Agentic AI: Building Autonomous Multi-Agent Systems for Enterprise Automation

# Autonomous Agent Framework with Reasoning and Planning
from typing import Dict, List, Optional, Any, Callable
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
import asyncio
import json
import uuid
from abc import ABC, abstractmethod

class TaskStatus(Enum):
    PENDING = "pending"
    IN_PROGRESS = "in_progress"
    COMPLETED = "completed"
    FAILED = "failed"
    CANCELLED = "cancelled"

class AgentCapability(Enum):
    REASONING = "reasoning"
    PLANNING = "planning"
    EXECUTION = "execution"
    COMMUNICATION = "communication"
    LEARNING = "learning"

@dataclass
class Task:
    id: str = field(default_factory=lambda: str(uuid.uuid4()))
    description: str = ""
    objective: str = ""
    priority: int = 5  # 1-10, higher = more urgent
    status: TaskStatus = TaskStatus.PENDING
    assigned_agent: Optional[str] = None
    created_at: datetime = field(default_factory=datetime.utcnow)
    updated_at: datetime = field(default_factory=datetime.utcnow)
    dependencies: List[str] = field(default_factory=list)
    context: Dict[str, Any] = field(default_factory=dict)
    result: Optional[Dict[str, Any]] = None
    error: Optional[str] = None

@dataclass
class AgentMemory:
    """Persistent memory for agent learning and context"""
    experiences: List[Dict[str, Any]] = field(default_factory=list)
    skills: Dict[str, float] = field(default_factory=dict)  # skill -> proficiency
    context: Dict[str, Any] = field(default_factory=dict)
    relationships: Dict[str, float] = field(default_factory=dict)  # agent_id -> trust_score

class Agent(ABC):
    """Base autonomous agent with reasoning, planning, and execution capabilities"""
    
    def __init__(self, agent_id: str, name: str, capabilities: List[AgentCapability]):
        self.agent_id = agent_id
        self.name = name
        self.capabilities = capabilities
        self.memory = AgentMemory()
        self.task_queue: List[Task] = []
        self.current_task: Optional[Task] = None
        self.is_active = True
        self.performance_metrics = {
            'tasks_completed': 0,
            'success_rate': 0.0,
            'avg_completion_time': 0.0,
            'learning_rate': 0.0
        }
        
    async def process_tasks(self):
        """Main agent processing loop"""
        while self.is_active:
            if not self.current_task and self.task_queue:
                # Select next task based on priority and dependencies
                self.current_task = await self.select_next_task()
                
            if self.current_task:
                try:
                    await self.execute_task(self.current_task)
                    self.current_task = None
                except Exception as e:
                    await self.handle_task_failure(self.current_task, str(e))
                    
            await asyncio.sleep(0.1)  # Prevent busy waiting
    
    async def select_next_task(self) -> Optional[Task]:
        """Intelligent task selection based on priority, dependencies, and capabilities"""
        
        # Filter tasks that can be executed (dependencies met)
        executable_tasks = []
        for task in self.task_queue:
            if task.status == TaskStatus.PENDING:
                dependencies_met = all(
                    self.is_task_completed(dep_id) for dep_id in task.dependencies
                )
                if dependencies_met and self.can_handle_task(task):
                    executable_tasks.append(task)
        
        if not executable_tasks:
            return None
            
        # Score tasks based on priority, urgency, and agent capabilities
        scored_tasks = []
        for task in executable_tasks:
            score = self.calculate_task_score(task)
            scored_tasks.append((task, score))
            
        # Select highest scoring task
        scored_tasks.sort(key=lambda x: x[1], reverse=True)
        selected_task = scored_tasks[0][0]
        
        # Remove from queue
        self.task_queue.remove(selected_task)
        selected_task.status = TaskStatus.IN_PROGRESS
        selected_task.assigned_agent = self.agent_id
        
        return selected_task
    
    def calculate_task_score(self, task: Task) -> float:
        """Calculate task priority score based on multiple factors"""
        
        base_score = task.priority
        
        # Urgency factor (time-sensitive tasks get higher score)
        time_since_creation = (datetime.utcnow() - task.created_at).total_seconds()
        urgency_factor = min(2.0, 1.0 + (time_since_creation / 3600))  # Max 2x after 1 hour
        
        # Capability match factor (higher score for tasks matching agent skills)
        capability_match = self.assess_capability_match(task)
        
        # Learning opportunity factor (slight boost for tasks that enhance skills)
        learning_factor = 1.0 + (0.1 if self.is_learning_opportunity(task) else 0.0)
        
        return base_score * urgency_factor * capability_match * learning_factor
    
    @abstractmethod
    async def execute_task(self, task: Task):
        """Execute a specific task - implemented by concrete agent classes"""
        pass
        
    @abstractmethod
    def can_handle_task(self, task: Task) -> bool:
        """Check if agent can handle this type of task"""
        pass
        
    async def reason_about_task(self, task: Task) -> Dict[str, Any]:
        """Reasoning phase - understand the task and context"""
        
        reasoning_result = {
            'task_analysis': self.analyze_task_requirements(task),
            'context_assessment': self.assess_context(task.context),
            'risk_factors': self.identify_risks(task),
            'success_probability': self.estimate_success_probability(task),
            'resource_requirements': self.estimate_resources_needed(task)
        }
        
        # Store reasoning in memory for future reference
        self.memory.experiences.append({
            'type': 'reasoning',
            'task_id': task.id,
            'timestamp': datetime.utcnow(),
            'result': reasoning_result
        })
        
        return reasoning_result
    
    async def plan_execution(self, task: Task, reasoning: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Planning phase - create step-by-step execution plan"""
        
        # Break down task into executable steps
        steps = self.decompose_task(task, reasoning)
        
        # Optimize step order and identify parallelizable steps
        optimized_plan = self.optimize_execution_plan(steps)
        
        # Add contingency plans for high-risk steps
        plan_with_fallbacks = self.add_contingency_plans(optimized_plan, reasoning['risk_factors'])
        
        return plan_with_fallbacks
    
    def analyze_task_requirements(self, task: Task) -> Dict[str, Any]:
        """Analyze what the task requires for successful completion"""
        
        # This would use NLP/ML to understand task requirements
        # Simplified implementation for demonstration
        
        return {
            'complexity': self.assess_complexity(task.description),
            'required_capabilities': self.identify_required_capabilities(task.description),
            'input_data_needed': self.identify_input_requirements(task.context),
            'output_expectations': self.identify_output_requirements(task.objective)
        }
    
    def assess_context(self, context: Dict[str, Any]) -> Dict[str, Any]:
        """Assess the context and environment for task execution"""
        
        return {
            'available_resources': context.get('resources', {}),
            'constraints': context.get('constraints', []),
            'environment_state': context.get('environment', {}),
            'related_tasks': context.get('related_tasks', [])
        }
    
    def identify_risks(self, task: Task) -> List[Dict[str, Any]]:
        """Identify potential risks and failure modes"""
        
        risks = []
        
        # Complexity risk
        if self.assess_complexity(task.description) > 0.7:
            risks.append({
                'type': 'complexity',
                'severity': 0.6,
                'description': 'Task complexity may lead to execution errors',
                'mitigation': 'Break into smaller sub-tasks'
            })
        
        # Resource availability risk
        required_resources = self.estimate_resources_needed(task)
        if required_resources.get('estimated_time', 0) > 3600:  # > 1 hour
            risks.append({
                'type': 'time_constraint',
                'severity': 0.4,
                'description': 'Long execution time may cause timeouts',
                'mitigation': 'Implement checkpointing and progress tracking'
            })
        
        return risks
    
    def estimate_success_probability(self, task: Task) -> float:
        """Estimate probability of successful task completion"""
        
        # Base probability from past experience with similar tasks
        base_probability = 0.8
        
        # Adjust based on complexity
        complexity = self.assess_complexity(task.description)
        complexity_adjustment = 1.0 - (complexity * 0.3)
        
        # Adjust based on agent experience
        experience_adjustment = min(1.2, 1.0 + (self.performance_metrics['success_rate'] * 0.2))
        
        return min(1.0, base_probability * complexity_adjustment * experience_adjustment)
    
    async def learn_from_experience(self, task: Task, success: bool, execution_time: float):
        """Learn from task execution to improve future performance"""
        
        # Update performance metrics
        self.performance_metrics['tasks_completed'] += 1
        
        if success:
            # Update success rate (exponential moving average)
            alpha = 0.1  # Learning rate
            old_success_rate = self.performance_metrics['success_rate']
            self.performance_metrics['success_rate'] = (
                (1 - alpha) * old_success_rate + alpha * 1.0
            )
        else:
            # Decrease success rate
            alpha = 0.05
            old_success_rate = self.performance_metrics['success_rate']
            self.performance_metrics['success_rate'] = (
                (1 - alpha) * old_success_rate + alpha * 0.0
            )
        
        # Update skill proficiency based on task type and outcome
        required_skills = self.identify_required_capabilities(task.description)
        for skill in required_skills:
            current_proficiency = self.memory.skills.get(skill, 0.5)
            
            if success:
                # Improve skill (with diminishing returns)
                improvement = 0.05 * (1.0 - current_proficiency)
                self.memory.skills[skill] = min(1.0, current_proficiency + improvement)
            else:
                # Slight degradation to encourage learning
                degradation = 0.01
                self.memory.skills[skill] = max(0.1, current_proficiency - degradation)
        
        # Store experience for future reference
        experience = {
            'task_id': task.id,
            'task_type': task.context.get('type', 'unknown'),
            'success': success,
            'execution_time': execution_time,
            'complexity': self.assess_complexity(task.description),
            'timestamp': datetime.utcnow(),
            'lessons_learned': self.extract_lessons(task, success)
        }
        
        self.memory.experiences.append(experience)
        
        # Keep only recent experiences to prevent memory bloat
        if len(self.memory.experiences) > 1000:
            self.memory.experiences = self.memory.experiences[-800:]

class SpecializedAgent(Agent):
    """Concrete implementation of a specialized agent"""
    
    def __init__(self, agent_id: str, name: str, specialization: str):
        capabilities = [AgentCapability.REASONING, AgentCapability.PLANNING, AgentCapability.EXECUTION]
        super().__init__(agent_id, name, capabilities)
        self.specialization = specialization
    
    async def execute_task(self, task: Task):
        """Execute task with specialization-specific logic"""
        
        # Reasoning phase
        reasoning = await self.reason_about_task(task)
        
        # Planning phase  
        execution_plan = await self.plan_execution(task, reasoning)
        
        # Execution phase
        start_time = datetime.utcnow()
        success = False
        
        try:
            # Execute each step in the plan
            for step in execution_plan:
                await self.execute_step(step, task)
            
            task.status = TaskStatus.COMPLETED
            task.result = {'completed_at': datetime.utcnow(), 'success': True}
            success = True
            
        except Exception as e:
            task.status = TaskStatus.FAILED
            task.error = str(e)
            task.result = {'failed_at': datetime.utcnow(), 'error': str(e)}
            
        finally:
            execution_time = (datetime.utcnow() - start_time).total_seconds()
            await self.learn_from_experience(task, success, execution_time)
    
    def can_handle_task(self, task: Task) -> bool:
        """Check if agent can handle task based on specialization"""
        
        task_type = task.context.get('type', '')
        return self.specialization in task_type.lower() or self.specialization == 'general'
    
    async def execute_step(self, step: Dict[str, Any], task: Task):
        """Execute individual step within a task"""
        
        step_type = step.get('type', '')
        
        if step_type == 'api_call':
            await self.execute_api_call(step, task)
        elif step_type == 'data_processing':
            await self.execute_data_processing(step, task)
        elif step_type == 'analysis':
            await self.execute_analysis(step, task)
        else:
            # Generic step execution
            await self.execute_generic_step(step, task)

# Multi-Agent Orchestration System
from typing import Dict, List, Set, Optional
import asyncio
from collections import defaultdict
from dataclasses import dataclass
import networkx as nx

@dataclass
class AgentMessage:
    sender_id: str
    receiver_id: str
    message_type: str
    content: Dict[str, Any]
    timestamp: datetime = field(default_factory=datetime.utcnow)
    priority: int = 5
    requires_response: bool = False

class CommunicationProtocol:
    """Standardized communication protocol for agent interactions"""
    
    MESSAGE_TYPES = {
        'TASK_REQUEST': 'task_request',
        'TASK_RESPONSE': 'task_response', 
        'STATUS_UPDATE': 'status_update',
        'RESOURCE_REQUEST': 'resource_request',
        'COLLABORATION_INVITE': 'collaboration_invite',
        'KNOWLEDGE_SHARE': 'knowledge_share',
        'CONFLICT_RESOLUTION': 'conflict_resolution'
    }

class MultiAgentOrchestrator:
    """Orchestrates multiple agents for complex workflow execution"""
    
    def __init__(self):
        self.agents: Dict[str, Agent] = {}
        self.message_queue: List[AgentMessage] = []
        self.task_graph = nx.DiGraph()  # Task dependency graph
        self.agent_capabilities: Dict[str, Set[str]] = defaultdict(set)
        self.resource_manager = ResourceManager()
        self.conflict_resolver = ConflictResolver()
        
    async def add_agent(self, agent: Agent):
        """Add agent to orchestration system"""
        
        self.agents[agent.agent_id] = agent
        
        # Register agent capabilities
        for capability in agent.capabilities:
            self.agent_capabilities[capability.value].add(agent.agent_id)
        
        # Start agent processing
        asyncio.create_task(agent.process_tasks())
    
    async def execute_workflow(self, workflow_definition: Dict[str, Any]) -> Dict[str, Any]:
        """Execute complex workflow using multiple coordinated agents"""
        
        # Parse workflow into task graph
        task_graph = self.parse_workflow_definition(workflow_definition)
        
        # Analyze workflow requirements
        workflow_analysis = await self.analyze_workflow(task_graph)
        
        # Create agent team based on requirements
        agent_team = await self.assemble_agent_team(workflow_analysis)
        
        # Distribute tasks to agents
        task_assignments = await self.distribute_tasks(task_graph, agent_team)
        
        # Monitor and coordinate execution
        execution_result = await self.coordinate_execution(task_assignments, task_graph)
        
        return execution_result
    
    async def analyze_workflow(self, task_graph: nx.DiGraph) -> Dict[str, Any]:
        """Analyze workflow requirements and complexity"""
        
        analysis = {
            'total_tasks': len(task_graph.nodes),
            'parallel_branches': self.count_parallel_branches(task_graph),
            'critical_path_length': self.calculate_critical_path(task_graph),
            'required_capabilities': self.extract_required_capabilities(task_graph),
            'resource_requirements': self.estimate_workflow_resources(task_graph),
            'estimated_completion_time': self.estimate_completion_time(task_graph)
        }
        
        return analysis
    
    async def assemble_agent_team(self, workflow_analysis: Dict[str, Any]) -> List[str]:
        """Select optimal team of agents for workflow execution"""
        
        required_capabilities = workflow_analysis['required_capabilities']
        team = []
        
        # For each required capability, select the best available agent
        for capability in required_capabilities:
            available_agents = self.agent_capabilities.get(capability, set())
            
            if available_agents:
                # Score agents based on performance and availability
                agent_scores = {}
                for agent_id in available_agents:
                    agent = self.agents[agent_id]
                    score = self.calculate_agent_score(agent, capability)
                    agent_scores[agent_id] = score
                
                # Select highest scoring agent
                best_agent = max(agent_scores.items(), key=lambda x: x[1])[0]
                if best_agent not in team:
                    team.append(best_agent)
        
        # Add coordinator agent for complex workflows
        if workflow_analysis['total_tasks'] > 10:
            coordinator = self.select_coordinator_agent()
            if coordinator and coordinator not in team:
                team.append(coordinator)
        
        return team
    
    async def distribute_tasks(self, task_graph: nx.DiGraph, 
                             agent_team: List[str]) -> Dict[str, List[Task]]:
        """Distribute tasks optimally across agent team"""
        
        assignments = defaultdict(list)
        
        # Topological sort to respect dependencies
        execution_order = list(nx.topological_sort(task_graph))
        
        for task_id in execution_order:
            task_data = task_graph.nodes[task_id]
            task = Task(
                id=task_id,
                description=task_data['description'],
                objective=task_data['objective'],
                priority=task_data.get('priority', 5),
                context=task_data.get('context', {}),
                dependencies=list(task_graph.predecessors(task_id))
            )
            
            # Find best agent for this task
            best_agent = await self.select_agent_for_task(task, agent_team)
            assignments[best_agent].append(task)
        
        return assignments
    
    async def coordinate_execution(self, assignments: Dict[str, List[Task]],
                                 task_graph: nx.DiGraph) -> Dict[str, Any]:
        """Coordinate task execution across multiple agents"""
        
        # Assign tasks to agents
        for agent_id, tasks in assignments.items():
            agent = self.agents[agent_id]
            agent.task_queue.extend(tasks)
        
        # Monitor execution progress
        start_time = datetime.utcnow()
        completed_tasks = set()
        failed_tasks = set()
        
        while len(completed_tasks) + len(failed_tasks) < len(task_graph.nodes):
            # Check task statuses
            for agent_id, agent in self.agents.items():
                for task in agent.task_queue + ([agent.current_task] if agent.current_task else []):
                    if task.status == TaskStatus.COMPLETED:
                        completed_tasks.add(task.id)
                        # Notify dependent tasks
                        await self.notify_task_completion(task, task_graph)
                    elif task.status == TaskStatus.FAILED:
                        failed_tasks.add(task.id)
                        # Handle task failure
                        await self.handle_task_failure_in_workflow(task, task_graph)
            
            # Process inter-agent messages
            await self.process_agent_communications()
            
            # Check for conflicts and resolve them
            conflicts = await self.detect_conflicts()
            for conflict in conflicts:
                await self.conflict_resolver.resolve_conflict(conflict)
            
            await asyncio.sleep(1)  # Check every second
        
        execution_time = (datetime.utcnow() - start_time).total_seconds()
        
        return {
            'total_tasks': len(task_graph.nodes),
            'completed_tasks': len(completed_tasks),
            'failed_tasks': len(failed_tasks),
            'success_rate': len(completed_tasks) / len(task_graph.nodes),
            'execution_time_seconds': execution_time,
            'agent_contributions': self.calculate_agent_contributions(assignments)
        }
    
    async def process_agent_communications(self):
        """Process messages between agents"""
        
        messages_to_process = self.message_queue[:]
        self.message_queue.clear()
        
        for message in messages_to_process:
            receiver = self.agents.get(message.receiver_id)
            if receiver:
                await self.deliver_message(message, receiver)
    
    async def deliver_message(self, message: AgentMessage, receiver: Agent):
        """Deliver message to target agent"""
        
        if message.message_type == CommunicationProtocol.MESSAGE_TYPES['TASK_REQUEST']:
            # Another agent is requesting help with a task
            await self.handle_task_request(message, receiver)
            
        elif message.message_type == CommunicationProtocol.MESSAGE_TYPES['KNOWLEDGE_SHARE']:
            # Agent is sharing knowledge/experience
            await self.handle_knowledge_share(message, receiver)
            
        elif message.message_type == CommunicationProtocol.MESSAGE_TYPES['RESOURCE_REQUEST']:
            # Agent needs additional resources
            await self.handle_resource_request(message, receiver)
    
    async def handle_collaboration_request(self, requester: Agent, task: Task, 
                                         required_capability: str) -> Optional[Agent]:
        """Handle request for agent collaboration"""
        
        # Find agents with required capability
        capable_agents = [
            self.agents[agent_id] 
            for agent_id in self.agent_capabilities.get(required_capability, [])
            if agent_id != requester.agent_id
        ]
        
        if not capable_agents:
            return None
        
        # Score potential collaborators
        collaborator_scores = {}
        for agent in capable_agents:
            score = (
                agent.performance_metrics['success_rate'] * 0.4 +
                agent.memory.relationships.get(requester.agent_id, 0.5) * 0.3 +
                (1.0 / (len(agent.task_queue) + 1)) * 0.3  # Availability
            )
            collaborator_scores[agent] = score
        
        # Select best collaborator
        best_collaborator = max(collaborator_scores.items(), key=lambda x: x[1])[0]
        
        # Send collaboration invite
        message = AgentMessage(
            sender_id=requester.agent_id,
            receiver_id=best_collaborator.agent_id,
            message_type=CommunicationProtocol.MESSAGE_TYPES['COLLABORATION_INVITE'],
            content={
                'task': task.__dict__,
                'required_capability': required_capability,
                'collaboration_type': 'assistance'
            },
            requires_response=True
        )
        
        self.message_queue.append(message)
        return best_collaborator

class ResourceManager:
    """Manages shared resources across agents"""
    
    def __init__(self):
        self.resources = {
            'compute_units': 100,
            'memory_mb': 8192,
            'api_calls_remaining': 10000,
            'storage_mb': 5120
        }
        self.allocations: Dict[str, Dict[str, int]] = defaultdict(dict)
        self.lock = asyncio.Lock()
    
    async def request_resources(self, agent_id: str, 
                              requested_resources: Dict[str, int]) -> bool:
        """Request resource allocation for agent"""
        
        async with self.lock:
            # Check if resources are available
            for resource, amount in requested_resources.items():
                available = self.resources.get(resource, 0)
                allocated = sum(
                    allocations.get(resource, 0) 
                    for allocations in self.allocations.values()
                )
                
                if available - allocated < amount:
                    return False  # Insufficient resources
            
            # Allocate resources
            for resource, amount in requested_resources.items():
                self.allocations[agent_id][resource] = amount
            
            return True
    
    async def release_resources(self, agent_id: str):
        """Release all resources allocated to agent"""
        
        async with self.lock:
            if agent_id in self.allocations:
                del self.allocations[agent_id]

class ConflictResolver:
    """Resolves conflicts between agents"""
    
    async def detect_resource_conflicts(self, agents: Dict[str, Agent]) -> List[Dict[str, Any]]:
        """Detect resource conflicts between agents"""
        
        conflicts = []
        
        # Check for resource contention
        resource_requests = {}
        for agent_id, agent in agents.items():
            if agent.current_task:
                required_resources = agent.estimate_resources_needed(agent.current_task)
                resource_requests[agent_id] = required_resources
        
        # Identify conflicts (simplified logic)
        for agent1_id, resources1 in resource_requests.items():
            for agent2_id, resources2 in resource_requests.items():
                if agent1_id != agent2_id:
                    conflict = self.check_resource_overlap(resources1, resources2)
                    if conflict:
                        conflicts.append({
                            'type': 'resource_conflict',
                            'agents': [agent1_id, agent2_id],
                            'resource': conflict,
                            'severity': 'medium'
                        })
        
        return conflicts
    
    async def resolve_conflict(self, conflict: Dict[str, Any]):
        """Resolve detected conflict between agents"""
        
        if conflict['type'] == 'resource_conflict':
            await self.resolve_resource_conflict(conflict)
        elif conflict['type'] == 'task_dependency':
            await self.resolve_dependency_conflict(conflict)
    
    async def resolve_resource_conflict(self, conflict: Dict[str, Any]):
        """Resolve resource conflicts using priority and negotiation"""
        
        # Implementation would use priority-based allocation,
        # time-sharing, or agent negotiation protocols
        pass