\u4e0a\u8a18\u306e\u6e96\u5099\u304c\u5b8c\u4e86\u3057\u305f\u3089\u30bf\u30fc\u30df\u30ca\u30eb\u3092\u958b\u304d\u3001\u4eee\u60f3\u74b0\u5883\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002<\/p>\n
# \u4f5c\u696d\u7528\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u3092\u4f5c\u6210\u30fb\u79fb\u52d5\nmkdir \ncd \n\n# \u4eee\u60f3\u74b0\u5883\u3092\u4f5c\u6210\npython3 -m venv \n\n# \u4eee\u60f3\u74b0\u5883\u306e\u30a2\u30af\u30c6\u30a3\u30d9\u30fc\u30c8\nsource \/bin\/activate<\/pre>\n\u7d9a\u3044\u3066\u5fc5\u8981\u306a\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u307e\u3059\u3002
\u4eca\u56de\u306fOpenAI API\u30ad\u30fc\u3092\u4f7f\u7528\u3059\u308b\u306e\u3067openai\u30d1\u30c3\u30b1\u30fc\u30b8\u3082\u4e00\u7dd2\u306b\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u307e\u3059\u3002<\/p>\n
pip install \"strands-agents[openai]\"<\/pre>\n\u6b21\u306b\u3001OpenAI\u306eAPI\u30ad\u30fc\u3092\u74b0\u5883\u5909\u6570\u306b\u8a2d\u5b9a\u3057\u307e\u3059\u3002
\u3054\u81ea\u8eab\u3067\u304a\u6301\u3061\u306eAPI\u30ad\u30fc\u3092\u8a2d\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n
export OPENAI_API_KEY=\"sk-xxxxxxxxxxxxxxxxxxxx\"<\/pre>\nStrands Agents\u304cOpenAI API\u30ad\u30fc\u3092\u4f7f\u7528\u3059\u308b\u3088\u3046\u306b\u306a\u3063\u3066\u3044\u308b\u304b\u78ba\u8a8d\u3057\u307e\u3059\u3002
\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u3067Python\u30a4\u30f3\u30bf\u30d7\u30ea\u30bf\u3092\u8d77\u52d5\u3057\u307e\u3059\u3002<\/p>\n
python3<\/pre>\n\u4ee5\u4e0b\u306e\u30b3\u30fc\u30c9\u3092\u4e00\u884c\u305a\u3064\u5165\u529b\u3057\u3066Enter\u30ad\u30fc\u3092\u62bc\u3057\u307e\u3059\u3002<\/p>\n
>>> from strands import Agent\n>>> agent = Agent()\n>>> print(type(agent.model))<\/pre>\n\u3059\u308b\u3068\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u51fa\u529b\u3055\u308c\u308b\u306f\u305a\u3067\u3059\u3002<\/p>\n
<\/pre>\n\u3053\u308c\u306fStrands Agents\u304c\u30c7\u30d5\u30a9\u30eb\u30c8\u3067BedrockModel\u3092\u4f7f\u7528\u3059\u308b\u3088\u3046\u306b\u306a\u3063\u3066\u3044\u308b\u305f\u3081\u3067\u3059\u3002
\u3053\u306e\u8a2d\u5b9a\u3092\u7de8\u96c6\u3057\u3001OpenAI API\u30ad\u30fc\u3092\u4f7f\u7528\u3059\u308b\u3088\u3046\u306b\u3057\u307e\u3059\u3002<\/p>\n
>>> import os\n>>> from strands import Agent\n>>> from strands.models.openai import OpenAIModel\n>>> openai_model = OpenAIModel(model_id=\"gpt-4o\")\n>>> agent = Agent(model=openai_model)\n>>> print(type(agent.model))<\/pre>\n\u4e0a\u8a18\u3092\u5b9f\u884c\u3057\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u51fa\u529b\u304c\u3042\u308c\u3070\u8a2d\u5b9a\u5b8c\u4e86\u3067\u3059\u3002<\/p>\n
<\/pre>\n\u7c21\u5358\u306a\u52d5\u4f5c\u78ba\u8a8d\u3092\u884c\u3044\u307e\u3059\u3002
\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u30a8\u30e9\u30fc\u304c\u51fa\u306a\u3051\u308c\u3070\u554f\u984c\u3042\u308a\u307e\u305b\u3093\u3002<\/p>\n
>>> response = agent(\"\u65e5\u672c\u306e\u9996\u90fd\u306f\u3069\u3053\u3067\u3059\u304b\uff1f\")\n\u65e5\u672c\u306e\u9996\u90fd\u306f\u6771\u4eac\u3067\u3059<\/pre>\n\u4f5c\u6210\u3059\u308b\u30a8\u30fc\u30b8\u30a7\u30f3\u30c8\u306b\u3064\u3044\u3066<\/h4>\n
\u4eca\u56de\u306f\u3001\u30e6\u30fc\u30b6\u30fc\u306e\u62bd\u8c61\u7684\u306a\u8981\u4ef6\u304b\u3089\u81ea\u5f8b\u7684\u306bAWS\u30a4\u30f3\u30d5\u30e9\u3092\u8a2d\u8a08\u30fb\u69cb\u7bc9\u3059\u308bAI\u30a8\u30fc\u30b8\u30a7\u30f3\u30c8\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002
Strands Agents\u306f\u3001\u3053\u308c\u3089\u306e\u5de5\u7a0b\u3092\u300cAgent\u300d\u3068\u547c\u3070\u308c\u308b\u81ea\u5f8b\u7684\u306a\u5358\u4f4d\u306b\u5206\u5272\u3057\u3001\u305d\u308c\u305e\u308c\u3092\u9023\u643a\u3055\u305b\u308b\u3053\u3068\u3067\u5b9f\u73fe\u3067\u304d\u307e\u3059\u3002
\u5404\u5de5\u7a0b\u3092\u62c5\u5f53\u3059\u308bAgent\u306e\u5f79\u5272\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002<\/p>\n
\n- \n
User Facing Agent\uff08\u5bfe\u8a71\u30a8\u30fc\u30b8\u30a7\u30f3\u30c8\uff09
\u30e6\u30fc\u30b6\u30fc\u3068\u306e\u5bfe\u8a71\u30a4\u30f3\u30bf\u30fc\u30d5\u30a7\u30a4\u30b9\u3092\u62c5\u5f53\u3057\u307e\u3059\u3002
\u30e6\u30fc\u30b6\u30fc\u304b\u3089\u306e\u521d\u671f\u8981\u4ef6\u3092\u53d7\u3051\u53d6\u308a\u3001\u5fc5\u8981\u306b\u5fdc\u3058\u3066\u8ffd\u52a0\u306e\u8cea\u554f\uff08\u4f8b:\u300c\u60f3\u5b9a\u3059\u308b\u30a2\u30af\u30bb\u30b9\u6570\u306f\u3069\u306e\u304f\u3089\u3044\u3067\u3059\u304b\uff1f\u300d\u300c\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306f\u5fc5\u8981\u3067\u3059\u304b\uff1f\u300d\u306a\u3069\uff09\u3092\u6295\u3052\u304b\u3051\u3001\u8981\u4ef6\u3092\u5177\u4f53\u5316\u3057\u307e\u3059\u3002<\/p>\n<\/li>\n
- \n
Architect Agent\uff08\u8a2d\u8a08\u30a8\u30fc\u30b8\u30a7\u30f3\u30c8\uff09
\u5177\u4f53\u5316\u3055\u308c\u305f\u8981\u4ef6\u3092\u53d7\u3051\u53d6\u308a\u3001AWS\u306e\u30d9\u30b9\u30c8\u30d7\u30e9\u30af\u30c6\u30a3\u30b9\uff08\u30bb\u30ad\u30e5\u30ea\u30c6\u30a3\u3001\u30b3\u30b9\u30c8\u3001\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\uff09\u306b\u3082\u3068\u3065\u3044\u3066\u6700\u9069\u306a\u30a4\u30f3\u30d5\u30e9\u69cb\u6210\u3092\u8a2d\u8a08\u3057\u307e\u3059\u3002
\u3069\u306eAWS\u30b5\u30fc\u30d3\u30b9\u3092\u3069\u306e\u3088\u3046\u306a\u30d1\u30e9\u30e1\u30fc\u30bf\u30fc\u3067\u5229\u7528\u3059\u308b\u304b\u3092\u6c7a\u5b9a\u3057\u3001\u69cb\u6210\u6848\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002<\/p>\n<\/li>\n
- \n
Terraform Coder Agent\uff08\u30b3\u30fc\u30c9\u751f\u6210\u30a8\u30fc\u30b8\u30a7\u30f3\u30c8\uff09
Architect Agent\u304c\u4f5c\u6210\u3057\u305f\u30a4\u30f3\u30d5\u30e9\u69cb\u6210\u6848\u3092\u3001\u5b9f\u884c\u53ef\u80fd\u306aTerraform\u306eHCL\u30b3\u30fc\u30c9\u306b\u5909\u63db\u3057\u307e\u3059\u3002
VPC\u3001\u30b5\u30d6\u30cd\u30c3\u30c8\u3001\u30bb\u30ad\u30e5\u30ea\u30c6\u30a3\u30b0\u30eb\u30fc\u30d7\u306a\u3069\u3001\u5404\u30ea\u30bd\u30fc\u30b9\u306e\u5b9a\u7fa9\u3092\u8a18\u8ff0\u3057\u307e\u3059\u3002<\/p>\n<\/li>\n
- \n
Test Agent\uff08\u30c6\u30b9\u30c8\u30a8\u30fc\u30b8\u30a7\u30f3\u30c8\uff09
\u751f\u6210\u3055\u308c\u305fTerraform\u30b3\u30fc\u30c9\u304c\u610f\u56f3\u901a\u308a\u304b\u3064\u5b89\u5168\u306b\u52d5\u4f5c\u3059\u308b\u304b\u3092\u4e8b\u524d\u306b\u691c\u8a3c\u3057\u307e\u3059\u3002<\/p>\n<\/li>\n
- \n
Deployment Agent\uff08\u30c7\u30d7\u30ed\u30a4\u30a8\u30fc\u30b8\u30a7\u30f3\u30c8\uff09
\u30c6\u30b9\u30c8\u6e08\u307f\u306eTerraform\u30b3\u30fc\u30c9\u3092\u5b9f\u884c\u3057\u3001AWS\u4e0a\u306b\u30a4\u30f3\u30d5\u30e9\u3092\u69cb\u7bc9\u3057\u307e\u3059\u3002
terraform plan\u3067\u6700\u7d42\u78ba\u8a8d\u3092\u884c\u3063\u305f\u5f8c\u3001terraform apply\u3067\u30d7\u30ed\u30d3\u30b8\u30e7\u30cb\u30f3\u30b0\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002<\/p>\n<\/li>\n
- \n
Cleanup Agent\uff08\u524a\u9664\u30a8\u30fc\u30b8\u30a7\u30f3\u30c8\uff09
\u691c\u8a3c\u304c\u5b8c\u4e86\u3057\u305f\u30a4\u30f3\u30d5\u30e9\u30ea\u30bd\u30fc\u30b9\u3092\u3059\u3079\u3066\u524a\u9664\u3057\u3001\u4e0d\u8981\u306a\u30b3\u30b9\u30c8\u304c\u767a\u751f\u3057\u306a\u3044\u3088\u3046\u306b\u3057\u307e\u3059\u3002
terraform destroy\u30b3\u30de\u30f3\u30c9\u3092\u5b9f\u884c\u3057\u3066\u3001\u30d7\u30ed\u30d3\u30b8\u30e7\u30cb\u30f3\u30b0\u3055\u308c\u305f\u30ea\u30bd\u30fc\u30b9\u3092\u30af\u30ea\u30fc\u30f3\u30a2\u30c3\u30d7\u3057\u307e\u3059\u3002<\/p>\n<\/li>\n<\/ol>\n
\u4e0a\u8a18\u306e\u30a8\u30fc\u30b8\u30a7\u30f3\u30c8\u3092\u4f7f\u7528\u3057\u3001\u5168\u4f53\u306e\u30ef\u30fc\u30af\u30d5\u30ed\u30fc\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n
\n- \n
\u8981\u4ef6\u5b9a\u7fa9
\u30e6\u30fc\u30b6\u30fc\u304c\u2460User Facing Agent\u306b\u8981\u4ef6\u3092\u5165\u529b\u3057\u3001\u5bfe\u8a71\u3092\u901a\u3058\u3066\u8981\u4ef6\u304c\u5177\u4f53\u5316\u3055\u308c\u307e\u3059\u3002<\/p>\n<\/li>\n
- \n
\u8a2d\u8a08
\u2461Architect Agent\u304c\u8981\u4ef6\u3092\u3082\u3068\u306b\u30a4\u30f3\u30d5\u30e9\u3092\u8a2d\u8a08\u3057\u307e\u3059\u3002<\/p>\n<\/li>\n
- \n
\u30b3\u30fc\u30c9\u751f\u6210
\u2462Terraform Coder Agent\u304c\u8a2d\u8a08\u6848\u304b\u3089Terraform\u30b3\u30fc\u30c9\u3092\u751f\u6210\u3057\u307e\u3059\u3002<\/p>\n<\/li>\n
- \n
\u30c6\u30b9\u30c8
\u2463Test Agent\u304cterraform validate<\/code>\u3092\u5b9f\u884c\u3057\u3001\u751f\u6210\u3055\u308c\u305f\u30b3\u30fc\u30c9\u306e\u69cb\u6587\u306b\u30a8\u30e9\u30fc\u304c\u306a\u3044\u304b\u3092\u78ba\u8a8d\u3057\u307e\u3059\u3002
\u69cb\u6587\u30a8\u30e9\u30fc\u304c\u3042\u308c\u3070\u3001\u4fee\u6b63\u306e\u305f\u3081\u306b\u2462\u306e\u30b9\u30c6\u30c3\u30d7\u306b\u5dee\u3057\u623b\u3057\u307e\u3059\u3002<\/p>\n<\/li>\n- \n
\u30c7\u30d7\u30ed\u30a4
\u30c6\u30b9\u30c8\u3092\u901a\u904e\u3057\u305f\u30b3\u30fc\u30c9\u3092\u2464Deployment Agent\u304c\u53d7\u3051\u53d6\u308a\u307e\u3059\u3002
terraform plan<\/code>\u3067\u5b9f\u884c\u8a08\u753b\u3092\u30e6\u30fc\u30b6\u30fc\u306b\u63d0\u793a\u3057\u3001\u627f\u8a8d\u3092\u5f97\u3066\u304b\u3089terraform apply<\/code>\u3067\u30a4\u30f3\u30d5\u30e9\u3092\u69cb\u7bc9\u3057\u307e\u3059\u3002<\/p>\n<\/li>\n- \n
\u30af\u30ea\u30fc\u30f3\u30a2\u30c3\u30d7
\u4eca\u56de\u306f\u691c\u8a3c\u7528\u306e\u305f\u3081\u3001\u30c7\u30d7\u30ed\u30a4\u5f8c\u306b\u2465Cleanup Agent\u304cterraform destroy<\/code>\u3092\u5b9f\u884c\u3057\u3001\u4f5c\u6210\u3055\u308c\u305f\u30ea\u30bd\u30fc\u30b9\u3092\u3059\u3079\u3066\u524a\u9664\u3057\u307e\u3059\u3002<\/p>\n<\/li>\n<\/ol>\n\u5b9f\u88c5<\/h4>\n
\u4f5c\u696d\u306b\u5165\u308b\u524d\u306b\u4f5c\u696d\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u3092\u4f5c\u6210\u3057\u3066\u79fb\u52d5\u3057\u307e\u3059\u3002
\u4f5c\u696d\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u540d\u306f\u4efb\u610f\u306e\u3082\u306e\u3067\u554f\u984c\u3042\u308a\u307e\u305b\u3093\u3002<\/p>\n
mkdir \ncd <\/pre>\n\u4f5c\u6210\u3057\u305f\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u5185\u306brun_agents.py<\/code>\u3068\u3044\u3046\u30d5\u30a1\u30a4\u30eb\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002
\u4efb\u610f\u306e\u30c6\u30ad\u30b9\u30c8\u30a8\u30c7\u30a3\u30bf\u30fc\u3092\u958b\u304d\u3001\u4ee5\u4e0b\u3092\u8cbc\u308a\u4ed8\u3051\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n\nrun_agents.py<\/summary>\n<\/details>\nimport<\/span> os\nimport<\/span> subprocess\nimport<\/span> json\nimport<\/span> uuid\nfrom<\/span> strands import<\/span> Agent\nfrom<\/span> strands.models.openai import<\/span> OpenAIModel\n\n\nTERRAFORM_PROJECT_PATH = \".\/terraform_project\"<\/span>\nMAX_RETRIES = 3<\/span>\n\n\ntry<\/span>:\n openai_model = OpenAIModel(model_id=\"gpt-4o\"<\/span>)\n strands_agent = Agent(model=openai_model)\n print<\/span>(\"Strands Agent with OpenAI model initialized successfully.\"<\/span>)\nexcept<\/span> Exception<\/span> as<\/span> e:\n print<\/span>(f\"Strands Agent\u306e\u521d\u671f\u5316\u306b\u5931\u6557\u3057\u307e\u3057\u305f: {e}\"<\/span>)\n exit()\n\n\nclass<\/span> BaseAgent<\/span>:\n def<\/span> __init__<\/span>(self, name: str<\/span>, role: str<\/span>):\n self.name = name\n self.role = role\n def<\/span> _header<\/span>(self):\n print<\/span>(\"<\/span>\\n<\/span>\"<\/span> + \"=\"<\/span>*50<\/span>)\n print<\/span>(f\"Agent: {self.name}\"<\/span>)\n print<\/span>(f\"Role: {self.role}\"<\/span>)\n print<\/span>(\"=\"<\/span>*50<\/span>)\n def<\/span> run<\/span>(self, *args, **kwargs):\n raise<\/span> NotImplementedError<\/span>\n\n\nclass<\/span> UserFacingAgent<\/span>(BaseAgent):\n def<\/span> __init__<\/span>(self, main_agent: Agent):\n super<\/span>().__init__(\"User Facing Agent\"<\/span>, \"\u5bfe\u8a71\u3092\u901a\u3058\u3066\u3001\u30e6\u30fc\u30b6\u30fc\u306e\u66d6\u6627\u306a\u8981\u6c42\u3092\u660e\u78ba\u306a\u30a4\u30f3\u30d5\u30e9\u8981\u4ef6\u306b\u5b9a\u7fa9\u3057\u307e\u3059\u3002\"<\/span>)\n self.agent = main_agent\n\n def<\/span> run<\/span>(self) -> str<\/span>:\n self._header()\n conversation_str = \"\"<\/span>\n initial_request = input<\/span>(\"\u3069\u306e\u3088\u3046\u306a\u30a4\u30f3\u30d5\u30e9\u3092\u69cb\u7bc9\u3057\u305f\u3044\u3067\u3059\u304b\uff1f<\/span>\\n<\/span>> \"<\/span>)\n conversation_str += f\"\u30e6\u30fc\u30b6\u30fc: {initial_request}<\/span>\\n<\/span>\"<\/span>\n \n system_prompt = \"\"\"<\/span>\n \u3042\u306a\u305f\u306f\u3001\u975e\u5e38\u306b\u512a\u79c0\u306aIT\u30b3\u30f3\u30b5\u30eb\u30bf\u30f3\u30c8\u3067\u3059\u3002\u30e6\u30fc\u30b6\u30fc\u3068\u306e\u5bfe\u8a71\u3092\u901a\u3058\u3066\u3001\u66d6\u6627\u306a\u8981\u6c42\u3092\u660e\u78ba\u306a\u30a4\u30f3\u30d5\u30e9\u8981\u4ef6\u306b\u843d\u3068\u3057\u8fbc\u307f\u307e\u3059\u3002<\/span>\n # \u300c\u60c5\u5831\u5341\u5206\u300d\u3068\u5224\u65ad\u3059\u308b\u305f\u3081\u306e\u6700\u4f4e\u6761\u4ef6<\/span>\n - \u76ee\u7684<\/span>\n - \u7279\u5225\u306a\u8981\u4ef6<\/span>\n # \u5bfe\u8a71\u306e\u9032\u3081\u65b9<\/span>\n 1. \u5bfe\u8a71\u5c65\u6b74\u3092\u5206\u6790\u3057\u3001\u4e0a\u8a18\u306e\u6700\u4f4e\u6761\u4ef6\u3092\u6e80\u305f\u3057\u3066\u3044\u308b\u304b\u78ba\u8a8d\u3057\u307e\u3059\u3002<\/span>\n 2. \u4e0d\u8db3\u3057\u3066\u3044\u308b\u9805\u76ee\u304c\u3042\u308c\u3070\u3001\u305d\u308c\u3092\u660e\u3089\u304b\u306b\u3059\u308b\u305f\u3081\u306e\u7c21\u6f54\u306a\u8cea\u554f\u6587\u306e\u307f\u3092\u751f\u6210\u3057\u307e\u3059\u3002<\/span>\n 3. \u3059\u3079\u3066\u306e\u6761\u4ef6\u304c\u6e80\u305f\u3055\u308c\u305f\u3068\u78ba\u4fe1\u3067\u304d\u305f\u5834\u5408\u306b\u306e\u307f\u3001\u300cCOMPLETE\u300d\u3068\u3044\u3046\u5358\u8a9e\u3092\u5fdc\u7b54\u306b\u542b\u3081\u3001\u305d\u306e\u5f8c\u306b\u7b87\u6761\u66f8\u304d\u3067\u6700\u7d42\u7684\u306a\u8981\u4ef6\u306e\u6982\u8981\u3092\u8a18\u8ff0\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span>\n \"\"\"<\/span>\n while<\/span> True<\/span>:\n print<\/span>(\"<\/span>\\n<\/span>AI\u304c\u6b21\u306e\u8cea\u554f\u3092\u8003\u3048\u3066\u3044\u307e\u3059...\"<\/span>)\n try<\/span>:\n prompt_for_llm = f\"{system_prompt}<\/span>\\n\\n<\/span># \u5bfe\u8a71\u5c65\u6b74<\/span>\\n<\/span>{conversation_str}\"<\/span>\n \n response_obj = self.agent(prompt_for_llm)\n response_str = str<\/span>(response_obj)\n\n if<\/span> \"COMPLETE\"<\/span> in<\/span> response_str:\n summary_part = response_str.split(\"COMPLETE\"<\/span>, 1<\/span>)[1<\/span>]\n final_summary = summary_part.strip()\n print<\/span>(f\"<\/span>\\n<\/span>\u8981\u4ef6\u304c\u78ba\u5b9a\u3057\u307e\u3057\u305f\u3002\"<\/span>)\n return<\/span> final_summary\n else<\/span>:\n question = response_str\n user_answer = input<\/span>(\"> \"<\/span>)\n conversation_str += f\"AI: {question}<\/span>\\n<\/span>\u30e6\u30fc\u30b6\u30fc: {user_answer}<\/span>\\n<\/span>\"<\/span>\n except<\/span> Exception<\/span> as<\/span> e:\n print<\/span>(f\"\u5bfe\u8a71\u51e6\u7406\u4e2d\u306b\u30a8\u30e9\u30fc\u304c\u767a\u751f\u3057\u307e\u3057\u305f: {e}\"<\/span>)\n return<\/span> initial_request\n\nclass<\/span> ArchitectAgent<\/span>(BaseAgent):\n def<\/span> __init__<\/span>(self, main_agent: Agent):\n super<\/span>().__init__(\"Architect Agent\"<\/span>, \"\u8981\u4ef6\u306b\u6700\u9069\u306aAWS\u30a4\u30f3\u30d5\u30e9\u69cb\u6210\u3092\u8a2d\u8a08\u3001\u307e\u305f\u306f\u30a8\u30e9\u30fc\u306b\u57fa\u3065\u304d\u518d\u8a2d\u8a08\u3057\u307e\u3059\u3002\"<\/span>)\n self.agent = main_agent\n\n def<\/span> run<\/span>(self, requirement: str<\/span>, feedback_error: str<\/span> = None<\/span>) -> dict<\/span>:\n self._header()\n if<\/span> feedback_error:\n print<\/span>(\"\u524d\u56de\u306e\u8a2d\u8a08\u3067\u5b9f\u884c\u6642\u30a8\u30e9\u30fc\u304c\u78ba\u8a8d\u3055\u308c\u307e\u3057\u305f\u3002\u30a8\u30e9\u30fc\u3092\u57fa\u306b\u8a2d\u8a08\u3092\u6839\u672c\u304b\u3089\u898b\u76f4\u3057\u307e\u3059...\"<\/span>)\n else<\/span>:\n print<\/span>(f\"\u53d7\u3051\u53d6\u3063\u305f\u8981\u4ef6\u3092\u5206\u6790\u3057\u3001\u30a4\u30f3\u30d5\u30e9\u3092\u8a2d\u8a08\u3057\u307e\u3059...\"<\/span>)\n \n prompt_for_llm = f\"\"\"<\/span>\n \u3042\u306a\u305f\u306f\u975e\u5e38\u306b\u512a\u79c0\u306aAWS\u306e\u30bd\u30ea\u30e5\u30fc\u30b7\u30e7\u30f3\u30a2\u30fc\u30ad\u30c6\u30af\u30c8\u3067\u3059\u3002<\/span>\n \u30e6\u30fc\u30b6\u30fc\u306e\u8981\u6c42\u3092\u5206\u6790\u3057\u3001\u305d\u308c\u3092\u5b9f\u73fe\u3059\u308b\u305f\u3081\u306e\u6700\u9069\u306aAWS\u30a4\u30f3\u30d5\u30e9\u69cb\u6210\u3092JSON\u5f62\u5f0f\u3067\u63d0\u6848\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span>\n \u51fa\u529b\u306f\u3001\u8aac\u660e\u6587\u306a\u3069\u3092\u4e00\u5207\u542b\u3081\u305a\u3001\u5fc5\u305a```json ... ```\u3067\u56f2\u3093\u3060\u30b3\u30fc\u30c9\u30d6\u30ed\u30c3\u30af\u3068\u3057\u3066JSON\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306e\u307f\u3092\u8fd4\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span>\n # \u8a2d\u8a08\u306e\u57fa\u672c\u539f\u5247<\/span>\n 1. \u30e6\u30fc\u30b6\u30fc\u304c\u4e8b\u524d\u306b\u4f55\u304b\u7279\u5225\u306a\u30ea\u30bd\u30fc\u30b9\u3092\u6301\u3063\u3066\u3044\u308b\u3053\u3068\u3092\u524d\u63d0\u3068\u3057\u306a\u3044\u3067\u304f\u3060\u3055\u3044\u3002<\/span>\n 2. \u30e6\u30fc\u30b6\u30fc\u304c\u30c9\u30e1\u30a4\u30f3\u540d\u3092\u660e\u793a\u3057\u306a\u3044\u9650\u308a\u3001Route53\u30ea\u30bd\u30fc\u30b9\u3084\u3001\u305d\u308c\u306b\u4f9d\u5b58\u3059\u308b\u8a2d\u5b9a\u3092\u8a2d\u8a08\u306b\u542b\u3081\u306a\u3044\u3067\u304f\u3060\u3055\u3044\u3002<\/span>\n 3. \u30b0\u30ed\u30fc\u30d0\u30eb\u3067\u30e6\u30cb\u30fc\u30af\u306a\u540d\u524d\u3092\u8981\u6c42\u3059\u308b\u30ea\u30bd\u30fc\u30b9\u306b\u306f\u3001\u7af6\u5408\u3092\u907f\u3051\u308b\u305f\u3081 `{{random_suffix}}` \u30d7\u30ec\u30fc\u30b9\u30db\u30eb\u30c0\u30fc\u3092\u8a2d\u8a08\u306b\u542b\u3081\u3066\u304f\u3060\u3055\u3044\u3002<\/span>\n 4. AWS\u306e\u30c7\u30d5\u30a9\u30eb\u30c8\u8a2d\u5b9a\u306f\u30bb\u30ad\u30e5\u30ea\u30c6\u30a3\u3092\u6700\u512a\u5148\u306b\u3057\u3066\u3044\u307e\u3059\u3002\u30e6\u30fc\u30b6\u30fc\u306e\u8981\u6c42\u3092\u5b9f\u73fe\u3059\u308b\u305f\u3081\u306b\u3001\u3053\u308c\u3089\u306e\u30c7\u30d5\u30a9\u30eb\u30c8\u8a2d\u5b9a\u3092\u5909\u66f4\u3059\u308b\u5fc5\u8981\u304c\u3042\u308b\u5834\u5408\u306f\u3001\u305d\u306e\u5909\u66f4\u3082\u8a2d\u8a08\u306b\u542b\u3081\u3066\u304f\u3060\u3055\u3044\u3002<\/span>\n # \u30e6\u30fc\u30b6\u30fc\u8981\u6c42<\/span>\n {requirement}<\/span>\n \"\"\"<\/span>\n if<\/span> feedback_error:\n print<\/span>(f\"\u518d\u8a2d\u8a08\u306e\u6839\u62e0\u3068\u306a\u308b\u30a8\u30e9\u30fc:<\/span>\\n<\/span>---<\/span>\\n<\/span>{feedback_error}<\/span>\\n<\/span>---\"<\/span>)\n prompt_for_llm += f\"<\/span>\\n\\n<\/span># \u91cd\u8981<\/span>\\n<\/span>\u4ee5\u524d\u63d0\u6848\u3057\u305f\u8a2d\u8a08\u306f`terraform apply`\u3067\u4e0a\u8a18\u306e\u30a8\u30e9\u30fc\u3092\u51fa\u3057\u307e\u3057\u305f\u3002\u8a2d\u8a08\u305d\u306e\u3082\u306e\u306b\u554f\u984c\u304c\u3042\u308b\u53ef\u80fd\u6027\u304c\u9ad8\u3044\u3067\u3059\u3002\u30a8\u30e9\u30fc\u3068\u8a2d\u8a08\u539f\u5247\u3092\u57fa\u306b\u3001\u554f\u984c\u304c\u767a\u751f\u3057\u306a\u3044\u4ee3\u66ff\u306e\u8a2d\u8a08\u6848\u3092\u518d\u63d0\u6848\u3057\u3066\u304f\u3060\u3055\u3044\u3002\"<\/span>\n try<\/span>:\n response_obj = self.agent(prompt_for_llm)\n response_str = str<\/span>(response_obj)\n json_part = response_str.split(\"```json\"<\/span>)[1<\/span>].split(\"```\"<\/span>)[0<\/span>]\n design = json.loads(json_part)\n main_service = \"N\/A\"<\/span>\n if<\/span> isinstance<\/span>(design, dict<\/span>):\n if<\/span> \"main_service\"<\/span> in<\/span> design:\n main_service = design[\"main_service\"<\/span>]\n elif<\/span> \"Resources\"<\/span> in<\/span> design and<\/span> isinstance<\/span>(design[\"Resources\"<\/span>], dict<\/span>):\n main_service = next<\/span>(iter<\/span>(design[\"Resources\"<\/span>].values()), {}).get(\"Type\"<\/span>, \"N\/A\"<\/span>)\n\n print<\/span>(f\"LLM\u306b\u3088\u308b\u30a4\u30f3\u30d5\u30e9\u8a2d\u8a08\u304c\u5b8c\u4e86\u3057\u307e\u3057\u305f\u3002(\u4e3b\u8981\u30b5\u30fc\u30d3\u30b9: {main_service})\"<\/span>)\n return<\/span> design\n except<\/span> Exception<\/span> as<\/span> e:\n