Agent Mesh Coordinator

class WorkStealingProtocol:    def __init__(self):        self.local_queue = TaskQueue()        self.peer_connections = PeerNetwork()        def steal_work(self):        if self.local_queue.is_empty():            # Find overloaded peers            candidates = self.find_busy_peers()            for peer in candidates:                stolen_task = peer.request_task()                if stolen_task:                    self.local_queue.add(stolen_task)                    break        def distribute_work(self, task):        if self.is_overloaded():            # Find underutilized peers            target_peer = self.find_available_peer()            if target_peer:                target_peer.assign_task(task)                return        self.local_queue.add(task)

class TaskDistributionDHT:    def route_task(self, task):        # Hash task ID to determine responsible node        hash_value = consistent_hash(task.id)        responsible_node = self.find_node_by_hash(hash_value)                if responsible_node == self:            self.execute_task(task)        else:            responsible_node.forward_task(task)        def replicate_task(self, task, replication_factor=3):        # Store copies on multiple nodes for fault tolerance        successor_nodes = self.get_successors(replication_factor)        for node in successor_nodes:            node.store_task_copy(task)

class TaskAuction:    def conduct_auction(self, task):        # Broadcast task to all peers        bids = self.broadcast_task_request(task)                # Evaluate bids based on:        evaluated_bids = []        for bid in bids:            score = self.evaluate_bid(bid, criteria={                'capability_match': 0.4,                'current_load': 0.3,                 'past_performance': 0.2,                'resource_availability': 0.1            })            evaluated_bids.append((bid, score))                # Award to highest scorer        winner = max(evaluated_bids, key=lambda x: x[1])        return self.award_task(task, winner[0])

class HeartbeatMonitor:    def __init__(self, timeout=10, interval=3):        self.peers = {}        self.timeout = timeout        self.interval = interval            def monitor_peer(self, peer_id):        last_heartbeat = self.peers.get(peer_id, 0)        if time.time() - last_heartbeat > self.timeout:            self.trigger_failure_detection(peer_id)        def trigger_failure_detection(self, peer_id):        # Initiate failure confirmation protocol        confirmations = self.request_failure_confirmations(peer_id)        if len(confirmations) >= self.quorum_size():            self.handle_peer_failure(peer_id)

class PartitionHandler:    def detect_partition(self):        reachable_peers = self.ping_all_peers()        total_peers = len(self.known_peers)                if len(reachable_peers) < total_peers * 0.5:            return self.handle_potential_partition()            def handle_potential_partition(self):        # Use quorum-based decisions        if self.has_majority_quorum():            return "continue_operations"        else:            return "enter_read_only_mode"

class LoadBalancer:    def balance_load(self):        # Collect load metrics from all peers        peer_loads = self.collect_load_metrics()                # Identify overloaded and underutilized nodes        overloaded = [p for p in peer_loads if p.cpu_usage > 0.8]        underutilized = [p for p in peer_loads if p.cpu_usage < 0.3]                # Migrate tasks from hot to cold nodes        for hot_node in overloaded:            for cold_node in underutilized:                if self.can_migrate_task(hot_node, cold_node):                    self.migrate_task(hot_node, cold_node)

class CapabilityRouter:    def route_by_capability(self, task):        required_caps = task.required_capabilities                # Find peers with matching capabilities        capable_peers = []        for peer in self.peers:            capability_match = self.calculate_match_score(                peer.capabilities, required_caps            )            if capability_match > 0.7:  # 70% match threshold                capable_peers.append((peer, capability_match))                # Route to best match with available capacity        return self.select_optimal_peer(capable_peers)