Google Agent Space: A First Look at Google's Enterprise AI Platform

I recently got a sneak peek at Google Agent Space during a demo with a colleague, and I was genuinely impressed by what Google has built. As someone deeply immersed in the AI tech space, I'm always looking for platforms that can actually deliver on the promise of AI agents—tools that can take meaningful action on our behalf across enterprise systems.

Let me share my first impressions and explain why this might be a significant step forward for enterprise AI.

What is Google Agentspace?

At its core, Google Agentspace combines Gemini's reasoning capabilities, Google-quality search, and enterprise data from various sources into a unified interface. It's designed to unlock enterprise expertise through AI agents that can access and act across your organization's systems.

The platform appears to focus on three main capabilities:

1. Enhanced Notebook LM with enterprise security
2. Enterprise-wide search and system connectivity
3. Custom expert agents for specific business functions

NotebookLM: Enterprise-Ready Knowledge Hub

Google has brought NotebookLM (which millions already use) into Agentspace with added security and privacy features for enterprise use. If you've been following my content, you know I've covered NotebookLM before - it's a powerful tool for ingesting and making sense of information.

What didn't make it into the demo but is worth highlighting is that NotebookLM shines when you add links, especially YouTube videos. As a salesperson doing account planning, you can throw in an hour-long interview video with a prospect's executive, which will be transcribed and let you interact with that content.

Rather than rewatching a lengthy video multiple times, you can generate a study guide or briefing document summarizing the executive's priorities and key talking points. This makes preparing for high-stakes meetings infinitely more efficient.

Breaking Down Silos: Connected Enterprise Search

This is where things get particularly interesting. Google Agentspace connects previously siloed systems, allowing you to perform actions across multiple platforms from a single interface.

In the demo, they showed a scenario where the agent could:

• Pull open Jira tickets using pre-built connectors
• Summarize those tickets
• Draft an email in Outlook with the summary
• Send the email - all from within Agentspace

Google has created pre-built connectors for commonly used applications, such as Confluence, Google Drive, Microsoft SharePoint, ServiceNow, and more.

What impressed me most was seeing a workflow seamlessly span three different systems. The big question, of course, is how much configuration is required to set this up. Many enterprise providers claim similar capabilities but require extensive setup and prompt engineering to achieve anything worthwhile.

The Future of Work: Expert Agents for Specific Tasks

The third demonstration showcased what Google calls "expert agents" - purpose-built agents designed to automate specific business functions.

The demo featured an expense report agent that could:

• Find an open expense report
• Accept a new receipt upload
• Extract information from the receipt
• Add it to the report

This is particularly meaningful to me. When I started my career, I was taping receipts to paper, scanning them, and manually typing data into spreadsheets. Even with modern expense apps, there's still tedious work involved. Seeing this level of automation is refreshing.

The demo highlighted an important aspect of current AI agent technology: the human-in-the-loop approach. Rather than agents running entirely autonomously, which we're not quite ready for outside of coding tasks, the system has the human user verify actions and provide necessary input.

My Takeaways

After watching the demo, several things stood out to me:

1. Human-in-the-loop is the near-term reality. Google envisions AI agents as collaborators, not replacements, for now. Users still initiate processes and validate outputs, which is the right approach for now.
2. Breaking down system silos is the real value. The ability to work across multiple systems from a single interface could eliminate countless hours of context-switching and manual data transfer.
3. Specialized agents make more sense than "super agents." Having purpose-built agents for specific functions (research, expenses, etc.) seems more practical than creating a single agent that does everything.
4. The interface matters. Google's clean, intuitive interface significantly improves over many legacy enterprise systems that try to add AI capabilities.

Final Thoughts

Google Agent Space represents a thoughtful approach to enterprise AI. Rather than adding chatbot interfaces to existing tools, it's reimagining how we interact with enterprise systems.

What we're building at Strama aligns with many of these principles - particularly in reimagining outbound sales workflows. By automating personalized prospecting and eliminating manual research, we're applying the same core insight: AI should adapt to how humans naturally work, not force humans to adjust to rigid workflows. While we focus specifically on the sales domain rather than general enterprise functions, the fundamental approach is similar.

Currently, Google Agent Space offers a 30-day free trial for Notebook LM, with other features in early access. If you're using Google Workspace, it's worth getting on the waitlist to try this out when it becomes available.

What do you think about Google's approach to AI agents? Are you excited about tools that can work across different enterprise systems? Let me know in the comments!

Want to see how we're applying similar principles specifically for sales teams? Check out what we're building at Strama to eliminate busywork and streamline sales workflows. strama.ai/onboard