chore: remove outdated documentation and reference files

- Remove debugging service documentation (DEBUgging_SERVICES.md)
- Remove development logs policy and quick reference guides
- Remove E2E test creation summary
- Remove gift certificate example file
- Remove GitHub pull summary documentation

tests/integration/test_advanced_agent_capabilities.py

@@ -0,0 +1,965 @@
+#!/usr/bin/env python3
+"""
+Advanced Agent Capabilities Tests
+Phase 9.1: Enhanced OpenClaw Agent Performance (Weeks 7-9)
+"""
+
+import pytest
+import asyncio
+import time
+import json
+import requests
+import numpy as np
+from typing import Dict, List, Any, Optional, Tuple
+from dataclasses import dataclass, field
+from datetime import datetime, timedelta
+import logging
+from enum import Enum
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class LearningAlgorithm(Enum):
+    """Machine learning algorithms for agents"""
+    Q_LEARNING = "q_learning"
+    DEEP_Q_NETWORK = "deep_q_network"
+    ACTOR_CRITIC = "actor_critic"
+    PPO = "ppo"
+    REINFORCE = "reinforce"
+    SARSA = "sarsa"
+
+class AgentCapability(Enum):
+    """Advanced agent capabilities"""
+    META_LEARNING = "meta_learning"
+    SELF_OPTIMIZATION = "self_optimization"
+    MULTIMODAL_FUSION = "multimodal_fusion"
+    REINFORCEMENT_LEARNING = "reinforcement_learning"
+    CREATIVITY = "creativity"
+    SPECIALIZATION = "specialization"
+
+@dataclass
+class AgentSkill:
+    """Agent skill definition"""
+    skill_id: str
+    skill_name: str
+    skill_type: str
+    proficiency_level: float
+    learning_rate: float
+    acquisition_date: datetime
+    last_used: datetime
+    usage_count: int
+    
+@dataclass
+class LearningEnvironment:
+    """Learning environment configuration"""
+    environment_id: str
+    environment_type: str
+    state_space: Dict[str, Any]
+    action_space: Dict[str, Any]
+    reward_function: str
+    constraints: List[str]
+    
+@dataclass
+class ResourceAllocation:
+    """Resource allocation for agents"""
+    agent_id: str
+    cpu_cores: int
+    memory_gb: float
+    gpu_memory_gb: float
+    network_bandwidth_mbps: float
+    storage_gb: float
+    allocation_strategy: str
+
+class AdvancedAgentCapabilitiesTests:
+    """Test suite for advanced agent capabilities"""
+    
+    def __init__(self, agent_service_url: str = "http://127.0.0.1:8005"):
+        self.agent_service_url = agent_service_url
+        self.agents = self._setup_agents()
+        self.skills = self._setup_skills()
+        self.learning_environments = self._setup_learning_environments()
+        self.session = requests.Session()
+        self.session.timeout = 30
+        
+    def _setup_agents(self) -> List[Dict[str, Any]]:
+        """Setup advanced agents for testing"""
+        return [
+            {
+                "agent_id": "advanced_agent_001",
+                "agent_type": "meta_learning_agent",
+                "capabilities": [
+                    AgentCapability.META_LEARNING,
+                    AgentCapability.SELF_OPTIMIZATION,
+                    AgentCapability.MULTIMODAL_FUSION
+                ],
+                "learning_algorithms": [
+                    LearningAlgorithm.DEEP_Q_NETWORK,
+                    LearningAlgorithm.ACTOR_CRITIC,
+                    LearningAlgorithm.PPO
+                ],
+                "performance_metrics": {
+                    "learning_speed": 0.85,
+                    "adaptation_rate": 0.92,
+                    "problem_solving": 0.88,
+                    "creativity_score": 0.76
+                },
+                "resource_needs": {
+                    "min_cpu_cores": 8,
+                    "min_memory_gb": 16,
+                    "min_gpu_memory_gb": 8,
+                    "preferred_gpu_type": "nvidia_a100"
+                }
+            },
+            {
+                "agent_id": "creative_agent_001",
+                "agent_type": "creative_specialist",
+                "capabilities": [
+                    AgentCapability.CREATIVITY,
+                    AgentCapability.SPECIALIZATION,
+                    AgentCapability.MULTIMODAL_FUSION
+                ],
+                "learning_algorithms": [
+                    LearningAlgorithm.REINFORCE,
+                    LearningAlgorithm.ACTOR_CRITIC
+                ],
+                "performance_metrics": {
+                    "creativity_score": 0.94,
+                    "innovation_rate": 0.87,
+                    "specialization_depth": 0.91,
+                    "cross_domain_application": 0.82
+                },
+                "resource_needs": {
+                    "min_cpu_cores": 12,
+                    "min_memory_gb": 32,
+                    "min_gpu_memory_gb": 16,
+                    "preferred_gpu_type": "nvidia_h100"
+                }
+            },
+            {
+                "agent_id": "optimization_agent_001",
+                "agent_type": "resource_optimizer",
+                "capabilities": [
+                    AgentCapability.SELF_OPTIMIZATION,
+                    AgentCapability.REINFORCEMENT_LEARNING
+                ],
+                "learning_algorithms": [
+                    LearningAlgorithm.Q_LEARNING,
+                    LearningAlgorithm.PPO,
+                    LearningAlgorithm.SARSA
+                ],
+                "performance_metrics": {
+                    "optimization_efficiency": 0.96,
+                    "resource_utilization": 0.89,
+                    "cost_reduction": 0.84,
+                    "adaptation_speed": 0.91
+                },
+                "resource_needs": {
+                    "min_cpu_cores": 6,
+                    "min_memory_gb": 12,
+                    "min_gpu_memory_gb": 4,
+                    "preferred_gpu_type": "nvidia_a100"
+                }
+            }
+        ]
+        
+    def _setup_skills(self) -> List[AgentSkill]:
+        """Setup agent skills for testing"""
+        return [
+            AgentSkill(
+                skill_id="multimodal_processing_001",
+                skill_name="Advanced Multi-Modal Processing",
+                skill_type="technical",
+                proficiency_level=0.92,
+                learning_rate=0.15,
+                acquisition_date=datetime.now() - timedelta(days=30),
+                last_used=datetime.now() - timedelta(hours=2),
+                usage_count=145
+            ),
+            AgentSkill(
+                skill_id="market_analysis_001",
+                skill_name="Market Trend Analysis",
+                skill_type="analytical",
+                proficiency_level=0.87,
+                learning_rate=0.12,
+                acquisition_date=datetime.now() - timedelta(days=45),
+                last_used=datetime.now() - timedelta(hours=6),
+                usage_count=89
+            ),
+            AgentSkill(
+                skill_id="creative_problem_solving_001",
+                skill_name="Creative Problem Solving",
+                skill_type="creative",
+                proficiency_level=0.79,
+                learning_rate=0.18,
+                acquisition_date=datetime.now() - timedelta(days=20),
+                last_used=datetime.now() - timedelta(hours=1),
+                usage_count=34
+            )
+        ]
+        
+    def _setup_learning_environments(self) -> List[LearningEnvironment]:
+        """Setup learning environments for testing"""
+        return [
+            LearningEnvironment(
+                environment_id="marketplace_optimization_001",
+                environment_type="reinforcement_learning",
+                state_space={
+                    "market_conditions": 10,
+                    "agent_performance": 5,
+                    "resource_availability": 8
+                },
+                action_space={
+                    "pricing_adjustments": 5,
+                    "resource_allocation": 7,
+                    "strategy_selection": 4
+                },
+                reward_function="profit_maximization_with_constraints",
+                constraints=["fair_trading", "resource_limits", "market_stability"]
+            ),
+            LearningEnvironment(
+                environment_id="skill_acquisition_001",
+                environment_type="meta_learning",
+                state_space={
+                    "current_skills": 20,
+                    "learning_progress": 15,
+                    "performance_history": 50
+                },
+                action_space={
+                    "skill_selection": 25,
+                    "learning_strategy": 6,
+                    "resource_allocation": 8
+                },
+                reward_function="skill_acquisition_efficiency",
+                constraints=["cognitive_load", "time_constraints", "resource_budget"]
+            )
+        ]
+        
+    async def test_meta_learning_capability(self, agent_id: str, learning_tasks: List[str]) -> Dict[str, Any]:
+        """Test advanced meta-learning for faster skill acquisition"""
+        try:
+            agent = next((a for a in self.agents if a["agent_id"] == agent_id), None)
+            if not agent:
+                return {"error": f"Agent {agent_id} not found"}
+                
+            # Test meta-learning setup
+            meta_learning_payload = {
+                "agent_id": agent_id,
+                "learning_tasks": learning_tasks,
+                "meta_learning_algorithm": "MAML",  # Model-Agnostic Meta-Learning
+                "adaptation_steps": 5,
+                "meta_batch_size": 32,
+                "inner_learning_rate": 0.01,
+                "outer_learning_rate": 0.001
+            }
+            
+            response = self.session.post(
+                f"{self.agent_service_url}/v1/meta-learning/setup",
+                json=meta_learning_payload,
+                timeout=20
+            )
+            
+            if response.status_code == 200:
+                setup_result = response.json()
+                
+                # Test meta-learning training
+                training_payload = {
+                    "agent_id": agent_id,
+                    "training_episodes": 100,
+                    "task_distribution": "uniform",
+                    "adaptation_evaluation": True
+                }
+                
+                training_response = self.session.post(
+                    f"{self.agent_service_url}/v1/meta-learning/train",
+                    json=training_payload,
+                    timeout=60
+                )
+                
+                if training_response.status_code == 200:
+                    training_result = training_response.json()
+                    
+                    return {
+                        "agent_id": agent_id,
+                        "learning_tasks": learning_tasks,
+                        "setup_result": setup_result,
+                        "training_result": training_result,
+                        "adaptation_speed": training_result.get("adaptation_speed"),
+                        "meta_learning_efficiency": training_result.get("efficiency"),
+                        "skill_acquisition_rate": training_result.get("skill_acquisition_rate"),
+                        "success": True
+                    }
+                else:
+                    return {
+                        "agent_id": agent_id,
+                        "setup_result": setup_result,
+                        "training_error": f"Training failed with status {training_response.status_code}",
+                        "success": False
+                    }
+            else:
+                return {
+                    "agent_id": agent_id,
+                    "error": f"Meta-learning setup failed with status {response.status_code}",
+                    "success": False
+                }
+                
+        except Exception as e:
+            return {
+                "agent_id": agent_id,
+                "error": str(e),
+                "success": False
+            }
+            
+    async def test_self_optimizing_resource_management(self, agent_id: str, initial_allocation: ResourceAllocation) -> Dict[str, Any]:
+        """Test self-optimizing agent resource management"""
+        try:
+            agent = next((a for a in self.agents if a["agent_id"] == agent_id), None)
+            if not agent:
+                return {"error": f"Agent {agent_id} not found"}
+                
+            # Test resource optimization setup
+            optimization_payload = {
+                "agent_id": agent_id,
+                "initial_allocation": asdict(initial_allocation),
+                "optimization_objectives": [
+                    "minimize_cost",
+                    "maximize_performance",
+                    "balance_utilization"
+                ],
+                "optimization_algorithm": "reinforcement_learning",
+                "optimization_horizon": "24h",
+                "constraints": {
+                    "max_cost_per_hour": 10.0,
+                    "min_performance_threshold": 0.85,
+                    "max_resource_waste": 0.15
+                }
+            }
+            
+            response = self.session.post(
+                f"{self.agent_service_url}/v1/resource-optimization/setup",
+                json=optimization_payload,
+                timeout=15
+            )
+            
+            if response.status_code == 200:
+                setup_result = response.json()
+                
+                # Test optimization execution
+                execution_payload = {
+                    "agent_id": agent_id,
+                    "optimization_period_hours": 24,
+                    "performance_monitoring": True,
+                    "auto_adjustment": True
+                }
+                
+                execution_response = self.session.post(
+                    f"{self.agent_service_url}/v1/resource-optimization/execute",
+                    json=execution_payload,
+                    timeout=30
+                )
+                
+                if execution_response.status_code == 200:
+                    execution_result = execution_response.json()
+                    
+                    return {
+                        "agent_id": agent_id,
+                        "initial_allocation": asdict(initial_allocation),
+                        "optimized_allocation": execution_result.get("optimized_allocation"),
+                        "cost_savings": execution_result.get("cost_savings"),
+                        "performance_improvement": execution_result.get("performance_improvement"),
+                        "resource_utilization": execution_result.get("resource_utilization"),
+                        "optimization_efficiency": execution_result.get("efficiency"),
+                        "success": True
+                    }
+                else:
+                    return {
+                        "agent_id": agent_id,
+                        "setup_result": setup_result,
+                        "execution_error": f"Optimization execution failed with status {execution_response.status_code}",
+                        "success": False
+                    }
+            else:
+                return {
+                    "agent_id": agent_id,
+                    "error": f"Resource optimization setup failed with status {response.status_code}",
+                    "success": False
+                }
+                
+        except Exception as e:
+            return {
+                "agent_id": agent_id,
+                "error": str(e),
+                "success": False
+            }
+            
+    async def test_multimodal_agent_fusion(self, agent_id: str, modalities: List[str]) -> Dict[str, Any]:
+        """Test multi-modal agent fusion for enhanced capabilities"""
+        try:
+            agent = next((a for a in self.agents if a["agent_id"] == agent_id), None)
+            if not agent:
+                return {"error": f"Agent {agent_id} not found"}
+                
+            # Test multimodal fusion setup
+            fusion_payload = {
+                "agent_id": agent_id,
+                "input_modalities": modalities,
+                "fusion_architecture": "cross_modal_attention",
+                "fusion_strategy": "adaptive_weighting",
+                "output_modalities": ["unified_representation"],
+                "performance_targets": {
+                    "fusion_accuracy": 0.90,
+                    "processing_speed": 0.5,  # seconds
+                    "memory_efficiency": 0.85
+                }
+            }
+            
+            response = self.session.post(
+                f"{self.agent_service_url}/v1/multimodal-fusion/setup",
+                json=fusion_payload,
+                timeout=20
+            )
+            
+            if response.status_code == 200:
+                setup_result = response.json()
+                
+                # Test fusion processing
+                processing_payload = {
+                    "agent_id": agent_id,
+                    "test_inputs": {
+                        "text": "Analyze market trends for AI compute resources",
+                        "image": "market_chart.png",
+                        "audio": "market_analysis.wav",
+                        "tabular": "price_data.csv"
+                    },
+                    "fusion_evaluation": True
+                }
+                
+                processing_response = self.session.post(
+                    f"{self.agent_service_url}/v1/multimodal-fusion/process",
+                    json=processing_payload,
+                    timeout=25
+                )
+                
+                if processing_response.status_code == 200:
+                    processing_result = processing_response.json()
+                    
+                    return {
+                        "agent_id": agent_id,
+                        "input_modalities": modalities,
+                        "fusion_result": processing_result,
+                        "fusion_accuracy": processing_result.get("accuracy"),
+                        "processing_time": processing_result.get("processing_time"),
+                        "memory_usage": processing_result.get("memory_usage"),
+                        "cross_modal_attention_weights": processing_result.get("attention_weights"),
+                        "enhanced_capabilities": processing_result.get("enhanced_capabilities"),
+                        "success": True
+                    }
+                else:
+                    return {
+                        "agent_id": agent_id,
+                        "setup_result": setup_result,
+                        "processing_error": f"Fusion processing failed with status {processing_response.status_code}",
+                        "success": False
+                    }
+            else:
+                return {
+                    "agent_id": agent_id,
+                    "error": f"Multimodal fusion setup failed with status {response.status_code}",
+                    "success": False
+                }
+                
+        except Exception as e:
+            return {
+                "agent_id": agent_id,
+                "error": str(e),
+                "success": False
+            }
+            
+    async def test_advanced_reinforcement_learning(self, agent_id: str, environment_id: str) -> Dict[str, Any]:
+        """Test advanced reinforcement learning for marketplace strategies"""
+        try:
+            agent = next((a for a in self.agents if a["agent_id"] == agent_id), None)
+            if not agent:
+                return {"error": f"Agent {agent_id} not found"}
+                
+            environment = next((e for e in self.learning_environments if e.environment_id == environment_id), None)
+            if not environment:
+                return {"error": f"Environment {environment_id} not found"}
+                
+            # Test RL training setup
+            rl_payload = {
+                "agent_id": agent_id,
+                "environment_id": environment_id,
+                "algorithm": "PPO",  # Proximal Policy Optimization
+                "hyperparameters": {
+                    "learning_rate": 0.0003,
+                    "batch_size": 64,
+                    "gamma": 0.99,
+                    "lambda": 0.95,
+                    "clip_epsilon": 0.2,
+                    "entropy_coefficient": 0.01
+                },
+                "training_episodes": 1000,
+                "evaluation_frequency": 100,
+                "convergence_threshold": 0.001
+            }
+            
+            response = self.session.post(
+                f"{self.agent_service_url}/v1/reinforcement-learning/train",
+                json=rl_payload,
+                timeout=120)  # 2 minutes for training
+                
+            if response.status_code == 200:
+                training_result = response.json()
+                
+                # Test policy evaluation
+                evaluation_payload = {
+                    "agent_id": agent_id,
+                    "environment_id": environment_id,
+                    "evaluation_episodes": 100,
+                    "deterministic_evaluation": True
+                }
+                
+                evaluation_response = self.session.post(
+                    f"{self.agent_service_url}/v1/reinforcement-learning/evaluate",
+                    json=evaluation_payload,
+                    timeout=30
+                )
+                
+                if evaluation_response.status_code == 200:
+                    evaluation_result = evaluation_response.json()
+                    
+                    return {
+                        "agent_id": agent_id,
+                        "environment_id": environment_id,
+                        "training_result": training_result,
+                        "evaluation_result": evaluation_result,
+                        "convergence_episode": training_result.get("convergence_episode"),
+                        "final_performance": evaluation_result.get("average_reward"),
+                        "policy_stability": evaluation_result.get("policy_stability"),
+                        "learning_curve": training_result.get("learning_curve"),
+                        "success": True
+                    }
+                else:
+                    return {
+                        "agent_id": agent_id,
+                        "training_result": training_result,
+                        "evaluation_error": f"Policy evaluation failed with status {evaluation_response.status_code}",
+                        "success": False
+                    }
+            else:
+                return {
+                    "agent_id": agent_id,
+                    "error": f"RL training failed with status {response.status_code}",
+                    "success": False
+                }
+                
+        except Exception as e:
+            return {
+                "agent_id": agent_id,
+                "error": str(e),
+                "success": False
+            }
+            
+    async def test_agent_creativity_development(self, agent_id: str, creative_challenges: List[str]) -> Dict[str, Any]:
+        """Test agent creativity and specialized AI capability development"""
+        try:
+            agent = next((a for a in self.agents if a["agent_id"] == agent_id), None)
+            if not agent:
+                return {"error": f"Agent {agent_id} not found"}
+                
+            # Test creativity development setup
+            creativity_payload = {
+                "agent_id": agent_id,
+                "creative_challenges": creative_challenges,
+                "creativity_metrics": [
+                    "novelty",
+                    "usefulness",
+                    "surprise",
+                    "elegance",
+                    "feasibility"
+                ],
+                "development_method": "generative_adversarial_learning",
+                "inspiration_sources": [
+                    "market_data",
+                    "scientific_papers",
+                    "art_patterns",
+                    "natural_systems"
+                ]
+            }
+            
+            response = self.session.post(
+                f"{self.agent_service_url}/v1/creativity/develop",
+                json=creativity_payload,
+                timeout=45
+            )
+            
+            if response.status_code == 200:
+                development_result = response.json()
+                
+                # Test creative problem solving
+                problem_solving_payload = {
+                    "agent_id": agent_id,
+                    "problem_statement": "Design an innovative pricing strategy for AI compute resources that maximizes both provider earnings and consumer access",
+                    "creativity_constraints": {
+                        "market_viability": True,
+                        "technical_feasibility": True,
+                        "ethical_considerations": True
+                    },
+                    "solution_evaluation": True
+                }
+                
+                solving_response = self.session.post(
+                    f"{self.agent_service_url}/v1/creativity/solve",
+                    json=problem_solving_payload,
+                    timeout=30
+                )
+                
+                if solving_response.status_code == 200:
+                    solving_result = solving_response.json()
+                    
+                    return {
+                        "agent_id": agent_id,
+                        "creative_challenges": creative_challenges,
+                        "development_result": development_result,
+                        "problem_solving_result": solving_result,
+                        "creativity_score": solving_result.get("creativity_score"),
+                        "innovation_level": solving_result.get("innovation_level"),
+                        "practical_applicability": solving_result.get("practical_applicability"),
+                        "novel_solutions": solving_result.get("solutions"),
+                        "success": True
+                    }
+                else:
+                    return {
+                        "agent_id": agent_id,
+                        "development_result": development_result,
+                        "solving_error": f"Creative problem solving failed with status {solving_response.status_code}",
+                        "success": False
+                    }
+            else:
+                return {
+                    "agent_id": agent_id,
+                    "error": f"Creativity development failed with status {response.status_code}",
+                    "success": False
+                }
+                
+        except Exception as e:
+            return {
+                "agent_id": agent_id,
+                "error": str(e),
+                "success": False
+            }
+            
+    async def test_agent_specialization_development(self, agent_id: str, specialization_domain: str) -> Dict[str, Any]:
+        """Test agent specialization in specific domains"""
+        try:
+            agent = next((a for a in self.agents if a["agent_id"] == agent_id), None)
+            if not agent:
+                return {"error": f"Agent {agent_id} not found"}
+                
+            # Test specialization development
+            specialization_payload = {
+                "agent_id": agent_id,
+                "specialization_domain": specialization_domain,
+                "training_data_sources": [
+                    "domain_expert_knowledge",
+                    "best_practices",
+                    "case_studies",
+                    "simulation_data"
+                ],
+                "specialization_depth": "expert",
+                "cross_domain_transfer": True,
+                "performance_targets": {
+                    "domain_accuracy": 0.95,
+                    "expertise_level": 0.90,
+                    "adaptation_speed": 0.85
+                }
+            }
+            
+            response = self.session.post(
+                f"{self.agent_service_url}/v1/specialization/develop",
+                json=specialization_payload,
+                timeout=60
+            )
+            
+            if response.status_code == 200:
+                development_result = response.json()
+                
+                # Test specialization performance
+                performance_payload = {
+                    "agent_id": agent_id,
+                    "specialization_domain": specialization_domain,
+                    "test_scenarios": 20,
+                    "difficulty_levels": ["basic", "intermediate", "advanced", "expert"],
+                    "performance_benchmark": True
+                }
+                
+                performance_response = self.session.post(
+                    f"{self.agent_service_url}/v1/specialization/evaluate",
+                    json=performance_payload,
+                    timeout=30
+                )
+                
+                if performance_response.status_code == 200:
+                    performance_result = performance_response.json()
+                    
+                    return {
+                        "agent_id": agent_id,
+                        "specialization_domain": specialization_domain,
+                        "development_result": development_result,
+                        "performance_result": performance_result,
+                        "specialization_score": performance_result.get("specialization_score"),
+                        "expertise_level": performance_result.get("expertise_level"),
+                        "cross_domain_transferability": performance_result.get("cross_domain_transfer"),
+                        "specialized_skills": performance_result.get("acquired_skills"),
+                        "success": True
+                    }
+                else:
+                    return {
+                        "agent_id": agent_id,
+                        "development_result": development_result,
+                        "performance_error": f"Specialization evaluation failed with status {performance_response.status_code}",
+                        "success": False
+                    }
+            else:
+                return {
+                    "agent_id": agent_id,
+                    "error": f"Specialization development failed with status {response.status_code}",
+                    "success": False
+                }
+                
+        except Exception as e:
+            return {
+                "agent_id": agent_id,
+                "error": str(e),
+                "success": False
+            }
+
+# Test Fixtures
+@pytest.fixture
+async def advanced_agent_tests():
+    """Create advanced agent capabilities test instance"""
+    return AdvancedAgentCapabilitiesTests()
+
+@pytest.fixture
+def sample_resource_allocation():
+    """Sample resource allocation for testing"""
+    return ResourceAllocation(
+        agent_id="advanced_agent_001",
+        cpu_cores=8,
+        memory_gb=16.0,
+        gpu_memory_gb=8.0,
+        network_bandwidth_mbps=1000,
+        storage_gb=500,
+        allocation_strategy="balanced"
+    )
+
+@pytest.fixture
+def sample_learning_tasks():
+    """Sample learning tasks for testing"""
+    return [
+        "market_price_prediction",
+        "resource_demand_forecasting",
+        "trading_strategy_optimization",
+        "risk_assessment",
+        "portfolio_management"
+    ]
+
+@pytest.fixture
+def sample_modalities():
+    """Sample modalities for multimodal fusion testing"""
+    return ["text", "image", "audio", "tabular", "graph"]
+
+@pytest.fixture
+def sample_creative_challenges():
+    """Sample creative challenges for testing"""
+    return [
+        "design_novel_marketplace_mechanism",
+        "create_efficient_resource_allocation_algorithm",
+        "develop_innovative_pricing_strategy",
+        "solve_cold_start_problem_for_new_agents"
+    ]
+
+# Test Classes
+class TestMetaLearningCapabilities:
+    """Test advanced meta-learning capabilities"""
+    
+    @pytest.mark.asyncio
+    async def test_meta_learning_setup(self, advanced_agent_tests, sample_learning_tasks):
+        """Test meta-learning setup and configuration"""
+        result = await advanced_agent_tests.test_meta_learning_capability(
+            "advanced_agent_001",
+            sample_learning_tasks
+        )
+        
+        assert result.get("success", False), "Meta-learning setup failed"
+        assert "setup_result" in result, "No setup result provided"
+        assert "training_result" in result, "No training result provided"
+        assert result.get("adaptation_speed", 0) > 0, "No adaptation speed measured"
+        
+    @pytest.mark.asyncio
+    async def test_skill_acquisition_acceleration(self, advanced_agent_tests):
+        """Test accelerated skill acquisition through meta-learning"""
+        result = await advanced_agent_tests.test_meta_learning_capability(
+            "advanced_agent_001",
+            ["quick_skill_acquisition_test"]
+        )
+        
+        assert result.get("success", False), "Skill acquisition test failed"
+        assert result.get("skill_acquisition_rate", 0) > 0.5, "Skill acquisition rate too low"
+        assert result.get("meta_learning_efficiency", 0) > 0.7, "Meta-learning efficiency too low"
+
+class TestSelfOptimization:
+    """Test self-optimizing resource management"""
+    
+    @pytest.mark.asyncio
+    async def test_resource_optimization(self, advanced_agent_tests, sample_resource_allocation):
+        """Test self-optimizing resource management"""
+        result = await advanced_agent_tests.test_self_optimizing_resource_management(
+            "optimization_agent_001",
+            sample_resource_allocation
+        )
+        
+        assert result.get("success", False), "Resource optimization test failed"
+        assert "optimized_allocation" in result, "No optimized allocation provided"
+        assert result.get("cost_savings", 0) > 0, "No cost savings achieved"
+        assert result.get("performance_improvement", 0) > 0, "No performance improvement achieved"
+        
+    @pytest.mark.asyncio
+    async def test_adaptive_resource_scaling(self, advanced_agent_tests):
+        """Test adaptive resource scaling based on workload"""
+        dynamic_allocation = ResourceAllocation(
+            agent_id="optimization_agent_001",
+            cpu_cores=4,
+            memory_gb=8.0,
+            gpu_memory_gb=4.0,
+            network_bandwidth_mbps=500,
+            storage_gb=250,
+            allocation_strategy="dynamic"
+        )
+        
+        result = await advanced_agent_tests.test_self_optimizing_resource_management(
+            "optimization_agent_001",
+            dynamic_allocation
+        )
+        
+        assert result.get("success", False), "Adaptive scaling test failed"
+        assert result.get("resource_utilization", 0) > 0.8, "Resource utilization too low"
+
+class TestMultimodalFusion:
+    """Test multi-modal agent fusion capabilities"""
+    
+    @pytest.mark.asyncio
+    async def test_multimodal_fusion_setup(self, advanced_agent_tests, sample_modalities):
+        """Test multi-modal fusion setup and processing"""
+        result = await advanced_agent_tests.test_multimodal_agent_fusion(
+            "advanced_agent_001",
+            sample_modalities
+        )
+        
+        assert result.get("success", False), "Multimodal fusion test failed"
+        assert "fusion_result" in result, "No fusion result provided"
+        assert result.get("fusion_accuracy", 0) > 0.85, "Fusion accuracy too low"
+        assert result.get("processing_time", 10) < 1.0, "Processing time too slow"
+        
+    @pytest.mark.asyncio
+    async def test_cross_modal_attention(self, advanced_agent_tests):
+        """Test cross-modal attention mechanisms"""
+        result = await advanced_agent_tests.test_multimodal_agent_fusion(
+            "advanced_agent_001",
+            ["text", "image", "audio"]
+        )
+        
+        assert result.get("success", False), "Cross-modal attention test failed"
+        assert "cross_modal_attention_weights" in result, "No attention weights provided"
+        assert len(result.get("enhanced_capabilities", [])) > 0, "No enhanced capabilities detected"
+
+class TestAdvancedReinforcementLearning:
+    """Test advanced reinforcement learning for marketplace strategies"""
+    
+    @pytest.mark.asyncio
+    async def test_ppo_training(self, advanced_agent_tests):
+        """Test PPO reinforcement learning training"""
+        result = await advanced_agent_tests.test_advanced_reinforcement_learning(
+            "advanced_agent_001",
+            "marketplace_optimization_001"
+        )
+        
+        assert result.get("success", False), "PPO training test failed"
+        assert "training_result" in result, "No training result provided"
+        assert "evaluation_result" in result, "No evaluation result provided"
+        assert result.get("final_performance", 0) > 0, "No positive final performance"
+        assert result.get("convergence_episode", 1000) < 1000, "Training did not converge efficiently"
+        
+    @pytest.mark.asyncio
+    async def test_policy_stability(self, advanced_agent_tests):
+        """Test policy stability and consistency"""
+        result = await advanced_agent_tests.test_advanced_reinforcement_learning(
+            "advanced_agent_001",
+            "marketplace_optimization_001"
+        )
+        
+        assert result.get("success", False), "Policy stability test failed"
+        assert result.get("policy_stability", 0) > 0.8, "Policy stability too low"
+        assert "learning_curve" in result, "No learning curve provided"
+
+class TestAgentCreativity:
+    """Test agent creativity and innovation capabilities"""
+    
+    @pytest.mark.asyncio
+    async def test_creativity_development(self, advanced_agent_tests, sample_creative_challenges):
+        """Test creativity development and enhancement"""
+        result = await advanced_agent_tests.test_agent_creativity_development(
+            "creative_agent_001",
+            sample_creative_challenges
+        )
+        
+        assert result.get("success", False), "Creativity development test failed"
+        assert "development_result" in result, "No creativity development result"
+        assert "problem_solving_result" in result, "No creative problem solving result"
+        assert result.get("creativity_score", 0) > 0.7, "Creativity score too low"
+        assert result.get("innovation_level", 0) > 0.6, "Innovation level too low"
+        
+    @pytest.mark.asyncio
+    async def test_novel_solution_generation(self, advanced_agent_tests):
+        """Test generation of novel solutions"""
+        result = await advanced_agent_tests.test_agent_creativity_development(
+            "creative_agent_001",
+            ["generate_novel_solution_test"]
+        )
+        
+        assert result.get("success", False), "Novel solution generation test failed"
+        assert len(result.get("novel_solutions", [])) > 0, "No novel solutions generated"
+        assert result.get("practical_applicability", 0) > 0.5, "Solutions not practically applicable"
+
+class TestAgentSpecialization:
+    """Test agent specialization in specific domains"""
+    
+    @pytest.mark.asyncio
+    async def test_domain_specialization(self, advanced_agent_tests):
+        """Test agent specialization in specific domains"""
+        result = await advanced_agent_tests.test_agent_specialization_development(
+            "creative_agent_001",
+            "marketplace_design"
+        )
+        
+        assert result.get("success", False), "Domain specialization test failed"
+        assert "development_result" in result, "No specialization development result"
+        assert "performance_result" in result, "No specialization performance result"
+        assert result.get("specialization_score", 0) > 0.8, "Specialization score too low"
+        assert result.get("expertise_level", 0) > 0.7, "Expertise level too low"
+        
+    @pytest.mark.asyncio
+    async def test_cross_domain_transfer(self, advanced_agent_tests):
+        """Test cross-domain knowledge transfer"""
+        result = await advanced_agent_tests.test_agent_specialization_development(
+            "advanced_agent_001",
+            "multi_domain_optimization"
+        )
+        
+        assert result.get("success", False), "Cross-domain transfer test failed"
+        assert result.get("cross_domain_transferability", 0) > 0.6, "Cross-domain transferability too low"
+        assert len(result.get("specialized_skills", [])) > 0, "No specialized skills acquired"
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v", "--tb=short"])