# Kiroのスペック定義例
from kiro import AgentSpec, Tool, Memory
class CustomerSupportAgent(AgentSpec):
    """
    カスタマーサポート用AIエージェントの仕様
    """
name = "CustomerSupportAgent"
description = "24時間対応のカスタマーサポートエージェント"
# 必要な機能を宣言的に定義
required_capabilities = {
"natural_language": True,
"memory": "long_term",
"knowledge_base": True
}
# 利用可能なツールを定義
tools = [
Tool("ticket_system", "チケット管理システム連携"),
Tool("product_catalog", "商品カタログ検索"),
Tool("customer_history", "顧客履歴参照")
]
# メモリ設定
memory = Memory(
type="conversation",
retention_period="30d",
max_tokens=4000
)
# 行動制約の定義
constraints = {
"max_response_time": "30s",
"escalation_threshold": "confidence < 0.8",
"prohibited_actions": [
"financial_transactions",
"personal_data_modification"
]
}
def validate(self):
        """仕様の妥当性検証"""
# Kiroが自動でテストケースを生成
pass
# スペックからエージェントを生成
agent = CustomerSupportAgent.create()

┌─────────────────────────────────────────────────────────┐
│                    Kiro IDE                             │
│                                                         │
│  ┌─────────────┐    ┌──────────────┐    ┌────────────┐ │
│  │   Spec      │    │   Agent      │    │  Deploy    │ │
│  │   Editor    │──▶│   Builder    │──▶│  Manager   │ │
│  └─────────────┘    └──────────────┘    └────────────┘ │
│         ▲                   ▲                  ▲        │
│         │                   │                  │        │
│  ┌─────────────┐    ┌──────────────┐    ┌────────────┐ │
│  │    Test     │    │    Code      │    │   AWS      │ │
│  │  Generator  │    │  Generator   │    │ Integration │ │
│  └─────────────┘    └──────────────┘    └────────────┘ │
└─────────────────────────────────────────────────────────┘
│                   │                  │
▼                   ▼                  ▼
┌─────────────────┐    ┌──────────────┐    ┌────────────┐
│    Bedrock      │    │   Lambda     │    │    ECS     │
│    AgentCore    │    │   Function   │    │  Service   │
└─────────────────┘    └──────────────┘    └────────────┘

from kiro import AgentProject, AgentSpec, Tool
from kiro.tools import BedrockKnowledgeBase, OpenSearchVector
from kiro.memory import ConversationMemory
from kiro.hooks import pre_response, post_response
class TechnicalSupportAgent(AgentSpec):
    """
    技術サポート用AIエージェント
    """
def __init__(self):
self.name = "TechSupportAgent"
self.description = "AWS技術サポート特化型AIエージェント"
# ナレッジベースの設定
self.knowledge_base = BedrockKnowledgeBase(
name="aws-tech-docs",
sources=[
"s3://tech-docs/aws/*.md",
"s3://tech-docs/best-practices/*.md"
],
update_frequency="daily"
)
# ベクトル検索の設定
self.vector_search = OpenSearchVector(
index_name="tech-support-vectors",
embedding_model="amazon.titan-embed-text-v1"
)
# 会話メモリの設定
self.memory = ConversationMemory(
retention_period="7d",
index_strategy="semantic"
)
@pre_response
def validate_context(self, query, context):
        """応答前の文脈検証"""
if context.confidence < 0.8:
return self.escalate_to_human(query, context)
return context
@post_response
def log_interaction(self, query, response, context):
        """応答後のログ記録"""
self.analytics.log({
'query': query,
'response': response,
'confidence': context.confidence,
'sources': context.sources,
'timestamp': context.timestamp
})
# プロジェクト設定
project = AgentProject(
name="tech-support-agents",
version="1.0.0",
environment="production"
)
# エージェントの登録
agent = TechnicalSupportAgent()
project.register_agent(agent)
# デプロイ設定
deployment = project.create_deployment(
target="aws",
region="ap-northeast-1",
scaling={
'min_instances': 1,
'max_instances': 10,
'target_concurrent_executions': 100
}
)
# デプロイの実行
deployment.deploy()

from kiro import AgentChain, AgentSpec
from kiro.tools import BedrockChat, LambdaFunction
class ArchitectureReviewChain(AgentChain):
    """
    アーキテクチャレビュー用エージェントチェーン
    """
def __init__(self):
# 専門エージェントの定義
self.security_agent = AgentSpec(
name="SecurityReviewer",
model="anthropic.claude-3-sonnet",
tools=[
Tool("security_scanner"),
Tool("compliance_checker")
]
)
self.cost_agent = AgentSpec(
name="CostOptimizer",
model="anthropic.claude-3-sonnet",
tools=[
Tool("cost_calculator"),
Tool("resource_analyzer")
]
)
self.performance_agent = AgentSpec(
name="PerformanceAnalyzer",
model="anthropic.claude-3-sonnet",
tools=[
Tool("load_tester"),
Tool("metrics_analyzer")
]
)
# チェーンの構築
self.chain = [
self.security_agent,
self.cost_agent,
self.performance_agent
]
def review(self, architecture_spec):
        """アーキテクチャレビューの実行"""
results = []
for agent in self.chain:
result = agent.analyze(architecture_spec)
results.append(result)
# 重大な問題が見つかった場合は早期終了
if result.severity == "HIGH":
return self.generate_emergency_report(results)
return self.generate_full_report(results)
# 使用例
review_chain = ArchitectureReviewChain()
architecture = {
'service': 'e-commerce',
'components': [
{'type': 'web', 'tech': 'Next.js'},
{'type': 'api', 'tech': 'Lambda'},
{'type': 'db', 'tech': 'DynamoDB'}
]
}
report = review_chain.review(architecture)

from kiro import Tool, APISpec
from kiro.auth import OAuth2Client
from kiro.validators import ResponseValidator
class JiraIntegrationTool(Tool):
    """
    Jiraとの連携ツール
    """
def __init__(self):
self.api_spec = APISpec(
base_url="https://your-domain.atlassian.net",
auth=OAuth2Client(
client_id="your-client-id",
scope=["read", "write"]
)
)
self.validator = ResponseValidator(
required_fields=["key", "summary", "status"],
max_response_time=5000  # 5秒
)
def create_ticket(self, summary, description, priority="Medium"):
        """Jiraチケットの作成"""
response = self.api_spec.post(
"/rest/api/3/issue",
json={
"fields": {
"project": {"key": "TECH"},
"summary": summary,
"description": description,
"priority": {"name": priority},
"issuetype": {"name": "Task"}
}
}
)
self.validator.validate(response)
return response.json()
def update_status(self, ticket_key, status):
        """チケットステータスの更新"""
valid_statuses = ["To Do", "In Progress", "Done"]
if status not in valid_statuses:
raise ValueError(f"Invalid status. Must be one of: {valid_statuses}")
response = self.api_spec.post(
f"/rest/api/3/issue/{ticket_key}/transitions",
json={
"transition": {"name": status}
}
)
self.validator.validate(response)
return response.json()
# ツールの登録
jira_tool = JiraIntegrationTool()
agent.register_tool(jira_tool)

from kiro import Deployment, Monitoring, Alerts
from kiro.observability import MetricsCollector
from kiro.scaling import AutoScaler
class ProductionDeployment(Deployment):
    """
    本番環境デプロイメント設定
    """
def __init__(self, agent, environment="production"):
self.agent = agent
self.environment = environment
# モニタリング設定
self.monitoring = Monitoring(
metrics=[
"response_time",
"error_rate",
"token_usage",
"concurrent_requests"
],
dashboards=["performance", "cost", "quality"]
)
# アラート設定
self.alerts = Alerts(
conditions=[
{
"metric": "error_rate",
"threshold": 0.05,  # 5%以上でアラート
"window": "5m"
},
{
"metric": "response_time",
"threshold": 2000,  # 2秒以上でアラート
"window": "1m"
}
],
notifications=["slack", "email"]
)
# スケーリング設定
self.scaler = AutoScaler(
min_instances=2,
max_instances=10,
target_cpu_utilization=70,
scale_up_cooldown="3m",
scale_down_cooldown="5m"
)
def pre_deploy_checks(self):
        """デプロイ前チェック"""
checks = [
self.validate_configuration(),
self.test_integrations(),
self.verify_permissions(),
self.estimate_costs()
]
return all(checks)
def deploy(self):
        """デプロイの実行"""
if not self.pre_deploy_checks():
raise DeploymentError("Pre-deployment checks failed")
try:
# インフラのセットアップ
self.setup_infrastructure()
# エージェントのデプロイ
deployment_id = self.deploy_agent()
# モニタリングの有効化
self.monitoring.enable()
self.alerts.enable()
# スケーリングの設定
self.scaler.configure()
return {
'deployment_id': deployment_id,
'status': 'success',
'environment': self.environment,
'timestamp': datetime.now().isoformat()
}
except Exception as e:
self.rollback()
raise DeploymentError(f"Deployment failed: {str(e)}")
def rollback(self):
        """問題発生時のロールバック"""
# 前回の安定バージョンに戻す
self.revert_to_last_stable()
# アラートの通知
self.alerts.notify_team(
severity="high",
message="Deployment rollback initiated"
)
# デプロイメントの実行
deployment = ProductionDeployment(agent)
result = deployment.deploy()

# 収益計算クラス
class AIAgentBusinessModel:
def __init__(self):
self.development_costs = {
'initial_setup': 2000000,  # 初期設定費用
'agent_development': 3000000,  # エージェント開発費用
'training': 500000,  # トレーニング費用
'integration': 1000000  # システム統合費用
}
self.monthly_costs = {
'aws_infrastructure': 150000,  # AWSインフラ費用
'api_calls': 50000,  # API呼び出し費用
'maintenance': 200000,  # 保守運用費用
'support': 100000  # サポート費用
}
self.revenue_streams = {
'setup_fee': 3000000,  # 初期導入費
'monthly_subscription': 500000,  # 月額利用料
'api_usage': 100000,  # API利用料
'customization': 1000000  # カスタマイズ費用
}
def calculate_roi(self, months=12):
        """ROI計算"""
# 初期投資
initial_investment = sum(self.development_costs.values())
# 月間コスト
monthly_costs = sum(self.monthly_costs.values())
# 月間収益
monthly_revenue = (
self.revenue_streams['monthly_subscription'] +
self.revenue_streams['api_usage']
)
# 初期収益
initial_revenue = (
self.revenue_streams['setup_fee'] +
self.revenue_streams['customization']
)
# 期間収支
total_cost = initial_investment + (monthly_costs * months)
total_revenue = initial_revenue + (monthly_revenue * months)
# ROI計算
roi = ((total_revenue - total_cost) / total_cost) * 100
return {
'initial_investment': initial_investment,
'monthly_costs': monthly_costs,
'monthly_revenue': monthly_revenue,
'total_cost': total_cost,
'total_revenue': total_revenue,
'roi_percentage': roi,
'break_even_months': (initial_investment - initial_revenue) / (monthly_revenue - monthly_costs)
}
# 収益シミュレーション
model = AIAgentBusinessModel()
roi_analysis = model.calculate_roi(months=24)
print(f"2年間のROI分析:")
print(f"初期投資: ¥{roi_analysis['initial_investment']:,}")
print(f"月間コスト: ¥{roi_analysis['monthly_costs']:,}")
print(f"月間収益: ¥{roi_analysis['monthly_revenue']:,}")
print(f"ROI: {roi_analysis['roi_percentage']:.1f}%")
print(f"損益分岐点: {roi_analysis['break_even_months']:.1f}ヶ月")

# 簡単なMVP用のエージェントテンプレート
class MVPAgent(AgentSpec):
    """
    最小機能を備えたAIエージェント
    """
def __init__(self, domain):
self.domain = domain
self.capabilities = {
"chat": True,
"knowledge_base": True,
"api_integration": False,
"custom_actions": False
}
# 基本的なツールセット
self.tools = [
Tool("faq_lookup", "FAQ検索機能"),
Tool("ticket_creation", "問い合わせチケット作成")
]
def setup_knowledge_base(self, documents):
        """ナレッジベースの初期設定"""
kb = BedrockKnowledgeBase()
kb.add_documents(documents)
return kb
def configure_monitoring(self):
        """基本的なモニタリング設定"""
return {
"metrics": ["response_time", "user_satisfaction"],
"alerts": ["error_rate > 5%"]
}
# MVPのデプロイ
mvp = MVPAgent(domain="customer_support")
mvp.deploy(environment="staging")

from kiro.security import SecurityManager, DataEncryption
from kiro.compliance import ComplianceChecker
from kiro.audit import AuditLogger
class SecureAgentDevelopment:
    """
    セキュアなエージェント開発の基盤クラス
    """
def __init__(self):
self.security_manager = SecurityManager()
self.encryption = DataEncryption()
self.compliance = ComplianceChecker()
self.audit = AuditLogger()
def setup_security_controls(self):
        """セキュリティ制御の設定"""
controls = {
"data_encryption": {
"in_transit": True,
"at_rest": True,
"key_rotation": "30d"
},
"access_control": {
"authentication": "mfa",
"authorization": "rbac",
"session_timeout": 3600
},
"monitoring": {
"log_retention": "365d",
"alert_threshold": "critical"
}
}
self.security_manager.apply_controls(controls)
def validate_data_handling(self, data_flow):
        """データ処理の検証"""
checks = [
self.encryption.verify_encryption(data_flow),
self.compliance.check_gdpr_compliance(data_flow),
self.compliance.check_hipaa_compliance(data_flow)
]
return all(checks)
def audit_agent_actions(self, agent_id, timeframe):
        """エージェントの行動監査"""
audit_trail = self.audit.get_agent_actions(
agent_id=agent_id,
start_time=timeframe.start,
end_time=timeframe.end
)
return self.audit.analyze_actions(audit_trail)
# セキュリティ設定の適用
security = SecureAgentDevelopment()
security.setup_security_controls()

from kiro.performance import PerformanceOptimizer
from kiro.caching import ResponseCache
from kiro.metrics import MetricsCollector
class OptimizedAgent:
    """
    パフォーマンス最適化されたエージェント
    """
def __init__(self):
self.optimizer = PerformanceOptimizer()
self.cache = ResponseCache(
ttl="1h",
max_size="1GB"
)
self.metrics = MetricsCollector()
def optimize_response_time(self):
        """応答時間の最適化"""
optimizations = {
"caching": {
"frequent_queries": True,
"static_responses": True
},
"parallel_processing": {
"enabled": True,
"max_threads": 4
},
"resource_allocation": {
"memory": "2GB",
"cpu": "2vCPU"
}
}
self.optimizer.apply_optimizations(optimizations)
def monitor_performance(self):
        """パフォーマンスモニタリング"""
metrics = [
"response_time_p95",
"memory_usage",
"cpu_utilization",
"cache_hit_ratio"
]
return self.metrics.collect(metrics)
# 最適化の適用
agent = OptimizedAgent()
agent.optimize_response_time()

from kiro.workflow import GitWorkflow, CodeReview
from kiro.testing import TestSuite
from kiro.documentation import DocGenerator
class TeamDevelopmentWorkflow:
    """
    チーム開発ワークフロー管理
    """
def __init__(self):
self.git = GitWorkflow()
self.review = CodeReview()
self.tests = TestSuite()
self.docs = DocGenerator()
def setup_development_flow(self):
        """開発フローの設定"""
workflow = {
"branches": {
"main": "production",
"staging": "pre-production",
"develop": "integration",
"feature/*": "development"
},
"reviews": {
"required_approvals": 2,
"auto_assign": True
},
"tests": {
"unit": True,
"integration": True,
"performance": True
},
"documentation": {
"auto_generate": True,
"api_docs": True,
"usage_examples": True
}
}
return self.git.configure_workflow(workflow)
def create_feature_branch(self, feature_name):
        """機能開発ブランチの作成"""
branch = self.git.create_branch(f"feature/{feature_name}")
self.tests.setup_test_env(branch)
return branch
def submit_for_review(self, branch):
        """レビュー依頼の提出"""
diff = self.git.get_changes(branch)
reviewers = self.review.assign_reviewers(diff)
self.docs.update_documentation(diff)
return self.review.create_review_request(diff, reviewers)
# ワークフローの設定
workflow = TeamDevelopmentWorkflow()
workflow.setup_development_flow()

class CareerDevelopmentPlan:
def __init__(self):
self.skill_categories = {
"technical_skills": [
"AIエージェント開発",
"LLMプロンプトエンジニアリング",
"分散システム設計",
"セキュリティ・コンプライアンス",
"パフォーマンス最適化"
],
"business_skills": [
"AIソリューション提案",
"ROI分析・提案",
"プロジェクトマネジメント",
"チームリーダーシップ",
"クライアントコミュニケーション"
],
"emerging_skills": [
"マルチエージェントシステム",
"エージェント間協調",
"認知アーキテクチャ",
"エージェント倫理",
"説明可能AI"
]
}
def calculate_market_value(self, mastered_skills):
        """スキルベースの市場価値計算"""
skill_values = {
"AIエージェント開発": 1000000,  # 年収アップ額
"LLMプロンプトエンジニアリング": 800000,
"AIソリューション提案": 1200000,
"マルチエージェントシステム": 1500000
}
total_value_increase = sum(
skill_values.get(skill, 500000) for skill in mastered_skills
)
base_salary = 8000000  # ベース年収
projected_salary = base_salary + total_value_increase
return {
"base_salary": base_salary,
"skill_premium": total_value_increase,
"projected_salary": projected_salary,
"increase_percentage": (total_value_increase / base_salary) * 100
}
# キャリア価値計算
career_plan = CareerDevelopmentPlan()
my_skills = [
"AIエージェント開発",
"LLMプロンプトエンジニアリング",
"AIソリューション提案"
]
value_projection = career_plan.calculate_market_value(my_skills)
print("=== キャリア価値予測 ===")
print(f"ベース年収: {value_projection['base_salary']:,}円")
print(f"スキルプレミアム: {value_projection['skill_premium']:,}円")
print(f"予想年収: {value_projection['projected_salary']:,}円")
print(f"年収アップ率: {value_projection['increase_percentage']:.1f}%")