HKGAI V3: Hong Kong's Super Agent Era Arrives with 10x Token Efficiency

# HKGAI V3 Hardware Abstraction Layer (Simplified Illustration)
class HardwareAdapter:
    """Unified Hardware Abstraction Interface"""
    
    SUPPORTED_CHIPS = {
        "nvidia_a100": {"vendor": "NVIDIA", "tflops": 312},
        "nvidia_h100": {"vendor": "NVIDIA", "tflops": 989},
        "huawei_ascend_910c": {"vendor": "Huawei", "tflops": 256},
        "amd_mi300x": {"vendor": "AMD", "tflops": 530}
    }
    
    def __init__(self, chip_type: str):
        if chip_type not in self.SUPPORTED_CHIPS:
            raise ValueError(f"Unsupported chip: {chip_type}")
        self.chip_type = chip_type
        self.config = self._load_chip_config(chip_type)
    
    def _load_chip_config(self, chip_type: str) -> dict:
        """Load chip-specific configuration"""
        return {
            "memory_bandwidth": self._get_bandwidth(chip_type),
            "optimal_batch_size": self._get_optimal_batch(chip_type),
            "quantization_precisions": self._get_supported_precisions(chip_type)
        }
    
    def prepare_model_weights(self, model_path: str) -> bytes:
        """Convert model weights for target chip"""
        if self.chip_type.startswith("nvidia"):
            return self._convert_to_cuda(model_path)
        elif self.chip_type.startswith("huawei"):
            return self._convert_to_ascend(model_path)
        return self._convert_to_onnx(model_path)
    
    def optimize_inference(self, model: "Model") -> "OptimizedModel":
        """Optimize inference for specific chip"""
        if self.chip_type == "huawei_ascend_910c":
            return HuaweiAscendOptimizer(model).apply()
        return GenericOptimizer(model).apply()

import asyncio
from dataclasses import dataclass
from typing import List, Dict, Optional
import jieba
import jieba.analyse
import opencc

@dataclass
class HongKongTextSample:
    """Hong Kong local text sample"""
    content: str
    language: str  # 'yue', 'zh-Hans', 'en'
    domain: str   # 'legal', 'finance', 'government', 'news', 'social'
    cultural_tags: List[str]
    toxicity_score: float
    source: str

class LocalDataProcessor:
    """Hong Kong local data processor"""
    
    def __init__(self):
        # Initialize traditional-simplified converter
        self.converter_s2t = opencc.OpenCC('s2t')
        self.converter_t2s = opencc.OpenCC('t2s')
        
        # Load Hong Kong variant dictionary
        jieba.set_dictionary('/models/dict/hk_variant.txt')
        jieba.initialize()
        
        # Load local vocabulary (Hong Kong expressions)
        self.hk_slang = self._load_hk_slang()
    
    def _load_hk_slang(self) -> Dict[str, str]:
        """Load Hong Kong slang dictionary"""
        return {
            "打完斋不要和尚": "to discard someone after using their help",
            "大只佬": "muscular man",
            "收工会": "knock off work",
            "俾面": "give face/respect",
            "出事": "something went wrong",
            "有心": "thank you for your kindness",
            "知啦": "I know",
            "搞掂": "settled/done",
            "好嘢": "excellent",
            "hea": "slack off"
        }
    
    async def process_text(self, text: str) -> HongKongTextSample:
        """Process single text sample"""
        # 1. Language detection & normalization
        lang = self.detect_language(text)
        normalized = self.normalize(text, lang)
        
        # 2. Traditional-simplified conversion (if needed)
        if lang == 'zh-Hant':
            normalized = self.converter_s2t.convert(normalized)
        
        # 3. Keyword extraction
        keywords = jieba.analyse.extract_tags(
            normalized, topK=20, withWeight=True
        )
        
        # 4. Cultural tag annotation
        cultural_tags = self.extract_cultural_tags(normalized)
        
        # 5. Domain classification
        domain = self.classify_domain(normalized)
        
        # 6. Compliance check
        toxicity = self.check_toxicity(normalized)
        
        return HongKongTextSample(
            content=normalized,
            language=lang,
            domain=domain,
            cultural_tags=cultural_tags,
            toxicity_score=toxicity,
            source="hk_corpora_v3"
        )
    
    async def build_training_dataset(
        self, 
        raw_sources: List[str],
        min_quality_score: float = 0.7
    ) -> List[HongKongTextSample]:
        """Build high-quality training dataset"""
        samples = []
        
        async for source_url in self._crawl_sources(raw_sources):
            async for text in self._extract_texts(source_url):
                sample = await self.process_text(text)
                if sample.toxicity_score < 0.1:
                    samples.append(sample)
                    
                if len(samples) % 1000 == 0:
                    await self._log_progress(len(samples))
        
        scored_samples = await self._quality_score(samples)
        return [
            s for s in scored_samples 
            if s['quality_score'] >= min_quality_score
        ]

package agentcore

import (
    "context"
    "fmt"
    "log"
    "sync"
    "time"
    
    "github.com/HKGAI-V3/pkg/orchestrator"
    "github.com/HKGAI-V3/pkg/memory"
    "github.com/HKGAI-V3/pkg/toolregistry"
)

// SuperAgentConfig Super Agent Configuration
type SuperAgentConfig struct {
    MaxExecutionTime   time.Duration // Max execution time (default 48 hours)
    CheckpointInterval time.Duration // Checkpoint save interval
    MaxRetries         int           // Max retries per step
    ContextWindow      int           // Context window size
}

// SuperAgent Core Super Agent
type SuperAgent struct {
    config       SuperAgentConfig
    llm          LLMInterface
    memory       *memory.LongTermMemory
    tools        *toolregistry.ToolRegistry
    orchestrator *orchestrator.TaskOrchestrator
    
    mu          sync.RWMutex
    state       AgentState
    checkpoints []*Checkpoint
}

// AgentState Agent execution state
type AgentState struct {
    CurrentTask      string
    TaskHistory      []TaskRecord
    ToolCalls        []ToolInvocation
    LLMInteractions  int
    TotalTokens      int64
    StartTime        time.Time
    LastCheckpoint   time.Time
}

// TaskRecord Task execution record
type TaskRecord struct {
    TaskID     string
    Description string
    Status     TaskStatus
    StartTime  time.Time
    EndTime    time.Time
    Result     string
    Error      error
}

// Core execution loop
func (sa *SuperAgent) Execute(ctx context.Context, goal string) (*ExecutionResult, error) {
    sa.mu.Lock()
    sa.state = AgentState{
        CurrentTask: goal,
        StartTime:   time.Now(),
    }
    sa.mu.Unlock()
    
    // Task decomposition
    subTasks, err := sa.orchestrator.Decompose(ctx, goal)
    if err != nil {
        return nil, fmt.Errorf("task decomposition failed: %w", err)
    }
    
    log.Printf("Task decomposed into %d subtasks", len(subTasks))
    
    // Execute subtasks one by one
    for i, task := range subTasks {
        select {
        case <-ctx.Done():
            return nil, ctx.Err()
        default:
        }
        
        log.Printf("Executing subtask [%d/%d]: %s", i+1, len(subTasks), task.Description)
        
        result, err := sa.executeTask(ctx, task)
        if err != nil {
            if task.RetryCount < sa.config.MaxRetries {
                task.RetryCount++
                log.Printf("Task execution failed, retrying (%d/%d): %v", 
                    task.RetryCount, sa.config.MaxRetries, err)
                i--
                continue
            }
            return nil, fmt.Errorf("task [%s] execution failed: %w", task.ID, err)
        }
        
        if err := sa.saveCheckpoint(ctx); err != nil {
            log.Printf("Checkpoint save failed: %v", err)
        }
        
        sa.recordTaskCompletion(task, result)
    }
    
    finalResult := sa.generateReport(ctx)
    
    return &ExecutionResult{
        Success:     true,
        TotalTime:   time.Since(sa.state.StartTime),
        TasksDone:   len(sa.state.TaskHistory),
        TokensUsed:  sa.state.TotalTokens,
        Output:      finalResult,
    }, nil
}

// executeTask Execute single task
func (sa *SuperAgent) executeTask(ctx context.Context, task *Task) (*TaskResult, error) {
    prompt := sa.buildPrompt(task)
    
    response, err := sa.llm.Chat(ctx, &LLMRequest{
        Messages:   sa.buildMessages(prompt),
        MaxTokens:  4096,
        Temperature: 0.7,
    })
    if err != nil {
        return nil, err
    }
    
    sa.mu.Lock()
    sa.state.TotalTokens += int64(response.Usage.TotalTokens)
    sa.state.LLMInteractions++
    sa.mu.Unlock()
    
    toolCalls := sa.parseToolCalls(response.Content)
    
    for _, call := range toolCalls {
        toolResult, err := sa.tools.Execute(ctx, call)
        if err != nil {
            return nil, fmt.Errorf("tool [%s] execution failed: %w", call.Name, err)
        }
        
        sa.memory.Store(&memory.MemoryEntry{
            Type:      memory.ToolResult,
            Content:   toolResult,
            Timestamp: time.Now(),
        })
    }
    
    return &TaskResult{
        TaskID: task.ID,
        Output: response.Content,
        Tools:  toolCalls,
    }, nil
}

// saveCheckpoint Periodic checkpoint save (supports resume after crash)
func (sa *SuperAgent) saveCheckpoint(ctx context.Context) error {
    sa.mu.RLock()
    stateCopy := sa.state
    sa.mu.RUnlock()
    
    checkpoint := &Checkpoint{
        ID:        fmt.Sprintf("cp_%d", time.Now().Unix()),
        Timestamp: time.Now(),
        State:     stateCopy,
    }
    
    return sa.persistCheckpoint(ctx, checkpoint)
}

// Restore from checkpoint
func (sa *SuperAgent) Restore(ctx context.Context, checkpointID string) error {
    checkpoint, err := sa.loadCheckpoint(ctx, checkpointID)
    if err != nil {
        return fmt.Errorf("checkpoint load failed: %w", err)
    }
    
    sa.mu.Lock()
    sa.state = checkpoint.State
    sa.mu.Unlock()
    
    log.Printf("Restored from checkpoint [%s], elapsed time: %s", 
        checkpointID, time.Since(sa.state.StartTime))
    
    return nil
}

import asyncio
from datetime import datetime, timedelta
from typing import Dict, List, Optional
from dataclasses import dataclass, field
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

@dataclass
class AutonomousTask:
    """Autonomous task description"""
    goal: str
    constraints: Dict[str, any] = field(default_factory=dict)
    deadline: Optional[datetime] = None
    callback_url: Optional[str] = None

@dataclass
class TaskProgress:
    """Task progress tracking"""
    task_id: str
    current_phase: str
    completed_steps: List[str]
    pending_steps: List[str]
    elapsed_time: timedelta
    estimated_remaining: Optional[timedelta]
    intermediate_results: Dict[str, any]

class AutonomousRunner:
    """Autonomous operation runner"""
    
    def __init__(self, agent, checkpoint_manager, notifier):
        self.agent = agent
        self.checkpoint_manager = checkpoint_manager
        self.notifier = notifier
        self.max_single_run = timedelta(hours=28)
        self.checkpoint_interval = timedelta(minutes=30)
    
    async def run_autonomous(
        self, 
        task: AutonomousTask,
        progress_callback: Optional[callable] = None
    ) -> Dict:
        """
        Launch autonomous operation mode
        
        This is a typical "empowerment" scenario: after the user provides a goal,
        the system will:
        1. Automatically plan the execution path
        2. Execute step by step according to plan
        3. Make autonomous decisions when encountering problems 
           (retry/skip/adjust strategy)
        4. Report automatically upon completion
        
        No user intervention required throughout.
        """
        start_time = datetime.now()
        progress = TaskProgress(
            task_id=task.goal[:32],
            current_phase="Planning",
            completed_steps=[],
            pending_steps=[],
            elapsed_time=timedelta(0),
            estimated_remaining=None,
            intermediate_results={}
        )
        
        logger.info(f"🟢 Autonomous task started: {task.goal}")
        
        try:
            # Phase 1: Deep Planning
            logger.info("📋 Phase 1: Deep task planning...")
            execution_plan = await self._deep_planning(task.goal)
            progress.current_phase = "Execution"
            progress.pending_steps = [s['description'] for s in execution_plan]
            
            # Phase 2: Step-by-step execution (Core loop)
            for step_idx, step in enumerate(execution_plan):
                if datetime.now() - start_time > self.max_single_run:
                    logger.warning("⚠️ Approaching max runtime, saving checkpoint")
                    await self._emergency_checkpoint(progress, task)
                    break
                
                logger.info(f"🔄 Executing step [{step_idx+1}/{len(execution_plan)}]: {step['description']}")
                
                try:
                    step_result = await self._execute_step(step)
                    
                    progress.completed_steps.append(step['description'])
                    progress.pending_steps.pop(0)
                    progress.intermediate_results[step['id']] = step_result
                    progress.elapsed_time = datetime.now() - start_time
                    
                    if progress.elapsed_time % self.checkpoint_interval < timedelta(minutes=1):
                        await self.checkpoint_manager.save(progress)
                        logger.info(f"💾 Checkpoint saved (running for {progress.elapsed_time})")
                    
                    if progress_callback:
                        await progress_callback(progress)
                        
                except Exception as e:
                    logger.error(f"❌ Step execution failed: {e}")
                    recovery_action = await self._intelligent_recovery(step, e)
                    if recovery_action == "skip":
                        progress.completed_steps.append(f"{step['description']} [skipped]")
                        logger.warning(f"⏭️ Skipped failed step")
                    elif recovery_action == "retry":
                        step_idx -= 1
                        logger.info("🔁 Retrying current step")
            
            # Phase 3: Result Integration
            logger.info("📊 Phase 3: Integrating analysis results...")
            final_report = await self._compile_report(progress.intermediate_results)
            
            # Phase 4: Notification & Archival
            await self._finalize(task, final_report)
            
            elapsed = datetime.now() - start_time
            logger.info(f"✅ Task completed! Total time: {elapsed}")
            
            return {
                "status": "completed",
                "total_time": elapsed,
                "steps_completed": len(progress.completed_steps),
                "report": final_report
            }
            
        except Exception as e:
            logger.error(f"💥 Critical error: {e}")
            await self._handle_critical_failure(task, progress, e)
            raise

    async def _deep_planning(self, goal: str) -> List[Dict]:
        """Deep task planning: Let LLM autonomously design execution path"""
        planning_prompt = f"""
You are a professional task planning expert. The user's goal is:

{goal}

Please design a detailed multi-step execution plan. Each step should:
1. Have a clear specific objective
2. Be independently verifiable for completion
3. Produce intermediate results usable by subsequent steps

Please return the plan in JSON format as follows:
{{
    "steps": [
        {{
            "id": "step_1",
            "description": "Specific step description",
            "tools_needed": ["search", "analyze", "write"],
            "estimated_time": "30 minutes",
            "validation": "How to verify step completion"
        }}
    ],
    "total_estimated_time": "Estimated total time",
    "key_milestones": ["Key milestones"]
}}
"""
        response = await self.agent.chat(planning_prompt)
        import json
        return json.loads(response.content)['steps']

import numpy as np
from typing import List, Tuple, Optional
from dataclasses import dataclass

@dataclass
class CompressionConfig:
    """Compression configuration"""
    target_ratio: float = 0.1  # Target compression ratio: 10:1
    min_token_length: int = 2
    use_semantic_clustering: bool = True
    preserve_entities: bool = True

class TokenCompressor:
    """
    HKGAI V3 Token Compression Engine
    
    Core Technologies:
    1. Semantic Hashing: Map texts with similar meanings to same hash
    2. Dynamic Vocabulary: Adaptive adjustment based on domain
    3. Entity Protection: Ensure proper nouns aren't miscompressed
    4. Hierarchical Compression: Multi-level compression (sentence→paragraph→document)
    """
    
    def __init__(self, config: CompressionConfig):
        self.config = config
        self.semantic_vectors = self._load_semantic_model()
        self.entity_recognizer = self._init_entity_recognizer()
        self.dynamic_vocab = self._build_dynamic_vocab()
    
    def compress(self, text: str, context: Optional[dict] = None) -> str:
        """
        Main compression entry
        
        Strategy:
        1. Entity recognition & protection
        2. Semantic hashing & clustering
        3. Dynamic vocabulary replacement
        4. Context-aware coreference resolution
        """
        # Step 1: Entity recognition
        entities = self.entity_recognizer.extract(text)
        protected_ranges = [(e.start, e.end, e.type) for e in entities]
        
        # Step 2: Semantic hashing
        semantic_hash = self._compute_semantic_hash(text)
        
        # Step 3: Dynamic vocabulary compression
        compressed = self._dynamic_vocab_compress(text, context or {})
        
        # Step 4: Coreference resolution & compression
        compressed = self._resolve_and_compress_pronouns(
            compressed, context or {}
        )
        
        # Step 5: Restore entities
        compressed = self._restore_entities(compressed, entities)
        
        return compressed
    
    def _compute_semantic_hash(self, text: str) -> str:
        """
        Semantic Hashing: Map similar meaning texts to same hash
        
        Principle:
        - Use pretrained semantic embedding model to get text vector
        - Quantize vector to generate fixed-length hash
        - Similar texts will have same or similar hashes
        """
        embedding = self.semantic_vectors.encode(text)
        compressed = self._product_quantize(embedding, self.config.target_ratio)
        hash_str = self._vector_to_hash(compressed)
        return hash_str
    
    def _dynamic_vocab_compress(
        self, 
        text: str, 
        context: dict
    ) -> str:
        """
        Dynamic vocabulary compression
        
        Philosophy: Frequently co-occurring word pairs can be merged into one token
        Example: "Hong Kong" + "Special Administrative Region" -> "HK SAR"
        """
        compressed = text
        context_vocab = self._build_context_vocab(context)
        
        for phrase, replacement in sorted(
            context_vocab.items(), 
            key=lambda x: len(x[0]), 
            reverse=True
        ):
            if phrase in compressed and len(phrase) > self.config.min_token_length:
                compressed = compressed.replace(phrase, replacement)
        
        return compressed

class HierarchicalCompressor:
    """
    Hierarchical Compressor: Sentence → Paragraph → Document multi-level compression
    
    Core philosophy: Redundancy across hierarchy levels can be eliminated
    """
    
    def __init__(self, base_compressor: TokenCompressor):
        self.base = base_compressor
    
    def compress_document(
        self, 
        document: str,
        preserve_structure: bool = True
    ) -> str:
        """
        Document-level compression
        
        Strategy:
        1. Sentence-level summarization (extract key information)
        2. Paragraph-level condensation (eliminate intra-paragraph redundancy)
        3. Document-level integration (extract core themes)
        """
        paragraphs = document.split('\n\n')
        compressed_paragraphs = []
        
        for para in paragraphs:
            if len(para.strip()) < 50:
                continue
            compressed_para = self._compress_paragraph(para)
            compressed_paragraphs.append(compressed_para)
        
        if preserve_structure:
            return '\n\n'.join(compressed_paragraphs)
        return ' '.join(compressed_paragraphs)
    
    def _compress_paragraph(self, paragraph: str) -> str:
        """Paragraph-level compression"""
        sentences = self._split_sentences(paragraph)
        core_sentences = self._identify_core_sentences(sentences)
        
        compressed = []
        for sent in sentences:
            if sent in core_sentences:
                compressed.append(sent)
            else:
                summary = self._summarize_sentence(sent)
                compressed.append(summary)
        
        return ' '.join(compressed)

package agentruntime

import (
    "context"
    "runtime"
    "sync/atomic"
    "time"
    
    "github.com/HKGAI-V3/pkg/memory"
    "github.com/HKGAI-V3/pkg/resumable"
)

type RuntimeOptimizer struct {
    memoryManager    *memory.SmartMemoryManager
    checkpointMgr   *resumable.CheckpointManager
    resourceLimiter *ResourceLimiter
    
    totalRuns       int64
    successfulRuns  int64
    avgRunTime      time.Duration
    maxAchievedTime time.Duration
}

func (o *RuntimeOptimizer) OptimizeForLongRun(ctx context.Context, cfg *RuntimeConfig) {
    o.optimizeMemoryManagement()
    o.adjustResourceLimits(cfg)
    o.optimizeCacheStrategy(cfg.MaxDuration)
    o.tuneMonitoringThresholds(cfg)
}

func (o *RuntimeOptimizer) optimizeMemoryManagement() {
    o.memoryManager.SetPolicy(&memory.Policy{
        RecentWindow:     24 * time.Hour,
        MediumWindow:     7 * 24 * time.Hour,
        LongTermWindow:   30 * 24 * time.Hour,
        CompressionRatio: 0.1,
    })
    
    go o.scheduleProactiveGC()
}

func (o *RuntimeOptimizer) scheduleProactiveGC() {
    ticker := time.NewTicker(5 * time.Minute)
    defer ticker.Stop()
    
    for range ticker.C {
        var m runtime.MemStats
        runtime.ReadMemStats(&m)
        
        usagePercent := float64(m.Alloc) / float64(m.Sys) * 100
        if usagePercent > 70 {
            log.Printf("Memory usage: %.1f%%, triggering proactive GC", usagePercent)
            o.memoryManager.Compact()
            runtime.GC()
        }
    }
}

# Government Scenario: Regulation Interpretation & Policy Matching
class GovernmentDocAgent:
    """
    Hong Kong Government Regulation Intelligent Assistant
    
    Functions:
    1. Interpret Hong Kong legal provisions
    2. Match applicable preferential policies for enterprises
    3. Generate compliance recommendation reports
    """
    
    def __init__(self, hkgai_model):
        self.model = hkgai_model
        self.legal_knowledge = self._load_legal_kb()
        self.policy_db = self._load_policy_db()
    
    async def process_enterprise_request(self, enterprise_info: dict) -> dict:
        """
        Process enterprise consultation request
        
        Input: Enterprise basic info (industry, scale, business scope, etc.)
        Output: List of applicable regulations & policies + compliance recommendations
        """
        relevant_laws = await self._search_relevant_regulations(
            enterprise_info['industry'],
            enterprise_info['business_scope']
        )
        
        applicable_policies = await self._match_policies(
            enterprise_info,
            self.policy_db
        )
        
        risk_assessment = await self._assess_compliance_risks(
            enterprise_info,
            relevant_laws
        )
        
        report = await self._generate_compliance_report(
            enterprise_info=enterprise_info,
            regulations=relevant_laws,
            policies=applicable_policies,
            risks=risk_assessment
        )
        
        return report

# Finance Scenario: Market Analysis Report Generation
class FinanceReportAgent:
    """
    Financial Market Analysis Agent
    
    Features:
    - Can run continuously for hours for deep analysis
    - Automatically collects multi-source data
    - Generates structured analysis reports
    """
    
    async def generate_market_report(
        self, 
        market: str = "hk_stock",
        period: str = "1y",
        focus_areas: List[str] = None
    ) -> str:
        """
        Generate market analysis report
        
        The entire process can run continuously for hours
        without human intervention
        """
        if focus_areas is None:
            focus_areas = ["Trend Analysis", "Sector Rotation", "Risk Alerts"]
        
        # Phase 1: Data Collection
        market_data = await self._collect_market_data(market, period)
        news_data = await self._collect_news_sentiment(market)
        macro_data = await self._collect_macro_indicators(market)
        
        # Phase 2: Multi-dimensional Analysis
        analyses = {}
        for area in focus_areas:
            analyses[area] = await self._analyze_dimension(
                area, 
                market_data, 
                news_data,
                macro_data
            )
        
        # Phase 3: Comprehensive Assessment
        synthesis = await self._synthesize_findings(analyses)
        
        # Phase 4: Report Generation
        report = self._format_report(...)
        
        return report

// ClawNet: Super Agent Open Source Framework
// Enterprises can build customized AI Agents on this foundation

package clawnet

import (
    "context"
    "fmt"
    
    "github.com/HKGAI-V3/clawnet/pkg/agent"
    "github.com/HKGAI-V3/clawnet/pkg/tools"
    "github.com/HKGAI-V3/clawnet/pkg/memory"
)

type AgentFactory struct {
    toolRegistry  *tools.Registry
    memoryStore   *memory.Store
    llmProvider   LLMProvider
}

func (f *AgentFactory) NewCustomAgent(cfg *AgentConfig) (*agent.Agent, error) {
    ag := agent.New(agent.Options{
        LLM:           f.llmProvider.GetLLM(cfg.Model),
        Tools:         f.toolRegistry,
        Memory:        f.memoryStore,
        MaxSteps:      cfg.MaxSteps,
        Checkpoint:    cfg.EnableCheckpoint,
    })
    
    for _, skill := range cfg.CustomSkills {
        if err := ag.AddSkill(skill); err != nil {
            return nil, fmt.Errorf("skill addition failed [%s]: %w", skill.Name, err)
        }
    }
    
    return ag, nil
}

// Example: Building Enterprise Knowledge Base Q&A Agent
func Example_EnterpriseKB() {
    factory := NewAgentFactory()
    
    cfg := &AgentConfig{
        Model:           "hkgai-v3",
        MaxSteps:        50,
        EnableCheckpoint: true,
        CustomSkills: []Skill{
            {
                Name:        "kb_search",
                Description: "Search enterprise knowledge base",
                Handler:     tools.KBSearchHandler,
            },
            {
                Name:        "doc_process",
                Description: "Process various enterprise documents",
                Handler:     tools.DocProcessHandler,
            },
        },
    }
    
    myAgent, err := factory.NewCustomAgent(cfg)
    if err != nil {
        panic(err)
    }
    
    result, err := myAgent.Run(context.Background(), 
        "Please summarize this week's important company announcements and point out matters requiring my attention")
    if err != nil {
        panic(err)
    }
    
    fmt.Println(result)
}

# Hong Kong AI Dual-Track Strategy Implementation Framework
class HongKongAIStrategy:
    """
    Hong Kong AI Development Strategy Framework
    
    Track 1: AI Industrialization
    - Cultivate local AI enterprises
    - Build AI industrial parks
    - Improve AI entrepreneurship ecosystem
    
    Track 2: Industrial AI-ization
    - Promote digital transformation of traditional industries
    - Build AI public service platforms
    - Promote AI applications in finance, trade, logistics, etc.
    """
    
    def __init__(self):
        self.industries = [
            "FinTech",
            "International Trade",
            "Logistics & Shipping",
            "Professional Services",
            "Healthcare",
            "Creative Industries"
        ]
        
        self.ai_platforms = {
            "HKChat": "Citizen AI Assistant",
            "HKPilot": "Government Document Processing",
            "HKMeeting": "Smart Meeting Assistant",
            "HKLaw": "Legal Compliance Q&A",
            "HKEnv": "Environmental Monitoring",
            "HKMusic": "Music Creation"
        }
    
    def assess_industry_readiness(self, industry: str) -> dict:
        """Assess industry AI maturity"""
        indicators = {
            "FinTech": {"digitalization": 85, "AI_adoption": 60, "talent_pool": 70},
            "International Trade": {"digitalization": 65, "AI_adoption": 35, "talent_pool": 50},
            "Logistics & Shipping": {"digitalization": 75, "AI_adoption": 45, "talent_pool": 55},
        }
        return indicators.get(industry, {})

# Sovereign AI Capability Assessment Framework
class SovereignAICapability:
    """
    Sovereign AI Capability Assessment
    
    Assessment Dimensions:
    1. Compute Autonomy: Local chip ratio
    2. Data Sovereignty: Data localization degree
    3. Model Capability: Local LLM capabilities
    4. Talent Autonomy: AI talent reserves
    5. Governance Autonomy: AI regulatory capabilities
    """
    
    def evaluate(self, region: str) -> dict:
        """Assess a region's sovereign AI capabilities"""
        
        if region == "Hong Kong":
            return {
                "compute_independence": 65,
                "data_sovereignty": 80,
                "model_capability": 70,
                "talent_pool": 75,
                "governance": 85,
                "overall_score": 75,
                "recommendations": [
                    "Strengthen local chip R&D",
                    "Expand AI talent cultivation",
                    "Deepen Greater Bay Area computing cooperation"
                ]
            }
        
        return {}
    
    def benchmark_regions(self) -> List[dict]:
        """Benchmark against global major regions"""
        return [
            {"region": "USA", "score": 95, "strength": "Technology leadership"},
            {"region": "China", "score": 85, "strength": "Industrial completeness"},
            {"region": "EU", "score": 75, "strength": "Governance standards"},
            {"region": "Singapore", "score": 70, "strength": "Internationalization"},
            {"region": "Hong Kong", "score": 75, "strength": "Cross-border hub"}
        ]