A Complete Guide to Building Enterprise-Grade AI Assistants on Google Cloud (No-Code)

Introduction

Enterprises are under pressure to deliver AI solutions quickly, but the demand for talent and the complexity of integrations often slow progress. This has led to the rise of low-code platforms, which empower teams to design and deploy applications visually, reduce development time, and connect seamlessly to existing systems.

Google Cloud is aligning closely with this shift. Its AI Applications provides a low-code environment for building AI systems and Conversational Agents that can ground responses in enterprise data and take real actions through APIs. The platform offers data stores for uploading documents, pre-built connectors for popular enterprise tools (like Jira, ServiceNow, and SharePoint), and OpenAPI support for integrating custom backends—all inside a single ecosystem. This integration enables organizations to build agentic AI systems that are fast to deploy, secure, and governed — all within a low-code environment seamlessly embedded into daily workflows.

• Agents that can reason and act, grounded in enterprise data sources like PDFs, CRMs, ticketing, or HR systems.

• A single cohesive ecosystem rather than a patchwork of disconnected tools.

• Built-in security, scalability, and logging across the stack, because everything runs within Google Cloud.

In this article, I’ll walk you through building a three-agent system using Google Cloud’s no-code tooling—connected to real PDFs, a ticket API, and exposed through Slack with Cloud Logging as the observability layer. You’ll see how quickly you can go from blank project to fully functional, grounded enterprise chatbot team, all inside the same cloud ecosystem.

Understanding the Agentic System

Before we start building, let’s understand the architecture of the agentic system we’ll implement. The setup simulates a small enterprise IT helpdesk built with Google Cloud’s Conversational Agents, featuring one Supervisor Agent and two Specialized Agents, each connected to its own data source and responsible for distinct tasks.

The PDF Retriever Agent handles policy-related questions by retrieving grounded information from two key documents: the VPN Policy Template and the Database Credentials Standard (SANS, April 2025). These files are stored in a Data Store tool, which indexes the PDFs so the agent can extract relevant policy sections and summarize them into clear, contextual answers.

The API Caller Agent manages ticket-related operations using an OpenAPI tool connected to a mock ticketing API implemented in Google Cloud Functions. The API exposes simple endpoints to create and check support tickets, allowing the agent to simulate realistic IT helpdesk interactions during the conversation.

At the center of this workflow is the Supervisor Agent, the brain of the system that interprets user intent and delegates each request to the correct specialized agent. When a user asks a question or submits a request, the Supervisor routes it either to the PDF Retriever (for policy guidance) or the API Caller (for ticket operations). Each worker performs its task and responds directly to the user, after which the Supervisor automatically regains control to confirm completion and offer further help.

Let's build the Agent!

Getting Started: Set Up Your Google Cloud Project

Create a Google Cloud Platform Account

Before creating your AI application, you’ll need to set up a new Google Cloud environment
If you don’t already have one, go to Google Cloud Console
and sign in with your Google account.

Create a New Project

In the top navigation bar, click the project dropdown → “New Project”.
Give your project a descriptive name and select your billing account (if prompted).
Choose an organization or leave it under “No organization” if you’re testing.
Click Create.

Enable APIs and Integrations

After creating your project, the next step is to enable the necessary APIs that power your Conversational Agents and integrations.

1. Enable the AI Applications API
In the Google Cloud Console, use the search bar at the top and type “AI Applications”.

Select AI Applications API from the results and click "Enable" to activate it for your project.

2. Enable the Dialogflow API
Go back to the API Library. Search for "Dialogflow API" and click "Enable".

Dialogflow is required for integrating your conversational agents with chat platforms (e.g. Slack, Google Chat).

3. Set Up Slack Integration (Optional)
If you intend to make your agent accessible directly within Slack, you can configure the integration as an optional step.
Before proceeding, ensure you have:
- A Slack account
- Access to a Slack workspace

Agent Architecture

In Google Cloud’s Conversational Agents, playbooks come in two flavors: routine playbooks and task playbooks.
A routine playbook manages the overall flow of a conversation, while a task playbook performs a specific, well-defined function before handing control back.
In our system, we’ll combine both — a routine playbook to coordinate the conversation and task playbooks to handle specialized actions.

This modular approach keeps the design clean, scalable, and easy to maintain — each agent focuses on its own responsibility while working together as one system.

We’ll build the agentic system in three layers:
Tools → Data Store (for PDFs) and OpenAPI (for the Ticket API)
Task Playbooks → PDF Retriever and API Caller Agent
Routine Playbook → Supervisor Agent that coordinates everything

Let’s Create the Agentic System!

Create a New Conversational Agent

In the Google Cloud Console, go to AI Applications → Conversational Agents, then click Create an Agent.
Choose Build your own to start from scratch.
Give your agent a clear name (e.g., IT Assistant), pick your preferred location, set the correct time zone, and choose your default language. Finally, under Agent type, select Start with Playbook.

Once the agent is created, you’ll be redirected to the Default Generative Playbook page — this is your routine playbook, which will become the Supervisor Agent in our system.

For now, we’ll pause here. Click the ← arrow in the top-left corner to return to the main agent view.
The Supervisor Agent should be created last — after we first build the tools and the task playbooks (PDF Retriever and API Caller) that depend on them.

Setting Up the Tools

In this system, we’ll connect two tools:

• A Data Store to index and retrieve information from PDF policy documents.
• An OpenAPI tool to handle ticket-related operations such as creating and checking IT tickets.

Data Store

In the left sidebar, select Tools, then click Create → Fill the fields as follows:

Tool Name: ITPolicyDocs
Type: Data Store
Description: Searches the organization’s IT policy PDFs (e.g., Database Credentials Standard, VPN Policy Template) to provide grounded answers to user questions.

Next, it’s time to create the Data Store by indexing and ingesting the policy documents. From the Tools page, select Cloud Storage (unstructured data) since the source materials are PDFs stored in a Bucket. Open the Advanced options to gain finer control over the ingestion and indexing process.

Import your documents from Cloud Storage and move to the configuration screen. Set the name of your Data Store — for example, policies_store_1 — and apply the following recommended settings based on Vertex AI Search guides:

1. Parser → Layout parser: Best suited for PDFs and DOCX files, this parser maintains the original document layout and hierarchy, which helps the model retrieve information more accurately in retrieval-augmented generation (RAG) workflows.

2. Document Chunking → Keep the default chunk size of 500, which fits well with the moderate section length and structure of the policy documents, ensuring context is preserved without fragmenting the content. Enable “Include ancestor headings in chunks” to retain section headers, ensuring contextual grounding even when retrieving content from mid-document.

Once indexing begins, return to your tool configuration and select the newly created Data Store. Under Tool Settings, click “Customize” to adjust the grounding parameters. In the Grounding section, set the Lowest score allowed (grounding threshold) to Medium — this ensures that only sources with moderate-to-high confidence are used, improving reliability while avoiding overly strict filtering.

Other settings such as the Rewriter and Summarization model (here using gemini-2.0-flash-001) can remain at their default values, as they already provide concise, high-quality summarizations of retrieved content. This configuration ensures your agent gives grounded, trustworthy answers directly from your IT policy PDFs.

OpenAPI

In the left sidebar, go to Tools → Create, then fill the fields:
Tool Name: Ticket API
Type: OpenAPI
Description: Use createTicket to open a new IT request (required fields: summary, description, priority, requester). Use getTicket to check the status of an existing ticket by ID (required fields: ticketID).

For demo purposes, this tool connects to a mock ticketing service I implemented with Google Cloud Functions and deployed via Cloud Run. This lightweight setup simulates a simple helpdesk system, allowing agents to create and check ticket statuses as if they were interacting with a real backend. The function is exposed as a REST API through Cloud Run and defined using an OpenAPI YAML specification, making it easy to integrate directly into Google Cloud’s Conversational Agents as a tool. Although it doesn’t persist to a database, it stores tickets in memory to mimic realistic interactions. When a ticket is created, the API returns a generated ID (for example, IT-3B7A12) with status "Open". A status check returns the ticket ID, summary, description, and current status. This gives us a reliable, controlled environment to demonstrate real API calls inside Conversational Agents.

In the "Schema" section, choose YAML and paste an OpenAPI spec like the following:

openapi: 3.0.0
info:
  title: Ticket API
  version: 1.0.0
servers:
  - url: https://<YOUR-URL>     # e.g., https://ticketapi-xxxxx.run.app
paths:
  /tickets:
    post:
      operationId: createTicket
      summary: Create a ticket
      requestBody:
        required: true
        content:
          application/json:
            schema:
              type: object
              required: [summary, description, priority, requester]
              properties:
                summary: { type: string }
                description: { type: string }
                priority: { type: string, enum: [Low, Medium, High] }
                requester: { type: string, format: email }
      responses:
        "200":
          description: Ticket created
          content:
            application/json:
              schema:
                type: object
                properties:
                  id: { type: string }
                  status: { type: string }
  /tickets/{id}:
    get:
      operationId: getTicket
      summary: Get ticket status
      parameters:
        - name: id
          in: path
          required: true
          schema: { type: string }
      responses:
        "200":
          description: Ticket status
          content:
            application/json:
              schema:
                type: object
                properties:
                  id: { type: string }
                  status: { type: string }
                  summary: { type: string }
                  description: { type: string }

Let's build our task playbooks!

Setting Up the Task Playbooks

1. PDF Retriever From the left sidebar, navigate to Playbooks → Create, and select Task Playbook.

We’ll begin with the first specialized agent — the one responsible for handling policy-related queries using the PDF documents.

• Playbook name: PDF Retriever
• Goal: Answers IT policy questions by retrieving grounded passages from the uploaded PDFs. Always cite the policy title or section when possible.

Next, connect the Data Store tool you created earlier (ITPolicyDocs) so this playbook can search and retrieve content from the indexed policy PDFs.
This connection happens through the playbook’s instructions, where we explicitly reference the tool to guide the agent’s retrieval behavior.

Now, add the following instructions:

You are the PDF Retriever Agent. Your role is to handle IT policy queries by consulting the organization’s PDF policy documents.
Always search the attached PDFs (e.g., Database Credentials Standard, VPN Policy Template) to find relevant passages using the tool ${TOOL:ITPolicyDocs}.
- If the user’s question is ambiguous or missing context, ask clarifying questions.
Search the data store and answer based only on returned content.
Quote or paraphrase the relevant passage.
Keep responses concise and in plain language.
When possible, mention the document title/section.
If the policy clearly allows or denies, state that plainly.
- Output expectations
Provide the grounded answer text plus lightweight metadata (e.g., source_title, section, and optionally a proposed_action like create_ticket with collected fields if the user asked for escalation).
Once you answer to the user, update the parameter $route_to_supervisor=True

Next, define an output parameter so the playbook can signal back to the Supervisor when it finishes responding:

Go to the Parameters tab → Create Output Parameter.

Fill the fields as follows:

• Parameter name: route_to_supervisor
• Type: Boolean
• Description: Give control back to the Supervisor.

This ensures that each time the PDF Retriever provides an answer, the parameter is set to True, allowing the Supervisor Agent to automatically regain control of the conversation flow.

1. API Caller Agent

Next, let’s create the second task playbook — the one that handles ticket-related operations by interacting with the mock Ticket API.

From the left sidebar, go to to Playbooks → Create→ Task Playbook.

• Playbook name: API Caller Agent
• Goal: Handles IT support ticket operations by calling the Ticket API to create new requests or check ticket status.

Once the playbook is created, connect it to the OpenAPI tool you previously built (Ticket API). This allows the agent to interact directly with the mock ticketing backend through the predefined endpoints.
Now, add the instructions that define how the playbook will use the API tool to perform ticket operations.

You are the API Worker Agent. Your role is to handle all ticketing operations for the IT Helpdesk system. Always use the tool ${TOOL:Ticket API}.
-Use the createTicket action whenever the user instructs you to open a new support ticket.
-Always supply the required fields:
summary: a short title of the request
description: detailed explanation of the request
priority: Low, Medium, or High
requester: the requester’s email address

If required fields are missing or invalid, ask the user for them (you own follow-ups and validation).
Validate priority is one of Low/Medium/High.
Validate requester looks like an email.
For getTicket, require a ticket id; if absent, ask.
- After calling the tool ${TOOL:Ticket API}, return the ticket ID and its initial status.
-Use the getTicket action whenever you are asked to check the progress of an existing ticket.
-Provide the ticket id.
Never invent or guess ticket IDs or fields — only use what is provided.
After calling the tool, return the ID, current status, and any available details (summary and description).
Once you answer to the user, update the parameter $route_to_supervisor=True.

Next, define the same boolean output parameter "route_to_supervisor" to ensure that, after each interaction, control returns to the Supervisor.

This ensures that once the API Caller Agent completes a task, the conversation flow automatically returns to the Supervisor Agent, maintaining centralized control and continuity in the user experience.

Setting Up the Routine Playbook

Next, we will set up the Supervisor Agent, — the core routine playbook that controls the overall flow of the conversation. This agent acts as the orchestrator, greeting the user, understanding intent, delegating tasks, and regaining control after each task completes.

Go back to "Playbooks" and open the "Default Routine Playbook" we skipped earlier.
Fill the fields as follows:

Playbook name: Supervisor Agent
Goal: You are the Supervisor Agent. You own the conversation shell (greeting and closing) and delegate every user request to the correct task playbook. You do not answer policy or ticket details yourself.

Now, add the following instructions that define the agent’s behavior and control logic:

- Greet the user on the first turn.
- Intent routing (every turn):
- If the user asks about IT rules, acceptable use, VPN, credentials, or policies → delegate to ${PLAYBOOK:PDF Retriever}.
- If the user wants to open a ticket or check ticket status → delegate to ${PLAYBOOK:API Caller Agent}.
- Do not ask follow-up questions for handling missing information or validate details.
- Do not include any summary of previous conversation history.
- After any worker playbook finishes and the parameter $route_to_supervisor=True immediately take back control and ask:
- “Anything else I can help you with? 🙂”
- If the user indicates they’re done, say goodbye politely and end.
- Tone: concise, professional, friendly

Finally, let’s connect the Supervisor Agent to the rest of the system!
Go to the Parameters tab and click Add new read parameter.
Fill in the fields as follows:

Parameter name: route_to_supervisor
Type: Boolean
Description: Control to the supervisor

This parameter mirrors the output parameter you created earlier in both the PDF Retriever and API Caller Agent playbooks.
By reading this value from the session memory, the Supervisor knows exactly when a worker has completed its task and when it should take back control of the conversation. Once the parameter route_to_supervisor becomes True, the Supervisor automatically resumes interaction, prompting the user with: “Anything else I can help you with? 🙂”

This step closes the loop in your agentic workflow — ensuring smooth handoffs between agents and keeping the overall experience consistent and natural.

Toggle Simulator

You can now test the overall conversational flow using the Toggle Simulator, accessible from the top navigation bar. This built-in tool allows you to preview and validate interactions between your agents directly within the Conversational Agents interface. It provides a real-time view of how intents are detected, which playbook is triggered, and how parameters, such as route_to_supervisor, are passed between agents. Thus, the Toggle Simulator also serves as an effective debugging environment — allowing you to inspect conversation states, verify routing logic, and observe when each tool is invoked, which inputs are provided, and what outputs are returned during the interaction.

When starting a conversation in the Toggle Simulator, you can define the starting node of your agentic system. By default, the conversation begins with the Routine Playbook, which in this case is the Supervisor Agent.

Additionally, the simulator allows you to experiment with different AI models to evaluate performance and response quality. For this example, select Gemini 2.5 Flash, which offers fast and contextually accurate responses.

For instance, you can evaluate the system by submitting a query such as:

“Am I allowed to use my personal computer to connect to the company VPN?”

In this case, the Supervisor Agent identifies the intent as policy-related and delegates the query to the PDF Retriever Agent, which searches the VPN Policy Template document.

The PDF Retriever invokes the ITPolicyDocs tool, searches the indexed VPN Policy Template, and returns a grounded, policy-based answer. Once the answer is delivered, the PDF Retriever completes its execution with State: OK, indicating a successful run, and sets the output parameter route_to_supervisor=True, signaling the Supervisor Agent to regain control of the conversation.

The Supervisor then resumes interaction smoothly, prompting the user with “Anything else I can help you with? 🙂” — demonstrating the seamless orchestration between agents within the system.

Setting Up some examples

According to Google Cloud’s documentation examples act as training cues that help the model understand the types of user inputs it should recognize and how to respond effectively. They guide the playbook in interpreting intent, selecting the right tools, and maintaining an appropriate tone and context throughout the conversation.
A practical advantage of Google Cloud’s Conversational Agents platform is that you can add examples directly from the Toggle Simulator. After testing an interaction, simply click “Save as example” to capture the full conversational flow — including the user’s input, the playbook transitions, and the model’s response. This feature allows you to link real interaction data to the relevant playbook, turning it into a reference example that improves the model’s understanding of similar future queries.

If something doesn’t go as expected during testing — for instance, if a playbook routes incorrectly or a response needs refinement — you can inspect and adjust the full sequence of messages, tool calls, and playbook states directly in the simulator. Once you’ve configured the flow to behave exactly as intended you can save it as an example for the specific playbook. This makes it easy to fine-tune your agent iteratively, ensuring that future runs follow the corrected behavior.

Further Configuration: Generative AI Settings

Google Cloud’s Conversational Agents offer flexible configuration options for fine-tuning how your agents generate, process, and manage responses. Under Settings → Generative AI, you can adjust model behavior and generation parameters to align with your organization’s conversational goals.

• In the Generative Model Selection section, you can choose from available Gemini models (for instance, gemini-2.5-flash), define input and output token limits, and set the temperature, which controls creativity versus precision. Lower temperature values (close to 0) produce more deterministic, consistent outputs, while higher values introduce greater variation and expressiveness in responses.

• The Context Token Limits option determines how much conversation history is preserved between turns — useful for maintaining long-term context in multi-step workflows without exceeding model constraints.

Beyond generative tuning, the General tab under the same menu provides safety and compliance controls. Here you can define banned phrases, preventing the model from generating or processing specific terms in both prompts and responses. This helps ensure content safety and brand compliance, especially in enterprise deployments. You can also customize safety filters, configuring how strictly the system blocks sensitive or harmful content categories such as hate speech or explicit language.

Logging

Monitoring and evaluating your agent’s performance is a crucial part of maintaining a reliable conversational system. Google Cloud’s Conversational Agents platform provides two ways to track and analyze interactions: Conversation History and Cloud Logging.

In the top navigation bar, open Settings→ Logging Settings and "Enable conversation history" and "Enable Cloud Logging".

Conversation History

Conversation History automatically captures every exchange between users and your agents. You can review full transcripts right in the Conversation History panel — perfect for debugging, validating flow logic, or simply seeing how users engage with your agents over time.

Cloud Logging
Enable Cloud Logging to export detailed query and debugging data to Google Cloud’s Logs Explorer.
This integration provides deeper visibility into your agentic system’s behavior — including request timing, playbook transitions, tool invocations, and message trends. With Cloud Logging, you can perform analytics, identify common user intents, and monitor system performance metrics across all conversations.

Slack integration

To make your conversational agent accessible directly from your organization’s Slack workspace, you can integrate it using Google Cloud’s Slack integration feature.

To set it up, we will follow Google Cloud’s official guide:
👉 Integrate Dialogflow with Slack

1. Prerequisites

A Slack account and a Slack workspace where you can install custom apps.

2. Create the Slack app (from a manifest)

• Go to Slack Apps and create a new app from an app manifest.
• Use the manifest structure shown in Google’s doc as a template, ensuring these parts are present:
  • Bot token scopes (e.g., app_mentions:read, chat:write, im:read, im:write, im:history, incoming-webhook).
  • Event subscriptions with a Request URL (you’ll paste the URL generated by Google Cloud in step 4).
  • Bot events like app_mention and message.im.
  • Keep Socket Mode disabled (per the example).

display_information:
  name: Conversational Agents (Dialogflow CX)
  description: Conversational Agents (Dialogflow CX) integration
  background_color: "#1148b8"
features:
  app_home:
    home_tab_enabled: false
    messages_tab_enabled: true
    messages_tab_read_only_enabled: false
  bot_user:
    display_name: CX
    always_online: true
oauth_config:
  scopes:
    bot:
      - app_mentions:read
      - chat:write
      - im:history
      - im:read
      - im:write
      - incoming-webhook
settings:
  event_subscriptions:
    request_url: https://dialogflow-slack-4vnhuutqka-uc.a.run.app
    bot_events:
      - app_mention
      - message.im
  org_deploy_enabled: false
  socket_mode_enabled: false
  token_rotation_enabled: false

3. Install the app to your workspace and copy:
In your App:

• Bot User OAuth Token (Slack: Install App → OAuth Tokens for Your Workspace).

• Signing Secret (Slack: Basic Information → App Credentials).

(If you’re curious about Slack scopes in general, Slack’s developer docs explain how scopes map to bot capabilities.)

4. Connect Slack inside Google Cloud (Conversational Agents)

In the Conversational Agents console, open your agent and find in the left bar "Integrations".

Click Slack → Connect.

• Paste the Access token (your Slack Bot User OAuth Token) and Signing token (Slack Signing Secret) from step 3.

• Choose your environment deployed the agent (e.g. Draft)

• Click Start.

• Copy the generated Webhook URL.

5. Point Slack to your agent

• Return to your Slack app and open Event Subscriptions → Enable Events.

• Paste the Webhook URL you copied from step 4 into Request URL and save.

6. Configure Incoming Webhooks and Channel Access

In your Slack App configuration page, go to Features → Incoming Webhooks → Webhook URLs for Your Workspace.
Here, you can add Webhook URLs for the specific channels or direct messages (DMs) where you want your bot to communicate.

In public or private channels, the bot will respond whenever it is mentioned by name, ensuring it only engages when prompted, while in DMs, it can respond directly to user queries.

7. Customize your Agent
You can personalize your agent’s appearance and behavior in Slack to better reflect your organization’s branding and communication style.
From the Slack app configuration page, navigate to Features → App Home, where you can adjust the display name, bot icon, and description shown in your workspace.

8. Test the integration

In Slack, mention the bot in a channel or DM the bot to start chatting.

In the above conversation, the user initiates a chat with a greeting, and the IT Assistant — acting as the Supervisor Agent — responds politely, ready to assist.

The user then asks a policy-related question about what the company policy states when database credentials may have been exposed. The Supervisor detects this as a policy inquiry and routes the request to the PDF Retriever, which searches the Database Credentials Standard document. The retriever provides a grounded answer explaining that credentials must not be stored in clear text or in web-accessible locations, citing the relevant policy section.

Once the policy response is delivered, the Supervisor Agent resumes control of the conversation and courteously asks if further help is needed. The user then requests to create a ticket for review. Recognizing this as an operational task, the Supervisor delegates the request to the API Caller Agent, which interacts with the mock ticketing API. The API processes the input details — summary, description, requester, and priority — and responds with a generated ticket ID and an open status.

Finally, the Supervisor politely confirms the ticket creation and ends the interaction after the user says goodbye.

This example demonstrates the end-to-end flow of intent detection, delegation, and seamless orchestration between the agents — from grounded policy retrieval to action execution through the OpenAPI integration. It also highlights how the system operates smoothly within Slack, where users can interact naturally with the IT Assistant in their everyday workspace without leaving the chat environment.

Conclusion

Building agentic systems inside Google Cloud’s AI Applications is more than just a technical exercise — it’s a glimpse into the next evolution of enterprise automation. In this walkthrough, we saw how easy it is to design, orchestrate, and deploy a multi-agent helpdesk system using no-code tools — integrating policy retrieval, ticket creation, and chat-based interaction, all within a single, governed cloud environment.

The resulting architecture — one Supervisor Agent coordinating multiple specialized playbooks — provides a powerful blueprint for scalable enterprise AI systems. It allows organizations to design modular, transparent workflows where every agent serves a clear purpose, grounded in data and capable of performing real actions through APIs.

What makes this approach especially impactful is that everything happens within the same ecosystem: data security, access control, observability, and scalability are built-in through Google Cloud’s infrastructure. You can test, debug, and monitor your entire system with tools like Cloud Logging and Conversation History, or even deploy it directly to Slack for real-world usage with your team — no complex deployment pipeline required.

Next Steps and Opportunities

While this no-code setup covers the full lifecycle of a conversational system, advanced teams can take it further by blending low-code flexibility with custom logic:

• Add custom actions or logic through Cloud Functions or Cloud Run — for example, to validate inputs, enrich data from other APIs, or trigger workflows in external tools like Jira or ServiceNow.

• Integrate structured data sources, such as BigQuery for even richer, context-aware responses.

• Use Cloud Logging and BigQuery exports to build analytics dashboards — tracking usage, intent distribution, and success rates over time.

• Implement advanced integrations — such as email responders, or internal portals — to expand where and how users can access your AI assistant.

At its core, Google Cloud’s low-code AI platform allows enterprises to prototype fast and scale safely, bridging the gap between no-code experimentation and full-scale production AI. Whether you’re automating IT requests, HR inquiries, or customer service operations, this approach gives your teams the flexibility to innovate — without waiting on a long development cycle.

The next step? Start experimenting with your own data and APIs — and turn your organization’s workflows into intelligent, conversational systems.

👉 For further reading, explore Google Cloud’s Best Practices for playbooks to design reliable, maintainable, and scalable agentic architectures.