""" Advanced Agentic System ---------------------- A sophisticated multi-agent system with: Core Components: 1. Agent Management 2. Task Execution 3. Learning & Adaptation 4. Communication 5. Resource Management Advanced Features: 1. Self-Improvement 2. Multi-Agent Coordination 3. Dynamic Role Assignment 4. Emergent Behavior """ import logging from typing import Dict, Any, List, Optional, Union, TypeVar from dataclasses import dataclass, field from enum import Enum import json import asyncio from datetime import datetime import uuid from concurrent.futures import ThreadPoolExecutor import numpy as np from collections import defaultdict from orchestrator import ( AgentOrchestrator, AgentRole, AgentState, TaskPriority, Task ) from reasoning import UnifiedReasoningEngine as ReasoningEngine, StrategyType as ReasoningMode from reasoning.meta_learning import MetaLearningStrategy class AgentCapability(Enum): """Core capabilities of agents.""" REASONING = "reasoning" LEARNING = "learning" EXECUTION = "execution" COORDINATION = "coordination" MONITORING = "monitoring" class AgentPersonality(Enum): """Different personality types for agents.""" ANALYTICAL = "analytical" CREATIVE = "creative" CAUTIOUS = "cautious" PROACTIVE = "proactive" ADAPTIVE = "adaptive" @dataclass class AgentProfile: """Profile defining an agent's characteristics.""" id: str name: str role: AgentRole capabilities: List[AgentCapability] personality: AgentPersonality expertise_areas: List[str] learning_rate: float risk_tolerance: float created_at: datetime metadata: Dict[str, Any] class Agent: """Advanced autonomous agent with learning capabilities.""" def __init__( self, profile: AgentProfile, reasoning_engine: ReasoningEngine, meta_learning: MetaLearningStrategy, config: Dict[str, Any] = None ): self.profile = profile self.reasoning_engine = reasoning_engine self.meta_learning = meta_learning self.config = config or {} # State management self.state = AgentState.IDLE self.current_task: Optional[Task] = None self.task_history: List[Task] = [] # Learning and adaptation self.knowledge_base: Dict[str, Any] = {} self.learned_patterns: List[Dict[str, Any]] = [] self.adaptation_history: List[Dict[str, Any]] = [] # Performance metrics self.metrics: Dict[str, List[float]] = defaultdict(list) self.performance_history: List[Dict[str, float]] = [] # Communication self.message_queue = asyncio.Queue() self.response_queue = asyncio.Queue() # Resource management self.resource_usage: Dict[str, float] = {} self.resource_limits: Dict[str, float] = {} # Async support self.executor = ThreadPoolExecutor(max_workers=2) self.lock = asyncio.Lock() # Logging self.logger = logging.getLogger(f"Agent-{profile.id}") # Initialize components self._init_components() def _init_components(self): """Initialize agent components.""" # Set up knowledge base self.knowledge_base = { "expertise": {area: 0.5 for area in self.profile.expertise_areas}, "learned_skills": set(), "interaction_patterns": defaultdict(int), "success_patterns": defaultdict(float) } # Set up resource limits self.resource_limits = { "cpu": 1.0, "memory": 1000, "api_calls": 100, "learning_capacity": 0.8 } async def process_task(self, task: Task) -> Dict[str, Any]: """Process an assigned task.""" try: self.current_task = task self.state = AgentState.BUSY # Analyze task analysis = await self._analyze_task(task) # Plan execution plan = await self._plan_execution(analysis) # Execute plan result = await self._execute_plan(plan) # Learn from execution await self._learn_from_execution(task, result) # Update metrics self._update_metrics(task, result) return { "success": True, "task_id": task.id, "result": result, "metrics": self._get_execution_metrics() } except Exception as e: self.logger.error(f"Error processing task: {e}") self.state = AgentState.ERROR return { "success": False, "task_id": task.id, "error": str(e) } finally: self.state = AgentState.IDLE self.current_task = None async def _analyze_task(self, task: Task) -> Dict[str, Any]: """Analyze task requirements and constraints.""" # Use reasoning engine for analysis analysis = await self.reasoning_engine.reason( query=task.description, context={ "agent_profile": self.profile.__dict__, "task_history": self.task_history, "knowledge_base": self.knowledge_base }, mode=ReasoningMode.ANALYTICAL ) return { "requirements": analysis.get("requirements", []), "constraints": analysis.get("constraints", []), "complexity": analysis.get("complexity", 0.5), "estimated_duration": analysis.get("estimated_duration", 3600), "required_capabilities": analysis.get("required_capabilities", []) } async def _plan_execution(self, analysis: Dict[str, Any]) -> List[Dict[str, Any]]: """Plan task execution based on analysis.""" # Use reasoning engine for planning plan = await self.reasoning_engine.reason( query="Plan execution steps", context={ "analysis": analysis, "agent_capabilities": self.profile.capabilities, "resource_limits": self.resource_limits }, mode=ReasoningMode.FOCUSED ) return plan.get("steps", []) async def _execute_plan(self, plan: List[Dict[str, Any]]) -> Dict[str, Any]: """Execute the planned steps.""" results = [] for step in plan: try: # Check resources if not self._check_resources(step): raise RuntimeError("Insufficient resources for step execution") # Execute step step_result = await self._execute_step(step) results.append(step_result) # Update resource usage self._update_resource_usage(step) # Learn from step execution await self._learn_from_step(step, step_result) except Exception as e: self.logger.error(f"Error executing step: {e}") results.append({"error": str(e)}) return { "success": all(r.get("success", False) for r in results), "results": results } async def _execute_step(self, step: Dict[str, Any]) -> Dict[str, Any]: """Execute a single step of the plan.""" step_type = step.get("type", "unknown") if step_type == "reasoning": return await self._execute_reasoning_step(step) elif step_type == "learning": return await self._execute_learning_step(step) elif step_type == "action": return await self._execute_action_step(step) else: raise ValueError(f"Unknown step type: {step_type}") async def _execute_reasoning_step(self, step: Dict[str, Any]) -> Dict[str, Any]: """Execute a reasoning step.""" result = await self.reasoning_engine.reason( query=step["query"], context=step.get("context", {}), mode=ReasoningMode.ANALYTICAL ) return { "success": result.get("success", False), "reasoning_result": result } async def _execute_learning_step(self, step: Dict[str, Any]) -> Dict[str, Any]: """Execute a learning step.""" result = await self.meta_learning.learn( data=step["data"], context=step.get("context", {}) ) return { "success": result.get("success", False), "learning_result": result } async def _execute_action_step(self, step: Dict[str, Any]) -> Dict[str, Any]: """Execute an action step.""" action_type = step.get("action_type") if action_type == "api_call": return await self._make_api_call(step) elif action_type == "data_processing": return await self._process_data(step) elif action_type == "coordination": return await self._coordinate_action(step) else: raise ValueError(f"Unknown action type: {action_type}") def _check_resources(self, step: Dict[str, Any]) -> bool: """Check if sufficient resources are available.""" required_resources = step.get("required_resources", {}) for resource, amount in required_resources.items(): if self.resource_usage.get(resource, 0) + amount > self.resource_limits.get(resource, float('inf')): return False return True def _update_resource_usage(self, step: Dict[str, Any]): """Update resource usage after step execution.""" used_resources = step.get("used_resources", {}) for resource, amount in used_resources.items(): self.resource_usage[resource] = self.resource_usage.get(resource, 0) + amount async def _learn_from_execution(self, task: Task, result: Dict[str, Any]): """Learn from task execution experience.""" # Prepare learning data learning_data = { "task": task.__dict__, "result": result, "context": { "agent_state": self.state, "resource_usage": self.resource_usage, "performance_metrics": self._get_execution_metrics() } } # Learn patterns patterns = await self.meta_learning.learn( data=learning_data, context=self.knowledge_base ) # Update knowledge base self._update_knowledge_base(patterns) # Record adaptation self.adaptation_history.append({ "timestamp": datetime.now(), "patterns": patterns, "metrics": self._get_execution_metrics() }) async def _learn_from_step(self, step: Dict[str, Any], result: Dict[str, Any]): """Learn from individual step execution.""" if result.get("success", False): # Update success patterns pattern_key = f"{step['type']}:{step.get('action_type', 'none')}" self.knowledge_base["success_patterns"][pattern_key] += 1 # Learn from successful execution await self.meta_learning.learn( data={ "step": step, "result": result }, context={"pattern_key": pattern_key} ) def _update_knowledge_base(self, patterns: Dict[str, Any]): """Update knowledge base with new patterns.""" # Update expertise levels for area, pattern in patterns.get("expertise_patterns", {}).items(): if area in self.knowledge_base["expertise"]: current = self.knowledge_base["expertise"][area] self.knowledge_base["expertise"][area] = current * 0.9 + pattern * 0.1 # Add new learned skills new_skills = patterns.get("learned_skills", set()) self.knowledge_base["learned_skills"].update(new_skills) # Update interaction patterns for pattern, count in patterns.get("interaction_patterns", {}).items(): self.knowledge_base["interaction_patterns"][pattern] += count def _update_metrics(self, task: Task, result: Dict[str, Any]): """Update performance metrics.""" metrics = { "success": float(result.get("success", False)), "duration": (datetime.now() - task.created_at).total_seconds(), "resource_efficiency": self._calculate_resource_efficiency(), "learning_progress": self._calculate_learning_progress() } for key, value in metrics.items(): self.metrics[key].append(value) self.performance_history.append({ "timestamp": datetime.now(), "metrics": metrics }) def _calculate_resource_efficiency(self) -> float: """Calculate resource usage efficiency.""" if not self.resource_limits: return 1.0 efficiencies = [] for resource, usage in self.resource_usage.items(): limit = self.resource_limits.get(resource, float('inf')) if limit > 0: efficiencies.append(1 - (usage / limit)) return sum(efficiencies) / len(efficiencies) if efficiencies else 1.0 def _calculate_learning_progress(self) -> float: """Calculate learning progress.""" if not self.knowledge_base["expertise"]: return 0.0 return sum(self.knowledge_base["expertise"].values()) / len(self.knowledge_base["expertise"]) def _get_execution_metrics(self) -> Dict[str, float]: """Get current execution metrics.""" return { key: sum(values[-10:]) / len(values[-10:]) for key, values in self.metrics.items() if values } class AgenticSystem: """Advanced multi-agent system with orchestration.""" def __init__(self, config: Dict[str, Any] = None): self.config = config or {} # Initialize orchestrator self.orchestrator = AgentOrchestrator(config) # Initialize components self.agents: Dict[str, Agent] = {} self.reasoning_engine = ReasoningEngine( min_confidence=self.config.get('min_confidence', 0.7), parallel_threshold=self.config.get('parallel_threshold', 3), learning_rate=self.config.get('learning_rate', 0.1), strategy_weights=self.config.get('strategy_weights', { "LOCAL_LLM": 0.8, "CHAIN_OF_THOUGHT": 0.6, "TREE_OF_THOUGHTS": 0.5, "META_LEARNING": 0.4 }) ) self.meta_learning = MetaLearningStrategy(config) # System state self.state = "initialized" self.metrics: Dict[str, List[float]] = defaultdict(list) # Async support self.executor = ThreadPoolExecutor(max_workers=4) self.lock = asyncio.Lock() # Logging self.logger = logging.getLogger("AgenticSystem") async def create_agent( self, name: str, role: AgentRole, capabilities: List[AgentCapability], personality: AgentPersonality, expertise_areas: List[str] ) -> str: """Create a new agent.""" # Create agent profile profile = AgentProfile( id=str(uuid.uuid4()), name=name, role=role, capabilities=capabilities, personality=personality, expertise_areas=expertise_areas, learning_rate=0.1, risk_tolerance=0.5, created_at=datetime.now(), metadata={} ) # Create agent instance agent = Agent( profile=profile, reasoning_engine=self.reasoning_engine, meta_learning=self.meta_learning, config=self.config.get("agent_config", {}) ) # Register with orchestrator agent_id = await self.orchestrator.register_agent( role=role, capabilities=[c.value for c in capabilities] ) # Store agent async with self.lock: self.agents[agent_id] = agent return agent_id async def submit_task( self, description: str, priority: TaskPriority = TaskPriority.MEDIUM, deadline: Optional[datetime] = None ) -> str: """Submit a task to the system.""" return await self.orchestrator.submit_task( description=description, priority=priority, deadline=deadline ) async def get_task_status(self, task_id: str) -> Dict[str, Any]: """Get status of a task.""" return await self.orchestrator.get_task_status(task_id) async def get_agent_status(self, agent_id: str) -> Dict[str, Any]: """Get status of an agent.""" agent = self.agents.get(agent_id) if not agent: raise ValueError(f"Unknown agent: {agent_id}") return { "profile": agent.profile.__dict__, "state": agent.state, "current_task": agent.current_task.__dict__ if agent.current_task else None, "metrics": agent._get_execution_metrics(), "resource_usage": agent.resource_usage } async def get_system_status(self) -> Dict[str, Any]: """Get overall system status.""" return { "state": self.state, "agent_count": len(self.agents), "active_tasks": len([a for a in self.agents.values() if a.state == AgentState.BUSY]), "performance_metrics": self._calculate_system_metrics(), "resource_usage": self._calculate_resource_usage() } def _calculate_system_metrics(self) -> Dict[str, float]: """Calculate overall system metrics.""" metrics = defaultdict(list) for agent in self.agents.values(): agent_metrics = agent._get_execution_metrics() for key, value in agent_metrics.items(): metrics[key].append(value) return { key: sum(values) / len(values) for key, values in metrics.items() if values } def _calculate_resource_usage(self) -> Dict[str, float]: """Calculate overall resource usage.""" usage = defaultdict(float) for agent in self.agents.values(): for resource, amount in agent.resource_usage.items(): usage[resource] += amount return dict(usage)