancing_threshold:
                        break
    
    def calculate_migration_cost(self, task, source_agent, target_agent):
        # Consider multiple factors for migration cost
        context_transfer_cost = task.context_size * 0.1
        setup_cost = target_agent.setup_time(task.type) * 0.2
        network_cost = self.estimate_network_latency(source_agent, target_agent)
        learning_cost = (1 - target_agent.familiarity(task.type)) * 0.3
        
        return context_transfer_cost + setup_cost + network_cost + learning_cost
    

class DistributedContextCache:
    def __init__(self):
        self.cache_nodes = {}
        self.consistency_manager = ConsistencyManager()
        self.eviction_policy = LRUWithSemanticSimilarity()
        self.context_index = VectorSearchIndex()
    
    async def get_relevant_context(self, task, agent_id):
        # 1. Generate semantic fingerprint for task
        task_embedding = await self.generate_task_embedding(task)
        
        # 2. Search for semantically similar cached contexts
        similar_contexts = await self.context_index.search(
            task_embedding, threshold=0.85, limit=10
        )
        
        # 3. Validate context freshness and relevance
        valid_contexts = []
        for context in similar_contexts:
            if self.is_context_valid(context, task):
                valid_contexts.append(context)
        
        # 4. Merge and optimize contexts
        merged_context = self.merge_contexts(valid_contexts)
        
        # 5. Update access patterns for cache optimization
        await self.update_access_patterns(agent_id, merged_context)
        
        return merged_context
    
    async def cache_context(self, context, task, agent_id):
        # Generate semantic embedding for the context
        context_embedding = await self.generate_context_embedding(context)
        
        # Determine optimal cache placement
        optimal_nodes = self.determine_cache_placement(
            context, agent_id, access_patterns=self.get_access_patterns(agent_id)
        )
        
        # Store context with metadata
        cache_entry = CacheEntry(
            context=context,
            embedding=context_embedding,
            created_by=agent_id,
            created_at=datetime.utcnow(),
            access_count=1,
            task_similarity_threshold=0.8
        )
        
        # Replicate to selected nodes
        await self.replicate_to_nodes(cache_entry, optimal_nodes)
        
        # Update index
        await self.context_index.add(cache_entry)
    
    def determine_cache_placement(self, context, agent_id, access_patterns):
        # Advanced placement algorithm considering:
        # - Network topology
        # - Agent collaboration patterns
        # - Historical access patterns
        # - Resource availability
        
        placement_scores = {}
        
        for node_id, node in self.cache_nodes.items():
            # Network proximity score
            network_score = 1.0 / (1.0 + self.get_network_distance(agent_id, node_id))
            
            # Collaboration pattern score
            collaboration_score = self.calculate_collaboration_score(
                agent_id, node.frequent_agents
            )
            
            # Resource availability score
            resource_score = 1.0 - node.utilization
            
            # Historical pattern score
            pattern_score = self.calculate_pattern_score(
                context, node.cached_contexts
            )
            
            placement_scores[node_id] = (
                network_score * 0.3 +
                collaboration_score * 0.3 +
                resource_score * 0.2 +
                pattern_score * 0.2
            )
        
        # Return top N nodes
        return sorted(placement_scores.items(), key=lambda x: x[1], reverse=True)[:3]