{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Soner Yıldırım",
  "title_listed": "I Replaced a 15-Minute Booking Process with a LangGraph AI Agent",
  "keywords": "",
  "domain": "大模型算法",
  "content_type": "深度技术解析",
  "is_paid_flag": "否",
  "url": "https://towardsdatascience.com/i-replaced-a-15-minute-booking-process-with-a-langgraph-ai-agent/",
  "publish_date": "2026-08-02",
  "corpus_stage": "",
  "notes": "",
  "id": "680de8d87dcb",
  "collected_at": "2026-10-02T09:24:05",
  "title": "I Replaced a 15-Minute Booking Process with a LangGraph AI Agent | Towards Data Science",
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It registers if a user is signed in to the Twitter platform and collects information about ad preferences.\n\n\n  * Cookie\nbcookie\n  * Duration\n1 year\n  * Description\nLinkedIn sets this cookie from LinkedIn share buttons and ad tags to recognize browser IDs.\n\n\n  * Cookie\n__Secure-ROLLOUT_TOKEN\n  * Duration\n6 months\n  * Description\nYouTube sets this cookie to manage feature rollout and experimentation. 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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\n# I Replaced a 15-Minute Booking Process with a LangGraph AI Agent\nA step-by-step guide to building, running, and monitoring a stateful customer support agent using Python, LangGraph, and Langfuse.\n[Soner Yıldırım](https://towardsdatascience.com/author/sonery/)\nAugust 2, 20266 min read\n![Photo by Towfiqu barbhuiya on Unsplash](../images/001.jpg)\n[Photo](https://unsplash.com/photos/person-in-blue-long-sleeve-shirt-sitting-beside-black-laptop-computer--9gPKrsbGmc) by Towfiqu barbhuiya on Unsplash\nI recently contacted a cleaning company to request a quote for cleaning a couch. They asked about its size and material and requested a picture of it. They also needed my address because travel time varies significantly across the city and therefore affects the price.\nAfter receiving the quote for the couch, I asked about apartment cleaning. This started another exchange of messages about the apartment’s size, the desired cleaning depth, and optional services. Once we agreed on a price, we chatted a little more to find a suitable appointment.\nThe entire booking process took around 15 minutes. This sounds like a problem worth handing to an AI agent.\nIn this article, we’ll build this agent using Python, LangGraph, and LangChain. We’ll create a demo for testing the complete booking flow and integrate Langfuse for observability.\nLearn this step by step with the interactive [AI Engineer](https://roadmap.sh/ai-engineer) roadmap.\nI also wrote an article about adding a proper UI with Streamlit for the customer service agent. Here is the article if you'd like to take a look at it as well:\n[![](../images/001.jpg)Building a Streamlit UI for My LangGraph AI AgentBuilding a production-ready web interface for a stateful LangGraph agentSoner Yıldırım · 6 min read](https://towardsdatascience.com/building-a-streamlit-ui-for-my-langgraph-ai-agent/)\nThe AI agent will not only speed up the process but also work 24/7 when deployed into production.\nPlease note that this is not a simple chatbot. It is a stateful AI agent capable of managing a multi-step business process by combining conversational intelligence with deterministic business rules.\nThe source code of the agent is available on GitHub at [customer-service-agent](https://github.com/SonerYldrm/customer-service-agent). Feel free to clone and test it yourself. \n### What does this agent do?\nThe agent does everything the customer-service representative of this business does:\n  * Responds to customer queries and understands their needs.\n  * Calculates the price for the service and informs the customer.\n  * Handles the customer’s acceptance or rejection.\n  * Proposes optimized time slots.\n  * Confirms and records the appointment.\n\n\n### Why an AI agent for this task?\nWe could build a quotation tool that asks customers a predefined set of questions. Once they approve the price, the system could automatically schedule an appointment.\nThis would work, but every customer would have to follow the same process.\nAn AI agent provides a more natural and flexible experience. For example, a customer can write:\n\"Hey! I need a cleaning service for my 2 bedroom apartment in this address. I also want you to clean inside of my refrigerator. I'm available on Tuesday and Wednesday.\". \nThis query includes all the required details so there is no need to ask any other question or make the customer enter the same information again through a form.\nThe AI agent can understand If all required information is already present and proceed directly to calculating and presenting a quote.\nThe AI agent can also handle the orchestration of any subsequent steps such as price calculation, handling customer’s decision, searching for optimized appointment times, and booking confirmation.\nIt also has the potential to support additional operations, such as notifying cleaners about new appointments or contacting nearby cleaners to find someone available for an urgent request.\n### Structure of the agent\nI created the drawing below to demonstrate the structure of our AI agent.\n![](../images/002.png)\nThe starting point is the interaction between a customer and AI agent. Chatting with the customer, AI agent tries to get all the information needed for the service.\nThe output of the first chat is booking or service details and the agent converts this to a structured output so that it can be used later on by the price and booking engines.\nThe `BookingDetails` I used in the first version is as follows:\npython\n\n```\nclass BookingDetails(BaseModel):\n    \"\"\"Information extracted from the conversation.\n\n    ``size_info`` is square footage for a house and seat count for a couch.\n    Fields are optional because this model also represents partial extraction.\n    \"\"\"\n\n    service_type: ServiceType | None = Field(default=None)\n    size_info: float | None = Field(default=None, gt=0)\n    cleaning_depth: CleaningDepth | None = Field(default=None)\n    add_ons: list[str] = Field(default_factory=list)\n    address: str | None = Field(default=None)\n    is_complete: bool = False\n    next_question: str | None = Field(\n        default=None,\n        exclude=True,\n        description=\"A concise question asking only for information still missing.\",\n    )\n```\n\nThe agent only asks a new question if there is some missing details. Thus, the agent decides what happens next, which is a key point that separates an AI agent from a workflow.\n### LangGraph\nWe use LangGraph because the booking workflow needs shared state and must resume across multiple customer messages. Thus, we need a stateful agent.\nThe state is a `TypedDict` as shown below:\npython\n\n```\nclass AgentState(TypedDict):\n    messages: Annotated[list[AnyMessage], add_messages]\n    booking_details: BookingDetails\n    calculated_price: NotRequired[float | None]\n    time_options: NotRequired[list[TimeOption]]\n    selected_slot: NotRequired[TimeOption | None]\n    status: BookingStatus\n    booking_id: NotRequired[str | None]\n```\n\nLangGraph manages state during graph execution. Nodes read the current state and return partial updates. Conditional routing functions inspect that state to decide which node runs next.\nWe can create the graph builder using this state schema:\npython\n\n```\nrom langgraph.graph import StateGraph\n\ngraph = StateGraph(AgentState)\n```\n\nThen, we start adding nodes and edges. For example, the following code snippet shows how we can create the price calculation node.\npython\n\n```\nfrom customer_service_agent.engines import calculate_price\n\ndef calculate_price_node(state: AgentState) -> dict[str, Any]:\n    return {\n        \"calculated_price\": calculate_price(\n            state[\"booking_details\"]\n        )\n    }\n\ngraph.add_node(\"calculate_price\", calculate_price_node)\n```\n\nThe price calculation node reads the extracted booking details and returns a partial state update.\nThe `calculate_price` is a function that calculates the price using the `BookingDetails` available in the `AgentState` .\nAfter the information-gathering node runs, a conditional routing function determines whether enough information is available to calculate a price:\npython\n\n```\ndef should_continue_to_pricing(\n    state: AgentState,\n) -> Literal[\"calculate_price\", \"end\"]:\n    if state[\"booking_details\"].is_complete:\n        return \"calculate_price\"\n\n    return \"end\"\n```\n\nWe connect that routing decision to the graph:\npython\n\n```\ngraph.add_conditional_edges(\n    \"gather_info\",\n    should_continue_to_pricing,\n    {\n        \"calculate_price\": \"calculate_price\",\n        \"end\": END,\n    },\n)\n```\n\nIf the booking details are complete, execution continues to the pricing node. Otherwise, the graph ends the current turn and waits for another customer message. \nThe remaining nodes and conditional routes are added in the same way. Finally, the graph is compiled with a checkpointer.\npython\n\n```\ngraph.compile(checkpointer=checkpointer or MemorySaver())\n```\n\n### Observability\nThe most critical component of any system is observability. Whether it's a production line at a factory, a traditional ML model to detect anomalies, or any AI-agent used in production.\nIf you do not know what goes on in your system, you just need to rely on pure luck to solve any issue. And it's certain that your production system will have issues.\nThere are many alternatives to use for observability of an AI agent. We will be using LangFuse, an open-source AI engineering platform designed for tracing, monitoring, evaluating, and debugging large language model (LLM) applications.\nThe free tier is enough to test our project. You just need to create the secret and public keys in LangFuse dashboard and paste them in your .env file:\npython\n\n```\nLANGFUSE_SECRET_KEY=\nLANGFUSE_PUBLIC_KEY=\nLANGFUSE_BASE_URL=\"https://cloud.langfuse.com\"\n```\n\nWe can create a langfuse handler as follows:\npython\n\n```\ndef create_langfuse_handler() -> Any | None:\n    \"\"\"Return a configured handler when credentials are available.\"\"\"\n    if not os.getenv(\"LANGFUSE_PUBLIC_KEY\") or not os.getenv(\"LANGFUSE_SECRET_KEY\"):\n        return None\n    from langfuse.langchain import CallbackHandler\n\n    return CallbackHandler()\n\ndef flush_langfuse(handler: Any | None) -> None:\n    \"\"\"Upload queued telemetry without making observability application-critical.\"\"\"\n    if handler is None:\n        return\n    try:\n        from langfuse import get_client\n\n        get_client().flush()\n    except Exception:\n        logger.exception(\"Failed to flush LangFuse telemetry\")\n```\n\nLangFuse dashboard shows every detail for us to debug and monitor our AI agent. We can see the prompts, inputs, and outputs. We also see the token usage and cost so it's very easy to detect if there is unexpected situation in our budget.\nLangFuse dashboard looks like this:\n![](../images/body-820095dc757be2ca2ca8.png)\nIt looks complicated at first but once you start using it, it becomes much easier.\n### What's next\nThe project includes a simple CLI to test the end-to-end booking workflow, but its modular design gives us plenty of room for improvement. \nYou need an OpenAI API Key to test it but it costs almost nothing. The screenshot below shows the cost of a single run:\n![](../images/body-4b2cd0d6b5c0cd8a566b.png)\nEvery component has access to the central agent state so the system can easily be extended with:\n  * Frontend interfaces such as web, mobile apps, or messaging platforms like WhatsApp.\n  * A production database like PostgreSQL.\n  * Direct connections to real employee calendars and location-based services.\n\n\nI consider this release as v0 and I'm planning to add more features and functionality on top of it.\nThanks for reading.\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\n# I Replaced a 15-Minute Booking Process with a LangGraph AI Agent\nA step-by-step guide to building, running, and monitoring a stateful customer support agent using Python, LangGraph, and Langfuse.\n[Soner Yıldırım](https://towardsdatascience.com/author/sonery/)\nAugust 2, 20266 min read\n![Photo by Towfiqu barbhuiya on Unsplash](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/08/towfiqu-barbhuiya-9gPKrsbGmc-unsplash-scaled-1.jpg)\n[Photo](https://unsplash.com/photos/person-in-blue-long-sleeve-shirt-sitting-beside-black-laptop-computer--9gPKrsbGmc) by Towfiqu barbhuiya on Unsplash\nI recently contacted a cleaning company to request a quote for cleaning a couch. They asked about its size and material and requested a picture of it. They also needed my address because travel time varies significantly across the city and therefore affects the price.\nAfter receiving the quote for the couch, I asked about apartment cleaning. This started another exchange of messages about the apartment’s size, the desired cleaning depth, and optional services. Once we agreed on a price, we chatted a little more to find a suitable appointment.\nThe entire booking process took around 15 minutes. This sounds like a problem worth handing to an AI agent.\nIn this article, we’ll build this agent using Python, LangGraph, and LangChain. We’ll create a demo for testing the complete booking flow and integrate Langfuse for observability.\nLearn this step by step with the interactive [AI Engineer](https://roadmap.sh/ai-engineer) roadmap.\nI also wrote an article about adding a proper UI with Streamlit for the customer service agent. Here is the article if you'd like to take a look at it as well:\n[![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/08/towfiqu-barbhuiya-9gPKrsbGmc-unsplash-scaled-1.jpg)Building a Streamlit UI for My LangGraph AI AgentBuilding a production-ready web interface for a stateful LangGraph agentSoner Yıldırım · 6 min read](https://towardsdatascience.com/building-a-streamlit-ui-for-my-langgraph-ai-agent/)\nThe AI agent will not only speed up the process but also work 24/7 when deployed into production.\nPlease note that this is not a simple chatbot. It is a stateful AI agent capable of managing a multi-step business process by combining conversational intelligence with deterministic business rules.\nThe source code of the agent is available on GitHub at [customer-service-agent](https://github.com/SonerYldrm/customer-service-agent). Feel free to clone and test it yourself. \n### What does this agent do?\nThe agent does everything the customer-service representative of this business does:\n  * Responds to customer queries and understands their needs.\n  * Calculates the price for the service and informs the customer.\n  * Handles the customer’s acceptance or rejection.\n  * Proposes optimized time slots.\n  * Confirms and records the appointment.\n\n\n### Why an AI agent for this task?\nWe could build a quotation tool that asks customers a predefined set of questions. Once they approve the price, the system could automatically schedule an appointment.\nThis would work, but every customer would have to follow the same process.\nAn AI agent provides a more natural and flexible experience. For example, a customer can write:\n\"Hey! I need a cleaning service for my 2 bedroom apartment in this address. I also want you to clean inside of my refrigerator. I'm available on Tuesday and Wednesday.\". \nThis query includes all the required details so there is no need to ask any other question or make the customer enter the same information again through a form.\nThe AI agent can understand If all required information is already present and proceed directly to calculating and presenting a quote.\nThe AI agent can also handle the orchestration of any subsequent steps such as price calculation, handling customer’s decision, searching for optimized appointment times, and booking confirmation.\nIt also has the potential to support additional operations, such as notifying cleaners about new appointments or contacting nearby cleaners to find someone available for an urgent request.\n### Structure of the agent\nI created the drawing below to demonstrate the structure of our AI agent.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/07/customer-service-agent.drawio.png)\nThe starting point is the interaction between a customer and AI agent. Chatting with the customer, AI agent tries to get all the information needed for the service.\nThe output of the first chat is booking or service details and the agent converts this to a structured output so that it can be used later on by the price and booking engines.\nThe `BookingDetails` I used in the first version is as follows:\npython\n\n```\nclass BookingDetails(BaseModel):\n    \"\"\"Information extracted from the conversation.\n\n    ``size_info`` is square footage for a house and seat count for a couch.\n    Fields are optional because this model also represents partial extraction.\n    \"\"\"\n\n    service_type: ServiceType | None = Field(default=None)\n    size_info: float | None = Field(default=None, gt=0)\n    cleaning_depth: CleaningDepth | None = Field(default=None)\n    add_ons: list[str] = Field(default_factory=list)\n    address: str | None = Field(default=None)\n    is_complete: bool = False\n    next_question: str | None = Field(\n        default=None,\n        exclude=True,\n        description=\"A concise question asking only for information still missing.\",\n    )\n```\n\nThe agent only asks a new question if there is some missing details. Thus, the agent decides what happens next, which is a key point that separates an AI agent from a workflow.\n### LangGraph\nWe use LangGraph because the booking workflow needs shared state and must resume across multiple customer messages. Thus, we need a stateful agent.\nThe state is a `TypedDict` as shown below:\npython\n\n```\nclass AgentState(TypedDict):\n    messages: Annotated[list[AnyMessage], add_messages]\n    booking_details: BookingDetails\n    calculated_price: NotRequired[float | None]\n    time_options: NotRequired[list[TimeOption]]\n    selected_slot: NotRequired[TimeOption | None]\n    status: BookingStatus\n    booking_id: NotRequired[str | None]\n```\n\nLangGraph manages state during graph execution. Nodes read the current state and return partial updates. Conditional routing functions inspect that state to decide which node runs next.\nWe can create the graph builder using this state schema:\npython\n\n```\nrom langgraph.graph import StateGraph\n\ngraph = StateGraph(AgentState)\n```\n\nThen, we start adding nodes and edges. For example, the following code snippet shows how we can create the price calculation node.\npython\n\n```\nfrom customer_service_agent.engines import calculate_price\n\ndef calculate_price_node(state: AgentState) -> dict[str, Any]:\n    return {\n        \"calculated_price\": calculate_price(\n            state[\"booking_details\"]\n        )\n    }\n\ngraph.add_node(\"calculate_price\", calculate_price_node)\n```\n\nThe price calculation node reads the extracted booking details and returns a partial state update.\nThe `calculate_price` is a function that calculates the price using the `BookingDetails` available in the `AgentState` .\nAfter the information-gathering node runs, a conditional routing function determines whether enough information is available to calculate a price:\npython\n\n```\ndef should_continue_to_pricing(\n    state: AgentState,\n) -> Literal[\"calculate_price\", \"end\"]:\n    if state[\"booking_details\"].is_complete:\n        return \"calculate_price\"\n\n    return \"end\"\n```\n\nWe connect that routing decision to the graph:\npython\n\n```\ngraph.add_conditional_edges(\n    \"gather_info\",\n    should_continue_to_pricing,\n    {\n        \"calculate_price\": \"calculate_price\",\n        \"end\": END,\n    },\n)\n```\n\nIf the booking details are complete, execution continues to the pricing node. Otherwise, the graph ends the current turn and waits for another customer message. \nThe remaining nodes and conditional routes are added in the same way. Finally, the graph is compiled with a checkpointer.\npython\n\n```\ngraph.compile(checkpointer=checkpointer or MemorySaver())\n```\n\n### Observability\nThe most critical component of any system is observability. Whether it's a production line at a factory, a traditional ML model to detect anomalies, or any AI-agent used in production.\nIf you do not know what goes on in your system, you just need to rely on pure luck to solve any issue. And it's certain that your production system will have issues.\nThere are many alternatives to use for observability of an AI agent. We will be using LangFuse, an open-source AI engineering platform designed for tracing, monitoring, evaluating, and debugging large language model (LLM) applications.\nThe free tier is enough to test our project. You just need to create the secret and public keys in LangFuse dashboard and paste them in your .env file:\npython\n\n```\nLANGFUSE_SECRET_KEY=\nLANGFUSE_PUBLIC_KEY=\nLANGFUSE_BASE_URL=\"https://cloud.langfuse.com\"\n```\n\nWe can create a langfuse handler as follows:\npython\n\n```\ndef create_langfuse_handler() -> Any | None:\n    \"\"\"Return a configured handler when credentials are available.\"\"\"\n    if not os.getenv(\"LANGFUSE_PUBLIC_KEY\") or not os.getenv(\"LANGFUSE_SECRET_KEY\"):\n        return None\n    from langfuse.langchain import CallbackHandler\n\n    return CallbackHandler()\n\ndef flush_langfuse(handler: Any | None) -> None:\n    \"\"\"Upload queued telemetry without making observability application-critical.\"\"\"\n    if handler is None:\n        return\n    try:\n        from langfuse import get_client\n\n        get_client().flush()\n    except Exception:\n        logger.exception(\"Failed to flush LangFuse telemetry\")\n```\n\nLangFuse dashboard shows every detail for us to debug and monitor our AI agent. We can see the prompts, inputs, and outputs. We also see the token usage and cost so it's very easy to detect if there is unexpected situation in our budget.\nLangFuse dashboard looks like this:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/07/image-445-1024x577.png)\nIt looks complicated at first but once you start using it, it becomes much easier.\n### What's next\nThe project includes a simple CLI to test the end-to-end booking workflow, but its modular design gives us plenty of room for improvement. \nYou need an OpenAI API Key to test it but it costs almost nothing. The screenshot below shows the cost of a single run:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/07/image-446-1024x255.png)\nEvery component has access to the central agent state so the system can easily be extended with:\n  * Frontend interfaces such as web, mobile apps, or messaging platforms like WhatsApp.\n  * A production database like PostgreSQL.\n  * Direct connections to real employee calendars and location-based services.\n\n\nI consider this release as v0 and I'm planning to add more features and functionality on top of it.\nThanks for reading.\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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