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One of the most exciting developments in the realm of Artificial Intelligence is AI agents—intelligent assistants that can automate tasks, provide insights, and enhance productivity. If you’re unfamiliar with AI agents and Google Agentspace, this guide will break it down for you.

What are AI Agents?

Google Agentspace is an enterprise AI platform that combines Google’s search technology, Gemini’s advanced reasoning, and enterprise data to create intelligent AI assistants. These AI-powered agents allow employees to interact with company knowledge using natural language and take action across applications.

Unlike large language models (LLMs) such as Gemini and ChatGPT, which focus on generating responses based on probabilistic predictions, AI agents go beyond just generating responses—they take actions, execute workflows, and integrate directly with enterprise systems. In other words, AI agents don’t just give suggestions—they actually perform tasks based on enterprise data and business applications.

What is Google Agentspace?

Google Agentspace is an enterprise AI platform that combines Google’s search technology, Gemini’s advanced reasoning, and enterprise data to create intelligent AI assistants. These AI-powered agents allow employees to interact with company knowledge using natural language and take action across applications.

🔹 Blended enterprise search – Find relevant information across internal and external sources
🔹 Automated workflows – AI agents handle research, content generation, and planning
🔹 Custom and pre-built AI agents – Employees can use Google’s expert AI agents or build their own

Key Features of Google Agentspace

1. Enterprise Search Across Multiple Platforms

Modern organizations use various tools for project management, collaboration, and content storage. Google Agentspace integrates with first- and third-party applications, including Google Drive, SharePoint, Jira, Salesforce, and more. It uses advanced search and AI reasoning to find relevant information across structured and unstructured data.

2. Multimodal AI

• 📄 Text – Understands and retrieves relevant information from documents, reports, and chat logs
• 🎨 Images – Allows users to upload images and ask questions about their content (e.g., extracting text from an invoice or analyzing a company logo)
• 🎙 Audio – Can summarize recorded meetings or provide podcast-like insights
• 🎥 Video – Extracts key insights from video content, making it easier to process large amounts of information

Google Agentspace goes beyond traditional text-based search by incorporating multimodal AI, meaning it can process, analyze, and generate insights across different types of data, including:

3. Pre-built and Custom Agents

In addition to multimodal AI, Google Agentspace provides a suite of pre-built AI agents designed to automate complex enterprise tasks. You can also create your own AI agents using low-code or no-code frameworks

Key Benefits of Pre-Built Agents:

🔹 Grounded in Enterprise Data – Unlike generic chatbots, these agents operate using real-time company data.
🔹 Task-Oriented – Instead of just providing information, they can perform actions in enterprise applications.
🔹 Integrated with Google & Third-Party Tools – Agents work across Google Workspace, Jira, Salesforce, ServiceNow, and other business platforms.

4. NotebookLM for Research and Data Synthesis

NotebookLM is a built-in tool within Google Agentspace that helps users summarize and extract insights from documents. Employees can upload PDFs, URLs, tests, and audio files, and the AI can:
✅ Generate structured summaries
✅ Provide citations for retrieved information
✅ Create interactive responses for deeper analysis

5. Security, Compliance, and Access Control

Google Agentspace is built with enterprise-grade security and compliance standards to ensure data privacy. Features include:

• Role-based access control (RBAC) – Ensures employees can only access relevant data
• Data encryption – Protects data both at rest and in transit
• Regulatory compliance – Meets standards like GDPR, HIPAA, and VPC Service Controls

How to Get Started with Google Agentspace?

Google Agentspace is available in multiple tiers, including:

• NotebookLM Plus Enterprise – Provides enhanced research tools with NotebookLM
• Google Agentspace Enterprise – Enables enterprise search and multimodal AI interactions
• Google Agentspace Enterprise Plus – Adds advanced AI automation, research agents, and action execution

Interested in learning more and onboarding to Google Agentspace? Contact Premier Cloud today to learn more about Google Agentspace and how AI agents can transform your enterprise productivity.

VICTORIA, BC. February 12, 2025 — Premier Cloud is proud to be named in CRN’s 2025 Managed Service Provider (MSP) 500 list in the Pioneer 250 category. This recognition highlights our continued commitment to delivering secure, scalable cloud and IT solutions that enhance efficiency and drive business success.

CRN’s MSP 500 list identifies top North American managed service providers that lead innovation in IT. The Pioneer 250 category acknowledges MSPs that support small and mid-sized businesses with cutting-edge technology solutions that help them navigate evolving IT challenges and stay competitive.

Premier Cloud specializes in Google Cloud and AI-driven solutions, helping businesses streamline IT operations, enhance security, and maximize technology investments. By offering proactive support, managed cloud services, and AI-powered automation, we ensure businesses can optimize performance while focusing on growth.

Premier Cloud’s client-centric approach is at the heart of our growth strategy, ensuring we consistently meet and exceed our clients’ expectations with each interaction. We are eager to continue expanding and evolving while strengthening our connections with clients and partners. Our dedication to delivering exceptional solutions that drive business growth and innovation remains unwavering.

See the full list here: https://www.crn.com/rankings-and-lists/msp2025

For more information, please contact: 
Alex Shahbazfar
alex.s@premiercloud.com 
888-866-2230

1. Introduction

Real-world applications often require access to dynamic and constantly changing information. This tutorial focuses on integrating external data sources, specifically APIs, into Vertex AI Agent Builder, unlocking possibilities for truly intelligent conversational experiences. By doing so, your agents can access real-time information, stay relevant, and provide accurate responses.

2. Understanding the Basics

What is Vertex AI Agent Builder?

Vertex AI Agent Builder is a platform for creating AI agents capable of interacting with and understanding the world. It supports natural language queries, integrates enterprise data, and becomes even more powerful with external data sources like APIs.

Static vs. Dynamic Data

Static data, such as PDFs or CSV files, quickly becomes outdated, limiting its use. For example, an e-commerce support agent relying on static data can’t provide live updates on product availability or shipping timelines. Dynamic sources, like APIs, offer real-time information, enabling agents to respond effectively to evolving needs.

3. Preparing for Integration

API Setup

To illustrate dynamic data integration, we use a Warehouse Address API. This API retrieves a warehouse’s address based on its ID, offering real-time updates. It is implemented using Flask and deployed on Cloud Run.

The configuration is defined in a YAML file, which acts as a blueprint for the API. It includes details like API paths, request parameters, and response formats. You can check the YAML file given below:

openapi: 3.0.0
info:
 title: Warehouse Address API
 version: v1
servers:
 - url: 'https://my-warehouse-api-zycua53bta-uc.a.run.app'
paths:
 /warehouses/{warehouseId}/address:
   get:
     summary: Get Warehouse Address
     description: Retrieves the address of a warehouse by its ID.
     parameters:
       - in: path
         name: warehouseId
         schema:
           type: integer
           format: int64
         required: true
         description: The ID of the warehouse.
     responses:
       '200':f
         description: Successful response with the warehouse address.
         content:
           application/json:
             schema:
               type: string
               description: The full address of the warehouse.
       '404':
         description: Warehouse not found.

This YAML file is crucial for integrating the API with Vertex AI Agent Builder.

Connecting Your API

1. Open the Agent Builder console.
2. Navigate to Tools and create a new tool.
3. Enter the tool’s name, type (OPENAPI), and description.
4. Paste the YAML file content and save it.
5. Proceed to create your agent.

4. Building and Testing Your Agent

Agent Setup

1. Create a new agent in Vertex AI.
2. Define its name, goal, and step-by-step instructions.
3. Reference the tool created earlier using the format: ${TOOL: Tool_name}.

Test your Agent

Just like any software, agents need thorough testing. This helps identify bugs in their code, understand their current capabilities, and most important, simulate real interactions with users to refine the overall customer experience. 

While you are in the console, go ahead and select the appropriate agent and select the generative model of your choice and you now ask questions to the agent and the response will be coming from the API.

5. Publishing the Chat App

Now that the Chat App is up and running, it’s time to publish it and embed it into our website. To publish your Chat App, click on the Publish button which will prompt you with the following:

You have to specify the agent environment, followed by the Access type and the UI Style that you want. Once you have all of this set, go ahead and click on Enable the unauthenticated API.

6. Conclusion

This tutorial demonstrated how to integrate external data sources into Vertex AI Agent Builder, using APIs to create dynamic conversational agents. By following these steps, you can build intelligent chat applications that adapt to real-time data and deliver superior user experiences.

By Aryan Irani

We are thrilled to announce that Ryan Brady, Premier Cloud’s VP of Sales, has been recognized as one of CRN’s Next-Gen Solution Provider Leaders for 2024! 

This prestigious honor spotlights young professionals under 40 already making a significant impact within the IT channel. Ryan’s drive, vision, and dedication to innovation have played an instrumental role in shaping Premier Cloud’s strategic direction and elevating our position in the marketplace.

Key Accomplishments That Set Us Apart

Over the past year, Ryan has led numerous initiatives that have dramatically diversified Premier Cloud’s product and service portfolio, creating multiple new revenue streams. We are proud to be a top seller in North America for sales of Google’s new Gemini product. This is a testament to our ability to stay ahead of industry trends and meet market demands for cutting-edge AI solutions.

Under Ryan’s leadership, we’ve also attained Premier status within the Google Cloud Partner Program and received several Specialization awards for our expertise in delivering Google Workspace and Google Cloud services. These accomplishments not only validate the exceptional quality of our solutions but also highlight our team’s dedication to excellence in every client engagement.

The Role of AI in Our Growth Journey

Artificial intelligence is transforming every industry, and Premier Cloud is at the forefront of this shift. As a reseller of AI solutions, particularly Google’s Gemini for Google Workspace and Vertex AI, we are uniquely positioned to help companies explore, understand, and implement AI meaningfully. Ryan’s vision for AI has enabled us to become trusted advisors in this space, helping clients bring their AI aspirations to life.

Join Us in Celebrating This Milestone

Ryan Brady’s inclusion in CRN’s Next-Gen Solution Provider Leaders list is a significant achievement, not just for him but for everyone at Premier Cloud. It’s a testament to the hard work, dedication, and innovative thinking that defines our team and drives our success. 

We look forward to building on this momentum as we transform the IT channel, empower our clients, and push the boundaries of what’s possible.

By Aryan Irani

As AI continues to evolve, Google’s Gemini AI models, developed by DeepMind, have made it easier for everyone—from individuals to businesses—to benefit from powerful AI tools. Whether you’re looking to speed up simple tasks or take on complex projects, there’s a Gemini model suited to your needs. In this guide, we’ll walk you through the key Gemini models and help you decide which one is right for you.

Meet the Gemini Family

The Gemini models are designed to handle various tasks, from quick, real-time responses to more complex, data-heavy applications. Below, we’ve outlined three of the main models—Gemini 1.5 Flash, Flash-8B, and Pro—so you can quickly see which one fits your requirements.

Quick Comparison of Gemini Models

We’ve made it easy to compare the key features of each model in the table below. Whether you’re after speed, complexity, or enterprise-level performance, this table helps you pick the right option.

What Are Tokens and Why Do They Matter?

Tokens represent pieces of the input or output text that the model processes. Think of tokens as chunks of words, parts of a sentence, or data points. A higher number of tokens means the model can handle longer and more complex tasks, while fewer tokens suit quicker, simpler tasks. For example, if you need to work with large data sets or generate long, detailed outputs, a model with a higher token count—like Gemini 1.5 Pro—would be ideal. On the other hand, for faster, real-time responses in customer service, a lower token count in Gemini 1.5 Flash would work perfectly.

What’s Important to Know About Each Model?

Gemini 1.5 Flash

This model is all about speed. It’s great for tasks that need quick responses, like customer service chatbots or real-time data analysis. Think of it as a fast, reliable worker who delivers results without much delay. If you’re dealing with simple tasks that need to be done right away, this model is a great fit.

Gemini 1.5 Flash-8B

This is the more advanced version of the Flash model. It’s still fast, but with 8 billion parameters, it can handle more complex tasks. If your work involves multitasking or analyzing both text and images at the same time (like in voice assistants or image recognition), this model provides a good balance of speed and complexity.

Gemini 1.5 Pro

For big, enterprise-level tasks, the Pro model is your go-to. It’s designed for companies that need high performance and detailed, accurate responses. Whether you’re working with large datasets, analyzing healthcare information, or generating financial reports, this model offers the power and accuracy needed for critical tasks.

A Glimpse Into Google AI Studio

Google AI Studio is a platform designed to make it easy for you to experiment with these Gemini models. It’s like a testing ground where you can quickly try out different AI models by entering prompts and seeing how the models respond. The interface lets you choose from different models, view the token count, and adjust advanced settings before running your prompts. Once you find something that works, you can even export the code and integrate it into your applications. It’s a great way to see how the different Gemini models perform, helping you pick the right one for your project.

How to Choose the Right Model

When choosing the best Gemini AI model, it really comes down to what you need it to do. Here’s a quick guide to help you decide:

• Need something fast for simpler tasks? Go for Gemini 1.5 Flash. It’s fast and effective for everyday tasks that require quick, real-time responses.
• Want speed but need to handle more complexity? The Gemini 1.5 Flash-8B gives you the speed of Flash with the ability to process more complicated inputs, making it great for advanced chatbots or apps that involve both text and images.
• Working with large-scale, high-accuracy projects? Choose the Gemini 1.5 Pro. This model is perfect for enterprises, handling big data, financial analysis, or in-depth healthcare tasks with ease.

Making the Most of Gemini AI Models

Now that you know the basics, using Gemini AI models is easy. You can experiment with them on platforms like Google AI Studio to get a feel for how they work and see which one suits your needs best. Whether you’re automating customer support, building an AI assistant, or diving into detailed data analysis, there’s a Gemini model ready to help.

By Aryan Irani

Small to mid-size businesses receive PDF invoices from vendors and need to store details like invoice number, date of invoice, total amount, and vendor name. Doing this manually for all your vendors is a tedious and time-consuming process. In this blog we are going to be automating the process of extracting details from PDFs and populating the Google Sheet using the Gemini 1.5 Flash model and Google Apps Script.

The Problem with Manual Invoice Processing

Manually processing invoices is a tedious and inefficient task. It involves poring over countless PDF documents, meticulously extracting data, and entering it into spreadsheets or databases. This process is not only time-consuming but also prone to human errors, which can lead to costly consequences.

Imagine a scenario where your accounts payable team spends hours each week manually processing invoices. This not only delays payments but also increases the risk of errors, potentially damaging relationships with your vendors. Moreover, manual data entry ties up your team’s time, preventing them from focusing on more strategic tasks that drive business growth.

What can this Automation do?

This function is designed to automate the process of manual data entry inside of Google Sheets. This function uses the Gemini 1.5 Flash model to extract required/ specific details from the Google Sheet and populate the Google Sheet. Here’s a high-level overview of how the solution works:

1. PDF Conversion: The Google Apps Script converts your PDF invoices into a format that Gemini 1.5 Flash can easily understand.
2. Data Extraction: The script interacts with Gemini 1.5 Flash, providing it with the converted invoice data and instructions on what information to extract.
3. Spreadsheet Update: The extracted data is seamlessly populated into a Google Sheet, ready for further processing or analysis.

Now that we have understood the problem and the possible solution, let’s get coding.

Sample Google Sheet

The Google Sheet that I will be using for this blog contains the following details:

Write the Automation Script

While you are in the Google Sheet, let’s open up the Script Editor to write some Google Apps Script. To open the Script Editor, follow these steps:

Click on Extensions and open Apps Script:

This brings up the Script Editor:

Now, enter the code:

function processPdfToSheet() {
 var archiveFolderId = "YOUR ARCHIVE FOLDER ID"; 
 const folderId1 = "YOUR FOLDER ID";
 var folder = DriveApp.getFolderById(folderId1);
 var files = folder.getFiles();

 while (files.hasNext()) {
   var file = files.next();
   if (file.getMimeType() === MimeType.PDF) { // Filter PDF files
     var fileId = file.getId();
     var pdfContent = convertPdfToGoogleDoc(fileId, folder);
     var responseData = sendToGemini(pdfContent);
     var details = extractFields(responseData);

     // Update Google Sheet with extracted details
     updateSheet(details);

     // Move the original PDF and the converted Google Doc to the archive folder
     var archiveFolder = DriveApp.getFolderById(archiveFolderId);
     moveFileToArchive(file, archiveFolder);
   }
 }
}

The convertPdfToGoogleDoc() function processes a PDF file by retrieving its content, creating a new Google Doc with extracted text, and then deleting the converted Google Doc to avoid clutter. We use the Drive API and DocumentApp to perform these operations, ensuring accurate text extraction. 

We then return the extracted text content, which can be further processed or used in applications.

function sendToGemini(pdfData) {
 const GEMINI_KEY = 'YOUR_GEMINI_KEY';
 const GEMINI_ENDPOINT = `https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash-latest:generateContent?key=${GEMINI_KEY}`;
 var headers = {
   "Content-Type": "application/json",
   "Accept": "application/json"
 };
 var requestBody = {
   "contents": [
     {
       "parts": [
         {
           "text": `extract the following details: Vendor Name: Invoice Number: Amount Due: Due Date: Description Tax: \n${pdfData}`
         }
       ]
     }
   ]
 };
 var options = {
   "method": "POST",
   "headers": headers,
   "payload": JSON.stringify(requestBody)
 };
 try {
   var response = UrlFetchApp.fetch(GEMINI_ENDPOINT, options);
   var datanew = JSON.parse(response.getContentText());
   return datanew;
 } catch (error) {
   Logger.log('Error calling Gemini API: ' + error);
   return null;
 }
}

We then use the sendToGemini() function to interact with Gemini 1.5 Flash to process and extract data from the text content. We construct a request that contains our prompt that specifies the details we want to extract from the content and then send the request to Gemini 1.5 Flash. 

In case of errors, it logs an error message and returns null. If successful, it returns the extracted details that we received from Gemini 1.5 Flash. 

function extractFields(datanew) {
 if (!datanew || !datanew.candidates || !datanew.candidates.length) {
   Logger.log('No valid data returned from Gemini.');
   return {};
 }

 var textContent = datanew.candidates[0].content.parts[0].text;
 textContent = textContent.replace(/- /g, '').trim();
 var lines = textContent.split('\n');

 var details = {};
 lines.forEach(function (line) {
   var parts = line.split(':');
   if (parts.length === 2) {
     var key = parts[0].replace(/\*\*/g, '').trim();
     var value = parts[1].replace(/\*\*/g, '').trim();
     details[key] = value;
   }
 });

 return details;
}

We use the extractFields() function to process the response from Gemini 1.5 Flash to extract relevant details. We check the validity of the response, extract the text content, split it into lines and iterate through each line to identify key-value pairs. 

These extracted details, such as Vendor Name, Invoice Number, and others, are returned in a structured object for further use.

function updateSheet(details) {
 var sheet = SpreadsheetApp.getActiveSpreadsheet().getSheetByName("Invoices");
 var range = sheet.getDataRange();
 var values = range.getValues();

 var vendorName = details['Vendor Name'];
 var nameFound = false;

 var currentDate = Utilities.formatDate(new Date(), Session.getScriptTimeZone(), 'MM/dd/yy');
 var formattedDateTime = Utilities.formatDate(new Date(), Session.getScriptTimeZone(), "yyyy-MM-dd HH:mm:ss");

 for (var i = 1; i < values.length; i++) {
   if (values[i][2].toLowerCase() === vendorName.toLowerCase()) { // Compare by Vendor Name
     nameFound = true;
     sheet.getRange(i + 1, 1).setValue(details['Invoice Number']); // Column A
     sheet.getRange(i + 1, 6).setValue(details['Amount Due']); // Column F
     sheet.getRange(i + 1, 7).setValue(details['Due Date']); // Column G
     sheet.getRange(i + 1, 9).setValue("Last updated at: " + formattedDateTime); // Column I
     Logger.log("Updated Row " + (i + 1));
     break;
   }
 }

 if (!nameFound) {
   Logger.log("Vendor not found: " + vendorName);
   var newRow = values.length + 1;

   sheet.getRange(newRow, 1).setValue(details['Invoice Number']); // Column A
   sheet.getRange(newRow, 3).setValue(vendorName); // Column C
   sheet.getRange(newRow, 4).setValue(details['Description']); // Column D
   sheet.getRange(newRow, 5).setValue(vendorName); // Column E
   sheet.getRange(newRow, 6).setValue(details['Amount Due']); // Column F
   sheet.getRange(newRow, 7).setValue(details['Due Date']); // Column G
   sheet.getRange(i + 1, 9).setValue("Last updated at: " + formattedDateTime); // Column I
   Logger.log("New Row Added");
 }
}

The updateSheet() function is responsible for managing entries within the Google Sheet. It first checks if the client or vendor already exists in the sheet. If a matching entry is found, the function updates the existing record with the new details. However, if no existing record is found, a new entry is created to store the information.

function moveFileToArchive(file, archiveFolder) {
 file.moveTo(archiveFolder);
}

The moveFileToArchive() function is a simple help function that takes the file and moves it to the archive folder after the data extraction and the Google Sheet is updated. 

Our code is complete and good to go.

Check the Output

It’s time to see if the code is able to access the invoices, extract details and update the Google Sheet successfully. 

Conclusion

In today’s digital age, automation is key to staying competitive. By harnessing the power of Gemini 1.5 Flash and Google Apps Script, you can transform your invoice processing workflow, saving time, reducing errors, and empowering your team to focus on what truly matters.

We encourage you to explore the possibilities of this solution and discover how it can revolutionize your data extraction processes. Remember, automation is not just about efficiency; it’s about unlocking new opportunities for growth and innovation.

To see the full code in one place, click here.

VICTORIA, BC, September 10, 2024 – Premier Cloud is excited to announce the promotion of Jason Murray-Rosel to Head of Client Success. Previously serving as Client Success Manager, Jason will now lead the Client Success team, focused on helping clients unlock the full potential of Google Cloud.

In his new role, Jason will deliver strategies to ensure clients optimize their Google Cloud investments, enhancing scalability, productivity, and overall business performance. With a proven track record of driving results and ensuring customer satisfaction, Jason has been pivotal as the primary point of contact for clients. Under his leadership, the client success team will continue to strengthen Premier Cloud’s client-centric approach, further maximizing the value clients gain from our cloud services and solutions.

Jason’s promotion is a reflection of his dedication, expertise, and ability to guide clients through their cloud journeys. As Premier Cloud continues to grow and expand its services, Jason’s role will be crucial in ensuring that clients not only adopt Google Cloud effectively but also use it to its fullest potential to drive growth and efficiency.

Victoria, BC, August 22, 2024 — Premier Cloud Inc. is proud to announce its newly established partnership with Lumin, a cloud-based e-Signature & document management solution, to elevate the efficiency and functionality of Google Workspace for its clients.

Lumin, known for its seamless integration with Google Workspace, offers a robust suite of tools designed to streamline document workflows. These tools enable real-time collaboration, secure storage, and advanced document editing directly within the Google ecosystem.

Premier Cloud, a leading provider of Google Cloud services, recognizes the importance of meeting the evolving needs of modern businesses. With this partnership, Premier Cloud reinforces its commitment to delivering comprehensive cloud solutions that enhance business operations across various sectors.

Key features of Lumin’s integration with Google Workspace include:

• Efficient eSignature management
• Real-time collaboration for improved workflow
• Merging of multiple documents into a single PDF
• Direct text editing within PDF files
• Scanning capabilities that ensure documents are searchable and readable

This partnership underscores Premier Cloud’s dedication to innovation and customer success by providing tools that drive productivity and simplify document management.

For more information, please contact: 
Alex Shahbazfar
alex.s@premiercloud.com 
888-866-2230