"""
AI Prompt Engineer Agent - Unified Agent Framework Integration
Combines LangChain, AutoGen, OpenAI, Anthropic, and custom BDI architecture
"""

import asyncio
import json
from typing import Dict, List, Optional, Any, Tuple, Union
from dataclasses import dataclass, field
from enum import Enum
from datetime import datetime
import numpy as np
from abc import ABC, abstractmethod

# External framework imports (these would be actual imports in production)
from langchain.agents import AgentExecutor, create_structured_chat_agent, Tool
from langchain.memory import ConversationBufferWindowMemory
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain.schema import BaseMessage, HumanMessage, SystemMessage
from langchain_core.runnables import RunnablePassthrough
from langchain_anthropic import ChatAnthropic
from langchain_openai import ChatOpenAI
from langchain_community.llms import LlamaCpp
from langchain.callbacks import StreamingStdOutCallbackHandler

# AutoGen for multi-agent coordination
from autogen import AssistantAgent, UserProxyAgent, GroupChat, GroupChatManager
from autogen.agentchat.contrib.capabilities import context_handling

# Model-specific SDKs
from anthropic import Anthropic
from openai import OpenAI
import google.generativeai as genai

# MCP (Model Context Protocol) integration
from mcp import MCPClient, Resource, Tool as MCPTool


class ProcessingTier(Enum):
    """Processing tier selection"""
    OFFLINE = 0      # Local models only
    TACTICAL = 1     # Online with single model
    STRATEGIC = 2    # Multi-agent coordination


class IntentionType(Enum):
    """Types of intentions in BDI architecture"""
    ENHANCE_QUERY = "enhance_query"
    BUILD_CONTEXT = "build_context"
    SECURITY_CHECK = "security_check"
    LEARN_PATTERN = "learn_pattern"
    COORDINATE_AGENTS = "coordinate_agents"


@dataclass
class Belief:
    """Represents agent's belief about the world state"""
    timestamp: datetime
    belief_type: str
    content: Dict[str, Any]
    confidence: float
    source: str
    
    def is_valid(self, max_age_seconds: int = 300) -> bool:
        """Check if belief is still valid"""
        age = (datetime.utcnow() - self.timestamp).total_seconds()
        return age < max_age_seconds and self.confidence > 0.5


@dataclass
class Desire:
    """Represents agent's goals"""
    goal_id: str
    goal_type: str
    priority: float
    constraints: Dict[str, Any]
    success_criteria: List[str]
    deadline: Optional[datetime] = None
    
    def is_achievable(self, beliefs: List[Belief]) -> bool:
        """Check if goal is achievable given current beliefs"""
        # Simplified check - in practice would be more complex
        return self.priority > 0.5


@dataclass
class Intention:
    """Represents agent's committed plans"""
    intention_id: str
    intention_type: IntentionType
    plan_steps: List[Dict[str, Any]]
    current_step: int = 0
    status: str = "pending"
    tier_requirement: ProcessingTier = ProcessingTier.TACTICAL
    
    def get_next_action(self) -> Optional[Dict[str, Any]]:
        """Get next action in plan"""
        if self.current_step < len(self.plan_steps):
            return self.plan_steps[self.current_step]
        return None


class BDIReasoningEngine:
    """
    Belief-Desire-Intention reasoning engine for autonomous agent behavior
    """
    
    def __init__(self):
        self.beliefs: List[Belief] = []
        self.desires: List[Desire] = []
        self.intentions: List[Intention] = []
        self.belief_revision_rules = self._init_belief_rules()
        self.plan_library = self._init_plan_library()
        
    def _init_belief_rules(self) -> Dict[str, Any]:
        """Initialize belief revision rules"""
        return {
            "query_complexity": {
                "indicators": ["length", "domain_terms", "multi_step"],
                "update_function": self._update_complexity_belief
            },
            "user_expertise": {
                "indicators": ["terminology", "specificity", "history"],
                "update_function": self._update_expertise_belief
            },
            "security_context": {
                "indicators": ["classification", "pii_detected", "injection_risk"],
                "update_function": self._update_security_belief
            }
        }
    
    def _init_plan_library(self) -> Dict[IntentionType, List[Dict]]:
        """Initialize library of plans for different intentions"""
        return {
            IntentionType.ENHANCE_QUERY: [
                {"action": "analyze_complexity", "tier": ProcessingTier.OFFLINE},
                {"action": "select_strategy", "tier": ProcessingTier.OFFLINE},
                {"action": "apply_enhancement", "tier": ProcessingTier.TACTICAL},
                {"action": "validate_quality", "tier": ProcessingTier.TACTICAL}
            ],
            IntentionType.BUILD_CONTEXT: [
                {"action": "gather_user_profile", "tier": ProcessingTier.OFFLINE},
                {"action": "retrieve_history", "tier": ProcessingTier.OFFLINE},
                {"action": "analyze_environment", "tier": ProcessingTier.OFFLINE},
                {"action": "synthesize_context", "tier": ProcessingTier.TACTICAL}
            ],
            IntentionType.COORDINATE_AGENTS: [
                {"action": "identify_required_agents", "tier": ProcessingTier.TACTICAL},
                {"action": "establish_communication", "tier": ProcessingTier.STRATEGIC},
                {"action": "distribute_tasks", "tier": ProcessingTier.STRATEGIC},
                {"action": "aggregate_results", "tier": ProcessingTier.STRATEGIC}
            ]
        }
    
    def perceive(self, observation: Dict[str, Any]) -> None:
        """Update beliefs based on new observations"""
        # Create new beliefs from observation
        if "query" in observation:
            complexity = self._assess_complexity(observation["query"])
            self.beliefs.append(Belief(
                timestamp=datetime.utcnow(),
                belief_type="query_complexity",
                content={"complexity": complexity, "query": observation["query"]},
                confidence=0.9,
                source="complexity_analyzer"
            ))
        
        if "user_context" in observation:
            self.beliefs.append(Belief(
                timestamp=datetime.utcnow(),
                belief_type="user_context",
                content=observation["user_context"],
                confidence=0.95,
                source="context_manager"
            ))
        
        # Revise existing beliefs
        self._revise_beliefs()
    
    def deliberate(self) -> Optional[Desire]:
        """Select desires based on current beliefs"""
        # Analyze beliefs to generate desires
        active_desires = []
        
        # Check if we need to enhance a query
        query_beliefs = [b for b in self.beliefs if b.belief_type == "query_complexity"]
        if query_beliefs and query_beliefs[-1].is_valid():
            active_desires.append(Desire(
                goal_id=f"enhance_{datetime.utcnow().timestamp()}",
                goal_type="query_enhancement",
                priority=query_beliefs[-1].content["complexity"],
                constraints={"max_latency": 5000, "min_quality": 0.85},
                success_criteria=["enhanced_query_produced", "quality_validated"]
            ))
        
        # Check if we need to coordinate with other agents
        if any(b.content.get("complexity", 0) > 0.8 for b in self.beliefs):
            active_desires.append(Desire(
                goal_id=f"coordinate_{datetime.utcnow().timestamp()}",
                goal_type="multi_agent_coordination",
                priority=0.9,
                constraints={"max_agents": 3, "timeout": 15000},
                success_criteria=["consensus_reached", "result_synthesized"]
            ))
        
        # Select highest priority achievable desire
        achievable_desires = [d for d in active_desires if d.is_achievable(self.beliefs)]
        if achievable_desires:
            return max(achievable_desires, key=lambda d: d.priority)
        
        return None
    
    def plan(self, desire: Desire) -> Intention:
        """Create intention (plan) to achieve desire"""
        intention_type = self._map_desire_to_intention(desire)
        plan_steps = self.plan_library.get(intention_type, [])
        
        # Customize plan based on current beliefs
        tier = self._determine_required_tier()
        
        return Intention(
            intention_id=f"intent_{desire.goal_id}",
            intention_type=intention_type,
            plan_steps=plan_steps,
            tier_requirement=tier
        )
    
    def _determine_required_tier(self) -> ProcessingTier:
        """Determine required processing tier based on beliefs"""
        # Check connectivity
        connectivity_beliefs = [b for b in self.beliefs if b.belief_type == "connectivity"]
        if connectivity_beliefs and not connectivity_beliefs[-1].content.get("online", True):
            return ProcessingTier.OFFLINE
        
        # Check complexity
        complexity_beliefs = [b for b in self.beliefs if b.belief_type == "query_complexity"]
        if complexity_beliefs:
            complexity = complexity_beliefs[-1].content.get("complexity", 0)
            if complexity > 0.8:
                return ProcessingTier.STRATEGIC
            elif complexity > 0.3:
                return ProcessingTier.TACTICAL
        
        return ProcessingTier.OFFLINE
    
    def _assess_complexity(self, query: str) -> float:
        """Assess query complexity (simplified)"""
        factors = {
            "length": len(query.split()) / 100,
            "questions": query.count("?") * 0.2,
            "technical_terms": sum(1 for term in ["implement", "architecture", "analyze"] if term in query.lower()) * 0.1
        }
        return min(1.0, sum(factors.values()))
    
    def _map_desire_to_intention(self, desire: Desire) -> IntentionType:
        """Map desire type to intention type"""
        mapping = {
            "query_enhancement": IntentionType.ENHANCE_QUERY,
            "context_building": IntentionType.BUILD_CONTEXT,
            "security_validation": IntentionType.SECURITY_CHECK,
            "multi_agent_coordination": IntentionType.COORDINATE_AGENTS
        }
        return mapping.get(desire.goal_type, IntentionType.ENHANCE_QUERY)
    
    def _revise_beliefs(self):
        """Revise beliefs based on consistency rules"""
        # Remove expired beliefs
        self.beliefs = [b for b in self.beliefs if b.is_valid()]
        
        # Apply revision rules
        for rule_name, rule in self.belief_revision_rules.items():
            relevant_beliefs = [b for b in self.beliefs if b.belief_type == rule_name]
            if relevant_beliefs:
                rule["update_function"](relevant_beliefs)
    
    def _update_complexity_belief(self, beliefs: List[Belief]):
        """Update complexity beliefs"""
        if len(beliefs) > 1:
            # Average recent complexity assessments
            avg_complexity = np.mean([b.content["complexity"] for b in beliefs[-3:]])
            beliefs[-1].content["complexity"] = avg_complexity
    
    def _update_expertise_belief(self, beliefs: List[Belief]):
        """Update user expertise beliefs"""
        pass  # Implementation depends on specific indicators
    
    def _update_security_belief(self, beliefs: List[Belief]):
        """Update security context beliefs"""
        pass  # Implementation depends on security policies


class UnifiedAgentFramework:
    """
    Main framework combining BDI reasoning with multiple agent implementations
    """
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.bdi_engine = BDIReasoningEngine()
        
        # Initialize different agent tiers
        self.offline_agent = self._init_offline_agent()
        self.tactical_agent = self._init_tactical_agent()
        self.strategic_coordinator = self._init_strategic_coordinator()
        
        # MCP client for tool access
        self.mcp_client = self._init_mcp_client()
        
        # Memory and state management
        self.memory = ConversationBufferWindowMemory(k=10)
        self.state = {"processing_tier": ProcessingTier.TACTICAL}
        
    def _init_offline_agent(self) -> Any:
        """Initialize offline agent using local Llama model"""
        # Local Llama 3.3 70B for offline processing
        llm = LlamaCpp(
            model_path=self.config["models"]["llama_path"],
            n_ctx=8192,
            n_threads=8,
            n_gpu_layers=35,  # Adjust based on GPU
            temperature=0.1,
            top_p=0.95,
            callback_manager=[StreamingStdOutCallbackHandler()],
            verbose=False
        )
        
        # Create LangChain agent with local tools
        tools = [
            Tool(
                name="local_knowledge",
                func=self._local_knowledge_search,
                description="Search local knowledge base for relevant information"
            ),
            Tool(
                name="pattern_matcher",
                func=self._pattern_match,
                description="Match query patterns to enhancement templates"
            )
        ]
        
        prompt = ChatPromptTemplate.from_messages([
            SystemMessage(content="""You are an offline prompt enhancement agent.
            Enhance queries using only local knowledge and patterns.
            Focus on clarity, specificity, and completeness."""),
            MessagesPlaceholder(variable_name="chat_history"),
            HumanMessage(content="{input}"),
            MessagesPlaceholder(variable_name="agent_scratchpad")
        ])
        
        agent = create_structured_chat_agent(llm, tools, prompt)
        return AgentExecutor(agent=agent, tools=tools, memory=self.memory)
    
    def _init_tactical_agent(self) -> Any:
        """Initialize tactical agent using Claude and GPT-4"""
        # Primary: Claude 3 Sonnet
        claude_llm = ChatAnthropic(
            model="claude-3-sonnet-20240229",
            temperature=0.2,
            max_tokens=4096
        )
        
        # Fallback: GPT-4
        openai_llm = ChatOpenAI(
            model="gpt-4-turbo-preview",
            temperature=0.2,
            max_tokens=4096
        )
        
        # Enhanced tools with MCP integration
        tools = [
            Tool(
                name="context7",
                func=self._context7_search,
                description="Search Context7 for up-to-date documentation"
            ),
            Tool(
                name="sequential_thinking",
                func=self._sequential_thinking,
                description="Apply sequential thinking for complex reasoning"
            ),
            Tool(
                name="knowledge_retrieval",
                func=self._hybrid_rag_search,
                description="Search hybrid RAG system for relevant context"
            )
        ]
        
        prompt = ChatPromptTemplate.from_messages([
            SystemMessage(content="""You are a tactical prompt enhancement agent.
            Use available tools to enhance queries with rich context and clarity.
            Follow the Context7 framework: user, task, data, environment, history, output, feedback."""),
            MessagesPlaceholder(variable_name="chat_history"),
            HumanMessage(content="{input}"),
            MessagesPlaceholder(variable_name="agent_scratchpad")
        ])
        
        # Create primary agent with Claude
        agent = create_structured_chat_agent(claude_llm, tools, prompt)
        executor = AgentExecutor(
            agent=agent,
            tools=tools,
            memory=self.memory,
            max_iterations=3,
            early_stopping_method="generate"
        )
        
        # Wrap with fallback logic
        return self._create_fallback_chain(executor, openai_llm, tools)
    
    def _init_strategic_coordinator(self) -> Any:
        """Initialize strategic multi-agent coordinator using AutoGen"""
        # Configuration for different specialized agents
        config_list = [
            {
                "model": "claude-3-opus-20240229",
                "api_type": "anthropic",
                "temperature": 0.1
            },
            {
                "model": "gpt-4-turbo-preview",
                "api_type": "openai",
                "temperature": 0.1
            }
        ]
        
        # Create specialized agents
        prompt_analyst = AssistantAgent(
            name="prompt_analyst",
            llm_config={"config_list": config_list},
            system_message="""You are a prompt analysis expert.
            Analyze queries for intent, complexity, and enhancement opportunities.
            Identify ambiguities and suggest clarifications."""
        )
        
        context_builder = AssistantAgent(
            name="context_builder",
            llm_config={"config_list": config_list},
            system_message="""You are a context building expert.
            Gather and synthesize relevant context using the Context7 framework.
            Ensure all seven lenses are considered."""
        )
        
        enhancement_specialist = AssistantAgent(
            name="enhancement_specialist",
            llm_config={"config_list": config_list},
            system_message="""You are a prompt enhancement expert.
            Transform queries into detailed, actionable prompts.
            Apply domain-specific enhancement strategies."""
        )
        
        quality_validator = AssistantAgent(
            name="quality_validator",
            llm_config={"config_list": config_list},
            system_message="""You are a quality validation expert.
            Verify enhanced prompts meet quality criteria.
            Check for completeness, clarity, and security compliance."""
        )
        
        # Create user proxy for coordination
        user_proxy = UserProxyAgent(
            name="coordinator",
            human_input_mode="NEVER",
            max_consecutive_auto_reply=1,
            code_execution_config={"use_docker": False}
        )
        
        # Set up group chat
        group_chat = GroupChat(
            agents=[prompt_analyst, context_builder, enhancement_specialist, quality_validator, user_proxy],
            messages=[],
            max_round=10,
            speaker_selection_method="round_robin"
        )
        
        return GroupChatManager(groupchat=group_chat, llm_config={"config_list": config_list})
    
    def _init_mcp_client(self) -> MCPClient:
        """Initialize MCP client for tool access"""
        mcp_config = {
            "servers": [
                {
                    "name": "context7",
                    "url": "mcp://context7.upstash.io",
                    "transport": "stdio"
                },
                {
                    "name": "sequential_thinking",
                    "url": "mcp://sequential-thinking.anthropic.com",
                    "transport": "stdio"
                },
                {
                    "name": "web_search",
                    "url": "mcp://search.agora.io",
                    "transport": "http"
                }
            ]
        }
        
        return MCPClient(mcp_config)
    
    async def process_query(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """
        Main entry point for processing queries through BDI cycle
        """
        # Perceive: Update beliefs with new observation
        observation = {
            "query": query,
            "user_context": context,
            "connectivity": self._check_connectivity(),
            "timestamp": datetime.utcnow()
        }
        self.bdi_engine.perceive(observation)
        
        # Deliberate: Select goal
        desire = self.bdi_engine.deliberate()
        if not desire:
            # No specific goal, default to basic enhancement
            desire = Desire(
                goal_id="default_enhance",
                goal_type="query_enhancement",
                priority=0.5,
                constraints={},
                success_criteria=["query_processed"]
            )
        
        # Plan: Create intention
        intention = self.bdi_engine.plan(desire)
        
        # Execute: Process through appropriate tier
        result = await self._execute_intention(intention, query, context)
        
        # Update beliefs with result
        self.bdi_engine.perceive({"execution_result": result})
        
        return result
    
    async def _execute_intention(self, intention: Intention, 
                               query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """Execute intention using appropriate processing tier"""
        tier = intention.tier_requirement
        
        try:
            if tier == ProcessingTier.OFFLINE:
                return await self._process_offline(query, context)
            elif tier == ProcessingTier.TACTICAL:
                return await self._process_tactical(query, context)
            elif tier == ProcessingTier.STRATEGIC:
                return await self._process_strategic(query, context, intention)
            else:
                raise ValueError(f"Unknown processing tier: {tier}")
                
        except Exception as e:
            # Fallback to lower tier
            if tier == ProcessingTier.STRATEGIC:
                return await self._process_tactical(query, context)
            elif tier == ProcessingTier.TACTICAL:
                return await self._process_offline(query, context)
            else:
                return {
                    "enhanced_query": query,
                    "error": str(e),
                    "fallback": True,
                    "processing_tier": "offline"
                }
    
    async def _process_offline(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """Process query using offline agent"""
        result = await self.offline_agent.ainvoke({
            "input": query,
            "context": json.dumps(context)
        })
        
        return {
            "enhanced_query": result["output"],
            "processing_tier": "offline",
            "metadata": {
                "model": "llama-3.3-70b",
                "tools_used": result.get("intermediate_steps", []),
                "latency_ms": self._measure_latency()
            }
        }
    
    async def _process_tactical(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """Process query using tactical agent with tools"""
        # Enhance context with MCP tools
        enhanced_context = await self._gather_tactical_context(query, context)
        
        result = await self.tactical_agent.ainvoke({
            "input": query,
            "context": json.dumps(enhanced_context)
        })
        
        return {
            "enhanced_query": result["output"],
            "processing_tier": "tactical",
            "metadata": {
                "model": result.get("model_used", "claude-3-sonnet"),
                "tools_used": result.get("intermediate_steps", []),
                "context_sources": enhanced_context.get("sources", []),
                "latency_ms": self._measure_latency()
            }
        }
    
    async def _process_strategic(self, query: str, context: Dict[str, Any], 
                               intention: Intention) -> Dict[str, Any]:
        """Process query using multi-agent coordination"""
        # Prepare task for group chat
        task = f"""
        Enhance the following query through collaborative analysis:
        
        Query: {query}
        Context: {json.dumps(context)}
        
        Requirements:
        1. Analyze intent and complexity
        2. Build comprehensive context
        3. Apply enhancement strategies
        4. Validate quality and security
        
        Provide a final enhanced query with detailed metadata.
        """
        
        # Run group chat
        chat_result = self.strategic_coordinator.initiate_chat(
            message=task,
            clear_history=False
        )
        
        # Extract and format result
        enhanced_query = self._extract_enhanced_query(chat_result)
        
        return {
            "enhanced_query": enhanced_query,
            "processing_tier": "strategic",
            "metadata": {
                "agents_involved": ["prompt_analyst", "context_builder", 
                                  "enhancement_specialist", "quality_validator"],
                "rounds": len(chat_result.chat_history),
                "consensus_reached": True,
                "latency_ms": self._measure_latency()
            }
        }
    
    async def _gather_tactical_context(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """Gather enhanced context using MCP tools"""
        enhanced_context = context.copy()
        
        # Query Context7 for relevant documentation
        if self._should_use_context7(query):
            context7_results = await self.mcp_client.call_tool(
                "context7",
                "search",
                {"query": query, "limit": 5}
            )
            enhanced_context["documentation"] = context7_results
        
        # Apply sequential thinking for complex queries
        if self._is_complex_query(query):
            thinking_results = await self.mcp_client.call_tool(
                "sequential_thinking",
                "analyze",
                {"query": query, "steps": 3}
            )
            enhanced_context["reasoning_chain"] = thinking_results
        
        return enhanced_context
    
    def _create_fallback_chain(self, primary_executor: AgentExecutor, 
                             fallback_llm: Any, tools: List[Tool]) -> Any:
        """Create chain with fallback logic"""
        async def fallback_wrapper(inputs: Dict[str, Any]) -> Dict[str, Any]:
            try:
                # Try primary executor
                result = await primary_executor.ainvoke(inputs)
                result["model_used"] = "claude-3-sonnet"
                return result
            except Exception as e:
                # Fallback to secondary model
                fallback_agent = create_structured_chat_agent(fallback_llm, tools, primary_executor.agent.prompt)
                fallback_executor = AgentExecutor(agent=fallback_agent, tools=tools, memory=self.memory)
                result = await fallback_executor.ainvoke(inputs)
                result["model_used"] = "gpt-4-turbo"
                result["fallback_reason"] = str(e)
                return result
        
        return fallback_wrapper
    
    def _check_connectivity(self) -> Dict[str, Any]:
        """Check current connectivity status"""
        # Simplified connectivity check
        return {
            "online": True,  # Would check actual network
            "latency_ms": 50,
            "bandwidth_mbps": 100
        }
    
    def _should_use_context7(self, query: str) -> bool:
        """Determine if Context7 should be used"""
        technical_indicators = ["code", "api", "implement", "documentation"]
        return any(indicator in query.lower() for indicator in technical_indicators)
    
    def _is_complex_query(self, query: str) -> bool:
        """Check if query requires complex reasoning"""
        complexity_indicators = [
            len(query.split()) > 50,
            query.count("?") > 2,
            any(word in query.lower() for word in ["analyze", "compare", "evaluate"])
        ]
        return sum(complexity_indicators) >= 2
    
    def _extract_enhanced_query(self, chat_result: Any) -> str:
        """Extract enhanced query from group chat result"""
        # Look for the final enhanced query in chat history
        for message in reversed(chat_result.chat_history):
            if "enhanced query:" in message.get("content", "").lower():
                # Extract the enhanced query portion
                content = message["content"]
                start = content.lower().find("enhanced query:") + len("enhanced query:")
                return content[start:].strip()
        
        # Fallback to last message
        return chat_result.chat_history[-1].get("content", "")
    
    def _measure_latency(self) -> float:
        """Measure processing latency"""
        # In production, would track actual timing
        return 250.0  # Mock latency in ms
    
    async def _local_knowledge_search(self, query: str) -> str:
        """Search local knowledge base"""
        # Integration with offline knowledge base from previous artifact
        return f"Local knowledge results for: {query}"
    
    async def _pattern_match(self, query: str) -> str:
        """Match query patterns for enhancement"""
        patterns = {
            "how to": "Add step-by-step instructions and examples",
            "explain": "Include definitions and analogies",
            "compare": "Create comparison table with pros/cons"
        }
        
        for pattern, enhancement in patterns.items():
            if pattern in query.lower():
                return enhancement
        
        return "No specific pattern matched"
    
    async def _context7_search(self, query: str) -> str:
        """Search Context7 documentation"""
        # Mock implementation - would use actual MCP
        return f"Context7 documentation for: {query}"
    
    async def _sequential_thinking(self, query: str) -> str:
        """Apply sequential thinking"""
        # Mock implementation - would use actual MCP
        return f"Sequential analysis: 1) Parse intent 2) Identify requirements 3) Structure response"
    
    async def _hybrid_rag_search(self, query: str) -> str:
        """Search hybrid RAG system"""
        # Mock implementation - would integrate with Mem0
        return f"RAG results with relevant context for: {query}"


# Example usage and demonstration
async def demonstrate_unified_framework():
    """Demonstrate the unified agent framework"""
    
    # Configuration
    config = {
        "models": {
            "llama_path": "/models/llama-3.3-70b-q4.gguf",
            "claude_api_key": "your-claude-key",
            "openai_api_key": "your-openai-key"
        },
        "mcp_servers": {
            "context7": "mcp://context7.upstash.io",
            "sequential_thinking": "mcp://sequential.anthropic.com"
        }
    }
    
    # Initialize framework
    framework = UnifiedAgentFramework(config)
    
    # Test scenarios
    scenarios = [
        {
            "name": "Simple Enhancement (Offline)",
            "query": "How to implement a binary search?",
            "context": {"user_role": "developer", "connectivity": False}
        },
        {
            "name": "Tactical Enhancement (Online)",
            "query": "Design a secure microservices architecture for fintech",
            "context": {"user_role": "architect", "domain": "fintech", "security_level": "high"}
        },
        {
            "name": "Strategic Multi-Agent (Complex)",
            "query": "Analyze the trade-offs between different ML deployment strategies for edge computing in military applications",
            "context": {"user_role": "ml_engineer", "domain": "military", "classification": "SECRET"}
        }
    ]
    
    for scenario in scenarios:
        print(f"\n{'='*60}")
        print(f"Scenario: {scenario['name']}")
        print(f"Query: {scenario['query']}")
        print(f"Context: {scenario['context']}")
        print(f"{'='*60}")
        
        # Force offline for first scenario
        if "Offline" in scenario["name"]:
            framework.bdi_engine.beliefs.append(Belief(
                timestamp=datetime.utcnow(),
                belief_type="connectivity",
                content={"online": False},
                confidence=1.0,
                source="system"
            ))
        
        # Process query
        result = await framework.process_query(scenario["query"], scenario["context"])
        
        print(f"\nEnhanced Query: {result['enhanced_query']}")
        print(f"Processing Tier: {result['processing_tier']}")
        print(f"Metadata: {json.dumps(result['metadata'], indent=2)}")
        
        # Clear connectivity belief for next test
        framework.bdi_engine.beliefs = [b for b in framework.bdi_engine.beliefs 
                                       if b.belief_type != "connectivity"]


# Production deployment wrapper
class AIPEAProductionAgent:
    """
    Production-ready wrapper for the AI Prompt Engineer Agent
    Includes monitoring, error handling, and Agora integration
    """
    
    def __init__(self, agora_config: Dict[str, Any]):
        self.agent_id = agora_config.get("agent_id", "prompt_engineer_001")
        self.framework = UnifiedAgentFramework(agora_config)
        self.message_bus = self._init_message_bus(agora_config)
        self.metrics_collector = self._init_metrics()
        
    def _init_message_bus(self, config: Dict[str, Any]) -> Any:
        """Initialize Agora message bus connection"""
        # Would connect to actual Agora message bus
        return {"connected": True}
    
    def _init_metrics(self) -> Any:
        """Initialize metrics collection"""
        return {
            "queries_processed": 0,
            "average_latency": 0,
            "tier_distribution": {0: 0, 1: 0, 2: 0}
        }
    
    async def handle_agora_message(self, message: Dict[str, Any]) -> Dict[str, Any]:
        """Handle incoming Agora messages"""
        try:
            # Extract query and context from Agora message
            query = message["payload"]["query"]
            context = message["payload"].get("context", {})
            
            # Add Agora-specific context
            context["source_agent"] = message["source"]
            context["correlation_id"] = message.get("correlation_id")
            context["security_classification"] = message.get("security_classification", "UNCLASSIFIED")
            
            # Process through framework
            result = await self.framework.process_query(query, context)
            
            # Update metrics
            self._update_metrics(result)
            
            # Format response for Agora
            return {
                "source": self.agent_id,
                "destination": message["source"],
                "message_type": "ENHANCED_QUERY_RESPONSE",
                "correlation_id": message.get("correlation_id"),
                "payload": result,
                "timestamp": datetime.utcnow().isoformat()
            }
            
        except Exception as e:
            # Error handling with graceful degradation
            return {
                "source": self.agent_id,
                "destination": message["source"],
                "message_type": "ERROR_RESPONSE",
                "correlation_id": message.get("correlation_id"),
                "payload": {
                    "error": str(e),
                    "fallback_query": message["payload"]["query"],
                    "processing_tier": "error_recovery"
                },
                "timestamp": datetime.utcnow().isoformat()
            }
    
    def _update_metrics(self, result: Dict[str, Any]):
        """Update performance metrics"""
        self.metrics_collector["queries_processed"] += 1
        
        # Update latency
        latency = result["metadata"].get("latency_ms", 0)
        current_avg = self.metrics_collector["average_latency"]
        count = self.metrics_collector["queries_processed"]
        self.metrics_collector["average_latency"] = (current_avg * (count - 1) + latency) / count
        
        # Update tier distribution
        tier_map = {"offline": 0, "tactical": 1, "strategic": 2}
        tier = tier_map.get(result["processing_tier"], 1)
        self.metrics_collector["tier_distribution"][tier] += 1
    
    def get_health_status(self) -> Dict[str, Any]:
        """Get agent health status for monitoring"""
        return {
            "agent_id": self.agent_id,
            "status": "healthy",
            "metrics": self.metrics_collector,
            "capabilities": {
                "offline_ready": True,
                "mcp_connected": bool(self.framework.mcp_client),
                "models_loaded": True
            },
            "timestamp": datetime.utcnow().isoformat()
        }


if __name__ == "__main__":
    # Run demonstration
    asyncio.run(demonstrate_unified_framework())
