Mike's Notes
Yesterday (Wednesday NZ time), I attended the Google Startups Product Roadmap Seminar: October 2026 via Google Meet.
"Join Google Cloud and Wiz specialists to explore the latest in startup security and Gemini, with a deep dive into our newest models. This session is exclusively tailored for our strategic Google Cloud startup customers." - Google
There were 2 presentations
- Gemini Roadmap: Agent Updates & Antigravity (Lia Passaglia: Cloud AI Specialist Engineer)
- Wiz Roadmap: AI Threat Readiness (Ian Wehba: Cloud Security Customer Engineer Manager)
"How to connect any LLM model, starting with Gemini, to a Pipi workspace so that an Ajabbi customer can use AI tools without the AI changing the actual workspace code. Everything must run in CFML on Ortus BoxLang, starting from Pipi 10."
Resources
- https://cloud.google.com/gemini-enterprise/connectors
- https://ai.boxlang.io/
- https://ai.ortusbooks.com/readme/faq
- https://ai.ortusbooks.com/main-components/memory
- https://www.youtube.com/watch?v=aUFp32r1dIA
- https://foojay.io/today/boxlang-ai-series-complete-guide-to-building-ai-agents/
- https://www.blog.ajabbi.com/2025/05/boxlang-100.html
- https://www.blog.ajabbi.com/2025/07/luis-majano-on-boxlang.html
- https://www.blog.ajabbi.com/2025/05/a-roadmap-to-pipi-10.html
- https://www.blog.ajabbi.com/2025/06/updated-pipi-9-to-10-plan.html
- https://www.blog.ajabbi.com/2026/03/accelerate-and-rubber-hits-road.html
- https://www.blog.ajabbi.com/2026/02/the-workspace-issue.html
References
- Reference
Repository
- Home > Ajabbi Research > Library >
- Home > Handbook >
Last Updated
23/10/2026
Pipi Workspace and Gemini integration
Mike Peters: Mike invented and designed Pipi and founded Ajabbi.
Google Search AI Mode (Gemini): GSAM is great.
...
Today, using Google Search AI Mode, I asked some very dumb questions about using connectors. I did this to find a way to safely connect any workspace generated by Pipi running in CFML on BoxLang to Gemini. Originally, MCP was the top contender.
Here is the raw output, with Grammarly post-editing; I'll rework the code manually. This is a good place to start.
Boxlang AI Module is free and open-source.
Remember, "Use this code with caution." 😎😎😎😎
Mike: What is a connector for Google Gemini?
Gemini: A Google Gemini connector is a secure bridge that links Google Gemini with external apps, databases, and third-party software.
Depending on the context, a "connector" refers to either data-ingestion tools built into enterprise versions of Gemini or third-party integrations that let other software platforms use Gemini’s AI model capabilities.
1. Gemini Enterprise Connectors (Data Ingestion)
In Gemini Enterprise (Business, Standard, and Plus editions), IT administrators manage connectors to securely bring scattered organisational data into Gemini. This allows users to search, summarise, and analyse their company's internal information using natural language.
- Google Connectors: Link seamlessly to core Google Workspace apps such as Gmail, Google Drive, Google Calendar, Chat, and Google Sites.
- Third-Party Connectors: Provide native access to external business ecosystems. Gemini Enterprise supports over 100 integrations, including Microsoft 365 (SharePoint, OneDrive), Atlassian (Jira, Confluence), Salesforce, ServiceNow, Slack, and Zendesk.
- Custom Connectors: If a specific proprietary database or software isn't in the standard catalogue, companies can build custom connectors using the Google Cloud Discovery Engine API to fetch, transform, and sync their data.
2. Outbound Connectors (AI for External Apps)
Conversely, many software developers use "Gemini Connectors" to inject Gemini's AI capabilities into other software. In this scenario, the connector acts as an API plugin. Examples include:
- Automation Platforms: Tools like Camunda and Microsoft Power Automate utilise a custom Gemini connector so business processes can automatically generate text, analyse sentiment, or extract keywords from files.
- App Development Modules: Low-code platforms like Mendix feature pre-packaged Gemini modules that let developers easily build AI chatbots, translate languages, or parse large blocks of text directly inside their applications.
Gemini: Are you looking to connect your enterprise data (like Jira or SharePoint) to Gemini, or are you trying to build Gemini's AI features into an external app you are developing? Let me know so I can provide the exact steps or documentation links!
Mike: I want to find a way to connect an enterprise SaaS I built to integrate with Gemini, so that Gemini can edit the content stored in the SaaS, but not the SaaS itself.
Gemini: To connect your enterprise SaaS so Gemini can manipulate content without threatening your core application codebase or infrastructure, you should implement Function Calling via the Gemini Enterprise Agent Platform (formerly Vertex AI) or the native Gemini developer API.
With Function Calling, Gemini doesn't execute code inside your application and has no direct access to your codebase. Instead, it acts as a decision-maker: you provide a strict blueprint of the content-editing APIs it can use, and Gemini replies with a request containing the specific content edits to make.
How the Security Boundary Works
Because Gemini lives on Google's servers, it can only interact with your SaaS through the specific API endpoints you choose to expose.
- Safe (Content Editing): You expose a PATCH /api/v1/documents/{id} or PUT /api/v1/content endpoint. Gemini can request changes to text or data fields.
- Isolated (SaaS Integrity): You never give Gemini endpoints that can rewrite your code, alter your deployment configurations, drop database tables, or access server SSH keys. Gemini literally cannot touch the SaaS itself because no such API actions are made available to it.
Architecture: The 4-Step Tool Loop
When a user gives Gemini an instruction (e.g., "Fix the typos in the project brief stored in the SaaS"), the following cycle occurs:
┌──────┐ 1. User Prompt + Function Schema ┌────────┐
│ Your │ ──────────────────────────────────> │ │
│ App │ <────────────────────────────────── │ Gemini │
└──────┘ 2. JSON Function Call └────────┘
│ ▲
│ 3. Your App executes internal logic │
│ & patches the SaaS database │ 4. Final Text Response
▼ │
┌──────┐ │
│ SaaS │ ─────────────────────────────────────────┘
└──────┘
- You Define the Blueprint: When you start a session using the Gemini API or the standard Interactions API, you pass a list of available tools in a JSON schema.
- Gemini Decides, It Does Not Execute: Gemini parses the user's request, notices that it needs to edit content, and spits back a structured JSON command (a functionCall) specifying which tool to use and what arguments to supply (e.g., document_id: "123", new_text: "...").
- Your SaaS Validates and Executes: Your application receives Gemini's JSON payload. Your server checks user permissions, sanitises the inputs, executes the actual database write, and generates a success confirmation.
- Closing the Loop: Your backend returns the execution results (functionResponse) back to Gemini, which then formats a friendly final confirmation message for the end user.
Code Example: Exposing Content-Only Tools
When configuring the model using a modern environment like Google AI Studio or your backend application SDK, you restrict Gemini's access directly in the tools array.
javascript
const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
// 1. Define only content-centric functions.
// Do NOT include system management or code modification tools here.
const updateSaasContentTool = {
functionDeclarations: [{
name: 'updateSaasContent',
description: 'Updates the text body or content of a specific resource inside the SaaS database.',
parameters: {
type: 'OBJECT',
properties: {
contentId: { type: 'STRING', description: 'The unique ID of the document or record.' },
updatedText: { type: 'STRING', description: 'The newly edited or corrected text content.' }
},
required: ['contentId', 'updatedText']
}
}]
};
// 2. Pass the content tool into the Gemini model instance
const chat = ai.chats.create({
model: 'gemini-2.5-flash', // Or your preferred Gemini model
config: {
tools: [updateSaasContentTool],
systemInstruction: "You are an assistant that edits customer data records within our SaaS system. You only have access to modify data fields via the provided tools."
}
});
Best Practices for Absolute Isolation
- Least Privilege API Keys: Ensure the API key or OAuth token your application uses to execute the function calls only has read/write permissions for user content data—never admin or infrastructure roles.
- Strict Schema Validation: Always treat Gemini’s JSON output as untrusted user input. Validate the fields on your server side before writing to your database.
- The Model Context Protocol (MCP): If you want an open-source, standardised framework for securely exposing your SaaS data tools to Gemini, consider building an MCP Server. Gemini natively supports connecting to external data ecosystems via MCP server setups.
Gemini: What language or framework is your SaaS backend built in (e.g., Node.js, Python, Go)? I can provide the exact code block for handling the functionCall feedback loop on your server.
Mike: The SaaS backend is built in CFML.
Gemini: Implementing Function Calling in a CFML backend (such as Lucee or Adobe ColdFusion) requires managing the request and response cycle via standard HTTP calls, as there isn't a native Google-maintained SDK for CFML.
Because Gemini only sends back JSON instructions, your CFML application retains absolute control. Gemini decides what to edit, but your CFML components (.cfc) validate the parameters, check user sessions, and execute the actual SQL or ORM operations to modify the content database.
The CFML Integration Workflow
The integration is split into two phases inside your CFML application:
- The Request: Sending the user prompt alongside your defined CFML tool schema to Gemini.
- The Execution: Parse Gemini's response, run your internal CFML logic, and send the results back to complete the loop.
Phase 1: Sending the Tool Definition to Gemini
You will send a POST request to the Gemini API (/v1beta/models/gemini-2.5-flash:generateContent). Include your system boundaries and the explicit content-editing functions in the tools array.
cfml
<cfset toolsPayload = [
{
"functionDeclarations": [
{
"name": "editSaaSContent",
"description": "Edits the text body of a specific record inside the SaaS database. Use this when the user asks to modify, format, or fix text content.",
"parameters": {
"type": "OBJECT",
"properties": {
"recordID": { "type": "STRING", "description": "The unique UUID or ID of the content record." },
"newContent": { "type": "STRING", "description": "The newly edited text content." }
},
"required": ["recordID", "newContent"]
}
}
]
}
]>
<!--- Prepare the request body --->
<cfset requestBody = {
"contents": [
{ "role": "user", "parts": [{ "text": "Fix the grammatical errors in document ID 98765" }] }
],
"systemInstruction": {
"parts": [{ "text": "You are a content assistant. You can only modify data records using the provided tools. You cannot touch SaaS infrastructure or codebase." }]
},
"tools": toolsPayload
}>
<!--- Make the API call to Gemini --->
<cfhttp url="https://googleapis.com" method="POST" result="geminiResponse">
<cfhttpparam type="header" name="Content-Type" value="application/json">
<cfhttpparam type="body" value="#serializeJSON(requestBody)#">
</cfhttp>
Phase 2: Processing Gemini’s Decision in CFML
Gemini will not return a text answer. Instead, it will return a functionCall object. Your CFML code must intercept this, verify it, run your safe database update, and send the result back to Gemini.
Here is how you handle that response securely:
cfml
<cfset responseData = deserializeJSON(geminiResponse.fileContent)>
<!--- Check if Gemini wants to call your content-editing tool --->
<cfif structKeyExists(responseData, "candidates")
AND arrayLen(responseData.candidates) GT 0
AND structKeyExists(responseData.candidates[1].content.parts[1], "functionCall")>
<cfset functionCall = responseData.candidates[1].content.parts[1].functionCall>
<!--- Ensure Gemini is calling the expected tool --->
<cfif functionCall.name EQ "editSaaSContent">
<cfset args = functionCall.args>
<!--- SECURITY: Safely validate incoming parameters before touching the database --->
<cfif isValid("guid", args.recordID) OR isValid("integer", args.recordID)>
<!--- Execute your native CFML database update --->
<cftry>
<query execute="UPDATE saas_content_table SET body_text = :newText WHERE id = :id"
params="{ newText: {value=args.newContent, cfsqltype='cf_sql_clob'}, id: {value=args.recordID, cfsqltype='cf_sql_varchar'} }">
<cfset executionStatus = "Success: Content has been updated inside the SaaS database.">
<cfcatch type="any">
<cfset executionStatus = "Error: Failed to write to database. #cfcatch.message#">
</cfcatch>
</cftry>
<cfelse>
<cfset executionStatus = "Error: Invalid record ID format provided by AI.">
</cfif>
<!--- Final Step: Send the execution result back to Gemini so it can reply to the user --->
<cfset followUpBody = {
"contents": [
<!--- You must include the original conversation history here, followed by: --->
{
"role": "function",
"parts": [{
"functionResponse": {
"name": "editSaaSContent",
"response": { "output": executionStatus }
}
}]
}
]
}>
<!--- Post this back to Gemini to get the final conversational confirmation message --->
</cfif>
</cfif>
</cfif>
Keeping the Boundary Enforced in CFML
- Session Validation: Do not rely solely on Gemini knowing who the user is. Before running the <cfquery> update, verify that the logged-in user session (session.userID) actually has permission to modify args.recordID.
- Input Sanitisation: Use cfqueryparam (or query params inside script syntax as shown above) without exception. This completely stops Gemini from performing SQL Injection if it goes rogue or gets manipulated by a malicious user prompt (Prompt Injection).
- Isolation of Admin CFCs: Keep this integration code inside a separate, non-administrative Component (e.g., aiService.cfc). Never pass any methods that handle server maps, application reloads (url.init), or file system manipulation (<cffile>) into the tools definition payload.
Gemini: Are you using a specific CFML framework like ColdBox, Framework One (FW/1), or a legacy/custom legacy codebase? I can help tailor the controller architecture or routing settings to match your current setup.
Mike: Custom legacy CFML codebase running on Boxlang.
Gemini: Running on BoxLang, you have a massive architectural advantage. Because BoxLang includes a native, unified ecosystem called BoxLang AI (bx-ai), you do not need to mess with raw JSON schemas or manual HTTP requests like traditional CFML setups require.
BoxLang AI acts as a multi-tenant orchestration layer. It allows you to wrap your legacy .cfm/.cfc functions as native AI Tools that Gemini can call.
The Security Sandbox Strategy
To ensure Gemini can edit data fields but cannot touch files, code, or the application framework, wrap your legacy database modification logic in a dedicated BoxLang AI tool execution loop.
- Expose only raw data components: Only register data-centric components with the BoxLang AI agent.
- Context isolation: BoxLang AI isolates system contexts, so Gemini sees only the specific functions you register via bx-ai. It has zero visibility into the underlying BoxLang JVM runtime, application maps, or file headers.
Step-by-Step Implementation with BoxLang AI
1. Configure the Gemini Provider
In your configuration file (or programmatically at app startup), specify Google Gemini as your LLM driver.
cfml
application.aiConfig = {
"provider": "google",
"model": "gemini-2.5-flash",
"apiKey": "#server.system.environment.GEMINI_API_KEY#"
};
2. Create the Legacy CFML Tool Component (ContentEditor.cfc)
Write a clean, isolated component that handles the actual content-editing task. BoxLang uses metadata annotations to communicate tool definitions to Gemini.
cfml
/**
* Updates the text body of a specific SaaS record.
* @recordID The unique primary key or UUID of the content row.
* @newText The finalized text generated by Gemini to inject into the row.
*/
public string function modifyRecordText( required string recordID, required string newText ) {
// 1. SECURITY CRITICAL: Session/Role authorization check
if ( !structKeyExists( session, "userID" ) ) {
return "Error: Unauthorized execution block.";
}
// 2. Perform safe, isolated SQL write
try {
queryExecute(
"UPDATE saas_user_content SET body_content = :newText WHERE id = :id AND user_id = :uid",
{
newText: { value=arguments.newText, cfsqltype="cf_sql_clob" },
id: { value=arguments.recordID, cfsqltype="cf_sql_varchar" },
uid: { value=session.userID, cfsqltype="cf_sql_varchar" }
}
);
return "Success: Database updated successfully.";
} catch ( any e ) {
return "Database insertion failure: " & e.message;
}
}
}
3. Execute via the BoxLang AI Agent
In your controller or execution script, register the ContentEditor.cfc as an active tool inside the BoxLang AI interface. BoxLang automatically converts your component methods into the function schemas Gemini expects.
cfml
aiService = new bx.ai.models.AIService( application.aiConfig );
// 2. Create an agent and bind our safe legacy tool component
agent = aiService.newAgent(
systemInstruction = "You are a content assistant. You format, correct, and edit user content strings. You have NO permission to manage software infrastructure or access code files."
);
// BoxLang registers the component methods natively as tools Gemini can read
agent.addTool( new path.to.components.ContentEditor() );
// 3. Process incoming prompt
userPrompt = "Please clean up the grammar inside item ID: ABC-12345";
aiResponse = agent.chat( userPrompt );
// BoxLang automatically intercepts Gemini's tool requests, matches it to your
// component method, executes the SQL, and handles the chat loop.
writeOutput( aiResponse.getText() );
Why BoxLang Safeguards Your Legacy Codebase
- Automatic Reflection Parsing: BoxLang reads the doc comments (@recordID, @newText) of your legacy components natively. It writes the precise JSON schemas Gemini needs without exposing your component's internal SQL or actual source path.
- Prompt Injection Defence: Because you execute database updates using standard BoxLang queryExecute bindings, any attempt by a user to manipulate Gemini into writing malicious SQL code is stopped via parameter isolation at the JVM database driver level.
- Process Isolation: The model never touches file interpreters or runtime evaluations (evaluate()). It is entirely a "data-in, data-out" loop passing string arguments back into your custom CFML method.
Gemini: Are you using CommandBox to manage this runtime environment, or are you utilizing the new BoxLang Spring Boot Starter / MiniServer configurations? Let me know if you want help mapping environment secrets or handling cross-origin requests securely.
Mike: Is there a need to create a MCP server in this situation?
Gemini: No, you do not need to create an MCP (Model Context Protocol) server for this specific situation.
Since you are running your legacy CFML codebase on BoxLang, you can use the built-in bx-ai module to handle Gemini’s function calling out of the box. Creating an independent MCP server would add unnecessary architectural overhead.
The distinction between the two approaches explains why the native BoxLang AI method is more effective for your current setup:
1. Traditional Native BoxLang AI (bx-ai) Approach
In the solution detailed above, you use BoxLang's native AI orchestration.
- How it works: Your SaaS application initiates the API call to Gemini, provides the component tools directly, intercepts the response, and runs the local CFML code.
- Why it's sufficient: The AI loop is fully contained inside your existing application runtime. There is no middleman.
2. The MCP (Model Context Protocol) Alternative
The Model Context Protocol (introduced by Anthropic) is designed to solve the "N×M integration problem"—enabling any AI model to talk to any software platform via a universal, standardised JSON-RPC protocol without custom API adapters.
If you were to build an MCP server for your SaaS, the architecture would look like this:
Gemini AI Client ⇄ Your Custom BoxLang MCP Server (JSON-RPC) ⇄ Your SaaS Database
Head-to-Head Comparison
| Feature | Native BoxLang AI (bx-ai) | Building a Custom MCP Server |
|---|---|---|
| Architectural Overhead | Very Low. Built directly into your existing app. | Medium. Requires setting up a persistent HTTP/SSE or STDIO listener. |
| Security Handling | Easier. Inherits your user's live session (session.userID) naturally during the web request. | Harder. You must implement a separate token-auth protocol so the MCP server knows which tenant's data is being edited. |
| When to use it | When you control the app interface and want Gemini to act as an integrated feature inside your SaaS. | When you want external AI clients (like Claude Desktop, Cursor, or ChatGPT) to connect directly into your SaaS database. |
The Only Exception: BoxLang's Built-In bx-mcp
The creators of BoxLang (Ortus Solutions) shipped a native module called bx-mcp. However, this module is specifically used to expose BoxLang runtime diagnostics and server monitoring to AI coding assistants (like Cursor or Claude Desktop) so they can help you debug your server. It is not meant for business-logic data editing inside a SaaS platform.
Final Recommendation
Stick to the native BoxLang AI tool registration (agent.addTool()) approach. It keeps your security boundaries tied straight to your legacy CFML session state, eliminates the need to route traffic through external protocol translators, and requires significantly less code to maintain.
Gemini: Is this Gemini integration meant to be a user-facing feature inside your SaaS dashboard, or is it an internal automation tool for your development team? Knowing the end-user environment helps determine the best way to handle user authentication.
Mike: It will be a user-facing feature inside the SaaS dashboard. Drafting emails, writing reports, etc.
Gemini: Since this is a user-facing feature inside your SaaS dashboard, security boundaries shift from protecting infrastructure to protecting tenant data isolation. You must guarantee that User A cannot trick Gemini into modifying or reading User B's content (a vulnerability known as Indirect Prompt Injection).
Using the native BoxLang AI Agent framework, your legacy CFML codebase can safely contain this interaction within the user's active session.
The 3-Tier Security Architecture
Because Gemini operates statelessly, it doesn't know who is logged into your application. Your CFML controller acts as the gatekeeper by wrapping the execution cycle:
[ User Dashboard ]
│ 1. Submits Prompt
▼
[ BoxLang Controller ] ──( 2. Restricts Tool to Session User ID )──► [ Gemini API ]
│ │
│ 4. Executes SQL restricted by User ID │ 3. Returns Function Call
▼ ▼
[ SaaS Content DB ] ◄──────────────────────────────────────────────────────┘
Production-Ready BoxLang Implementation
Here is how you handle the user's dashboard request safely. This ensures Gemini can draft emails and write reports only within the strict scope of that specific logged-in user.
1. The Secure Legacy Component (ContentManager.cfc)
To lock Gemini down, the component's internal SQL must strictly filter on the user's session identifier.
cfml
/**
* Creates a brand new document draft (like an email or report) for the logged-in user.
* @title The descriptive title of the document, email, or report.
* @body The AI-generated text body content.
*/
public struct function createDraftDocument( required string title, required string body ) {
// 1. HARD SECURITY WALL: Verify user is actively authenticated
if ( !structKeyExists( session, "userID" ) || !len( session.userID ) ) {
return { "success": false, "message": "Authentication required." };
}
try {
// 2. TENANT ISOLATION: The session.userID is hardcoded into the write operation
var newID = createUUID();
queryExecute(
"INSERT INTO saas_documents (id, user_id, title, body_content, status, created_at)
VALUES (:id, :uid, :title, :body, 'draft', NOW())",
{
id: { value=newID, cfsqltype="cf_sql_varchar" },
uid: { value=session.userID, cfsqltype="cf_sql_varchar" },
title: { value=arguments.title, cfsqltype="cf_sql_varchar" },
body: { value=arguments.body, cfsqltype="cf_sql_clob" }
}
);
return {
"success": true,
"message": "Draft securely created.",
"documentID": newID
};
} catch ( any e ) {
return { "success": false, "message": "Failed to save draft system-side." };
}
}
}
2. The Dashboard Endpoint (/api/generate.cfm)
When the user clicks "Draft Email" or "Write Report" on the UI, your legacy backend endpoint intercepts the action, provisions the BoxLang AI agent, and executes the sequence.
cfml
<cfcontent type="application/json">
<!--- Basic session protection checklist --->
<cfif !structKeyExists( session, "userID" )>
<cfset writeOutput( serializeJSON({ "error": "Unauthorized session context." }) )>
<cfabort>
</cfif>
<!--- Read incoming AJAX request payload from dashboard front-end --->
<cfset requestBody = toString( getPageContext().getRequest().getInputStream() )>
<cfset payload = isJSON( requestBody ) ? deserializeJSON( requestBody ) : {}>
<cfset userPrompt = structKeyExists( payload, "prompt" ) ? payload.prompt : "">
<cfif !len( trim( userPrompt ) )>
<cfset writeOutput( serializeJSON({ "error": "Prompt cannot be empty." }) )>
<cfabort>
</cfif>
<!--- Orchestrate the BoxLang AI Agent --->
<cfscript>
// Initialize BoxLang AI configuration
aiConfig = {
"provider": "google",
"model": "gemini-2.5-flash",
"apiKey": server.system.environment.GEMINI_API_KEY
};
aiService = new bx.ai.models.AIService( aiConfig );
// Inject persona boundaries preventing infrastructure manipulation
agent = aiService.newAgent(
systemInstruction = "You are an embedded text generation feature inside our SaaS dashboard. Your role is strictly to draft emails, generate reports, or format user notes. Use the provided creation tool when the text is ready."
);
// Register our secure, session-aware component
contentTool = new path.to.components.ContentManager();
agent.addTool( contentTool );
try {
// Execute the processing loop
// BoxLang handles: Prompt -> Gemini -> Tool Call execution -> Final message
aiResponse = agent.chat( userPrompt );
// Return clear status back to your Javascript dashboard UI
writeOutput( serializeJSON({
"status": "success",
"message": aiResponse.getText()
}));
} catch ( any e ) {
writeOutput( serializeJSON({
"status": "error",
"message": "An error occurred processing the AI generation request."
}));
}
</cfscript>
Core Rules for User-Facing UI Security
- Never Pass Document IDs blindly from Gemini: If you allow Gemini to edit existing reports, always add a server-side cross-check inside your CFC method to ensure that the document's user_id matches the current session.userID before running an UPDATE query.
- Sanitise Output Rendering: When displaying Gemini's output inside your web application's email template editor or dashboard UI, ensure your frontend escapes the returned string or uses text-only content inputs. This blocks potential HTML/XSS injection vulnerabilities if Gemini accidentally reads a malicious script from external text input.
- Keep the UI Asynchronous: Generating comprehensive reports can take a few seconds. Ensure your dashboard interface triggers a loading spinner or streaming layout while waiting for this endpoint to reply.
Gemini: How do you intend to present the output to your users? Will they see the final report saved quietly in the background to their file list, or should Gemini stream the text dynamically onto their screen inside a text editor layout?
Mike: Both options are required.
Gemini: To support both workflows seamlessly, structure your BoxLang backend to handle them dynamically.
When streaming content directly into a UI editor, you want Gemini to respond with raw text as fast as possible. When saving files silently in the background, you want Gemini to process the text and automatically invoke your secure ContentManager.cfc tool to write to the database.
Here is how you can implement both approaches within your legacy BoxLang infrastructure.
Architecture Strategy
You can use a simple URL parameter or request payload field (e.g., mode=stream or mode=background) to route the request:
- Stream Mode: Bypasses BoxLang's automated tool system entirely. It hooks into BoxLang AI's underlying streaming mechanism to push real-time text chunks straight to the client browser via Server-Sent Events (SSE) or a flushing response buffer.
- Background Mode: Utilises the Function Calling setup we built previously. It tells the agent to orchestrate the generation, save it quietly via your secure ContentManager.cfc, and return a simple "Success" payload.
The Dual-Mode Endpoint (/api/generate.cfm)
This unified legacy endpoint routes incoming dashboard requests based on the user's intent.
cfml
<!--- Session and validation guardrails --->
<cfif !structKeyExists( session, "userID" )>
<cfcontent type="application/json">
<cfset writeOutput( serializeJSON({ "error": "Unauthorized" }) )>
<cfabort>
</cfif>
<cfparam name="url.mode" default="background"> <!--- Options: 'stream' or 'background' --->
<cfset userPrompt = form.prompt ?: "">
<cfscript>
// Initialize standard BoxLang AI Configuration
aiConfig = {
"provider": "google",
"model": "gemini-2.5-flash",
"apiKey": server.system.environment.GEMINI_API_KEY
};
aiService = new bx.ai.models.AIService( aiConfig );
// ----------------------------------------------------
// OPTION 1: STREAM MODE (Direct to Text Editor UI)
// ----------------------------------------------------
if ( url.mode EQ "stream" ) {
// Change content type to allow dynamic browser streaming
cfcontent( type="text/event-stream" );
agent = aiService.newAgent(
systemInstruction = "You are an AI text editor assistant. Generate the requested text directly. Do NOT attempt to run tools or database commands."
);
// Request a stream from BoxLang AI
// BoxLang stream chunks are iterated natively
agent.chatStream( userPrompt, function( chunk ) {
// Write each text fragment directly to the dashboard UI
writeOutput( "data: " & chunk.getText() & "#chr(10)##chr(10)#" );
cfflush(); // Force the server buffer to send the text chunk immediately
});
cfabort();
}
// ----------------------------------------------------
// OPTION 2: BACKGROUND MODE (Save Silently to DB)
// ----------------------------------------------------
if ( url.mode EQ "background" ) {
cfcontent( type="application/json" );
agent = aiService.newAgent(
systemInstruction = "You are a background generator. Take the prompt, create the required content, and immediately use the createDraftDocument tool to save it."
);
// Bind the secure tool component we created earlier
contentTool = new path.to.components.ContentManager();
agent.addTool( contentTool );
try {
// Gemini creates content, triggers tool, BoxLang updates database
aiResponse = agent.chat( userPrompt );
writeOutput( serializeJSON({
"status": "success",
"message": "Your request has been generated and filed to your dashboard document list."
}));
} catch ( any e ) {
writeOutput( serializeJSON({ "status": "error", "message": "Failed background execution loop." }));
}
cfabort();
}
</cfscript>
Frontend JavaScript Connections
Handling Option 1 (Streaming into a UI Rich Text Editor)
To consume the streaming API on your client dashboard web page, use JavaScript's EventSource api or a standard fetch body reader:
javascript
const editorElement = document.getElementById('my-rich-text-editor');
editorElement.innerHTML = "Generating draft...";
const response = await fetch('/api/generate.cfm?mode=stream', {
method: 'POST',
headers: { 'Content-Type': 'application/x-www-form-urlencoded' },
body: new URLSearchParams({ prompt: promptText })
});
const reader = response.body.getReader();
const decoder = new TextDecoder();
editorElement.innerHTML = ""; // Clear loader
while (true) {
const { value, done } = await reader.read();
if (done) break;
let chunk = decoder.decode(value);
// Clean out SSE data prefix formatting if necessary
let processedText = chunk.replace(/^data:\s*/gm, '');
// Append raw content directly onto the editor screen in real-time
editorElement.append(processedText);
}
}
Handling Option 2 (Background Generation Banner)
For background workflows, a standard asynchronous fetch request alerts the user when the document safely lands in their dashboard portfolio.
javascript
showGlobalToast("Generating your report in the background...");
fetch('/api/generate.cfm?mode=background', {
method: 'POST',
headers: { 'Content-Type': 'application/x-www-form-urlencoded' },
body: new URLSearchParams({ prompt: promptText })
})
.then(res => res.json())
.then(data => {
if(data.status === "success") {
showGlobalToast("Finished! Your document is ready in your list.");
refreshDocumentTableGrid(); // Reloads legacy dashboard table data
}
});
}
The industry standard for embedding Gemini into a custom web UI is a Hybrid API + Frontend Component architecture. Your BoxLang backend acts as the secure proxy to the Gemini API, and your frontend uses JavaScript to render a tailored, styled chat or text interface.
Recommended Frontend Integration Paths
Depending on how integrated you want the feature to feel, you have three primary architectural choices:
1. Pre-built Custom Web Components (Easiest & Cleanest)
Instead of building a text box and message bubbles from scratch, you can use open-source, framework-agnostic UI component libraries designed exactly for this.
- The Tools: Use libraries like Vercel AI SDK UI components or Deep Chat Core.
- How it works: You drop a custom HTML tag (e.g., <deep-chat>) into your legacy .cfm dashboard page. You point its url property to your /api/generate.cfm endpoint. The component handles layout, streaming chunks, loading skeletons, and markdown formatting natively.
<script type="module" src="https://unpkg.com"></script>
<div class="dashboard-panel">
<h3>SaaS AI Copywriter</h3>
<!-- The component automatically handles user input and streams your BoxLang response -->
<deep-chat
request='{"url": "/api/generate.cfm?mode=stream", "method": "POST"}'
stream="true"
initialMessages='[{"text": "Hi! Let me help you draft your next email or report.", "role": "ai"}]'
style='{"borderRadius": "8px", "border": "1px solid #ccc", "height": "500px"}'>
</deep-chat>
</div>
2. Native Dynamic Text Injection (For In-Line Editors)
If your goal is to let users click a button (e.g., "Write a summary of this report"), you shouldn't use a chat window at all. Use inline JavaScript to inject text directly into your existing editor layout.
- The Tools: Standard JavaScript fetch() API combined with your existing text editors (like TinyMCE, CKEditor, or Quill).
- How it works: When the user clicks the "AI Assist" button, your JavaScript calls your BoxLang stream endpoint and updates the text editor's content block-by-block using the Reader API shown in the previous response. This creates a seamless "AI is writing inside my document" effect.
3. Custom Chat Assistant Widget (For Floating Help Panels)
If you want an expandable, floating AI helper in the bottom corner of your SaaS dashboard, you can build a small HTML/CSS chat container.
- How it works: You manage a message array in JavaScript. When the user types a prompt and hits send, your code appends a user-message bubble to the DOM, fires the AJAX request to BoxLang, and streams the response directly into a newly appended ai-message bubble.
Why This Architecture Keeps Your SaaS Secure
By routing everything through your BoxLang backend and rendering natively on the client side:
- API Key Masking: Your Google Gemini API key stays safe on your server inside BoxLang (server.system.environment.GEMINI_API_KEY). It never appears in the browser's developer inspector.
- Seamless Styling: The AI feature matches your SaaS design system, fonts, and colours perfectly.
- State Control: You maintain full control over the data loop, so you can save copies of the generated text to your database before it even touches the user's screen.
Gemini: Which style fits your vision best? A floating conversational chat assistant, a standalone copywriting page, or contextual buttons embedded right inside your text input fields? I can give you the exact HTML/CSS boilerplates to start wiring it up.
... to be continued ... 😎😎😎😎
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