Gemini Enterprise: The Sleeper Powerhouse of Agentic AI

I am incredibly bullish on the prospects for Gemini Enterprise becoming the "front door" for the Agentic AI era in enterprise.

If Google plays this right, and assuming the product team is forced to read the canonical Google Platform Rant every morning and evening, they are uniquely positioned to answer the industry's burning question: "Where for crying in a bucket are the actual productivity gains?"

The timing couldn’t be better for Google to compete aggressively. Microsoft Copilot is currently reputationally battered, bleeding user trust for being "oversold and underpowered." Meanwhile, Salesforce is walking back their bold "Agentforce" claims as Benioff’s bluster meets the cold reality of enterprise implementation.

The Right Moves

So far, the Gemini team is making the right strategic moves:

• Speed-running the 'Open Source the Complements' Playbook: They are aggressively pushing standards like A2A (Agent-to-Agent), ADK (Agent Development Kit), and A2UI (Agent-to-UI).
• Velocity: They are shipping so fast that the product still creaks at the edges (which, in this market, is a feature, not a bug).
• Brand Alignment: Killing the ill-conceived "AgentSpace" name to align with the stronger "Gemini" family was a crucial win. Kudos to whoever put their career on the line to win that internal holy war.
• Cloud-Native Distribution: Distributing through Google Cloud, within the standard Resource Hierarchy rather than an orphaned subscription like Gemini Code Assist, aligns perfectly with the existing partner ecosystem.

However, to strictly dominate "workflows" and truly deliver on the agentic promise, there are several no-brainer wins and necessary evolutions required. Here is the roadmap for Gemini Enterprise to win the war.

1. Quick wins

There are few things that would feel like they should be quick wins that would deliver immediate and differentiating benefits.

• Sub-Agents & Multiagent Interoperability: We need the ability to connect custom agents as "sub-agents" within the low-code builder. Google has done the heavy lifting on the A2A protocol; now it needs to be surfaced in the UI. Taking inspiration from Claude Code, the main "Router" agent should be able to dynamically suggest and route tasks to specialized sub-agents.
• Scoped Retrieval (Folders): Grounding needs more granularity. If I build a "Compliance Agent," I want to ground it specifically in a folder of documents that acts as a living context, rather than a static upload. Ideally this should work for both the Drive and Sharepoint connectors.
• Custom URLs: A simple but essential branding win for enterprise adoption: let us customize the app URLs. People are lazy. Asking them to bookmark something like  “https://vertexaisearch.cloud.google.com/home/cid/e1f5c27e-8064-452e-8146-38877211a674” creates an adoption nightmare. It’s even worse when signing in using Workforce Identity Federation.

2. Triggers: Agents vs. Workflows

If this "Agentic AI" paradigm is real, agents must strictly dominate workflows. We shouldn’t need another Zapier or n8n; we need intelligent, event-driven agents.

• Native Hooks: We need the ability to create "Triggers" where specific events call an agent with a prompt and context.
  
  • Email matching a specific sender/subject.
  • Document added to a specific Drive folder.
  • Row added to a Google Sheet.
• The ServiceNow Standard: ServiceNow already handles triggers elegantly in their agent builder. This should be a "High Priority" feature request. This is the missing link between a chatbot and a true digital employee.

3. A2UI: The Future of Internal Apps

It is becoming obvious that A2UI (Agent-to-UI) is the future of internal enterprise applications. The era of creaking, low-code app platforms that promised much and delivered little is ending.

• Custom Components: While Gemini Enterprise will adopt standard components, we need the ability to extend the interface with custom components. These should function as A2UI-compliant "plugins," allowing developers to build bespoke interfaces that the agent can render on demand.

4. UX: Moving Beyond the "Chatbot"

The Gemini Enterprise UX still feels too "Chatbotty." Software engineering is the discipline doing the hard R&D on UX right now, and the direction of travel is clear: longer time horizons (minutes and even hours, not seconds), parallel operations, and the human serving as a "conductor."

• Native HITL (Human-in-the-Loop): We need a genuine Human-in-the-Loop experience. Taking a leaf from ServiceNow, we should be able to set execution modes like "Supervised" vs. "Autonomous" for specific agents or even sessions.
• Session Management: Simply showing a chat history doesn't cut it. We need "Kanban-style" boards or at least some form of visual prompt to show where an Agent is stalled, waiting for input, or processing a long-running task (similar to the Deep Research features in the consumer app).

5. Power Agents & The MCP Trend

Google is wasting an opportunity to ship powerhouse "workflow primitives" as out of the box sub-agents and tailored Workspace MCP endpoints.

• Unbundle the Magic: Features like "Generate Slides" in NotebookLM are magical. Why force us to thread requests through a specific app? Unbundle these capabilities into standalone sub-agents we can call via Gemini Enterprise or integrate using the low-code Agent builder.
• Build Primitive “Utility” Sub-Agents to Expand Capabilities: For example, large videos or meeting recordings are often a fantastic source of context, but all too often fall afoul of context window limitations. My current workflow involves downloading the videos from Drive, running a ffmpeg script to downsample the video, re-uploading to Drive and then calling the agent again. It’s not hard to imagine a “utility” style sub-agent that has at its disposal an array of video manipulation tools for just such a use case. “PII Redaction” would also be a fantastic utility. Or what about a “Citation Hunter” that checks cited sources in Deep Research reports for hallucinations or low quality. A “Get Information from a URL”  utility would be incredibly helpful (although I’d understand Google’s reticence on this one). Or perhaps a dedicated “Localisation / Translation” utility sub-agent that optimally wraps the latest and greatest combination of LLM and API. 
• Model Context Protocol (MCP): The current "actions" list is pitiful. The list of remote MCP servers is growing daily. We are seeing enterprises buy Cursor licenses simply because it's the easiest way to plug an MCP server into an agent loop. That is bonkers. Please extend the low-code builder with the ability to easily "attach" approved MCP servers.
• Workspace Integration: The raw APIs aren't enough. We need agent-tailored tools for the entire suite, Docs, Slides, Calendar, capable of complex actions like "suggest times" or "edit slide layout."

6. Auth, Auth, Baby

As wonderful as "synced" connectors are, Federated is the future.

• OAuth UX: Authentication questions won't stop at the data layer. They will arise with custom agents and tools. We need better conventions for how custom agents trigger OAuth flows, how the UX handles that request, and how tokens are securely stored and passed.
• Security by Default: Don't believe the Vertex AI Discovery Engine Kool-Aid; data is going to stay in the system of record. Syncing ACL data is nice, but not a scalable approach across a fragmented systems. To quote Jamin Ball, we need to look at what makes cloud architecture robust. Making every layer of auth secure and easy by default is key.

7. Testing and Simulation

Finally, we need professional-grade testing environments. There is a lot more that Gemini Enterprise could be doing to help make like easier for “Agent Managers” (both low-code and fully custom) including:

• Evals / Testing: Making it easier to store and evaluate and test against “happy paths”. It’s important this happen in Gemini Enterprise in addition to the standard “dev tooling” style evals to be able to effectively evaluate alongside the user context provided through connectors.
• Dry Runs: Agents should be able to run in "Dry Run" mode, where tools only simulate state changes (similar to the validate_only flag in Google AIP-163).
• Diagnostics: Sessions should save extra diagnostic information, including reasoning traces and tool inputs/outputs, allowing for post-mortem analysis and feedback loops. Make it easier to “share” a session’s diagnostics with the Agent Manager. 

Gemini Enterprise has the potential to be the operating system for the AI-native enterprise. The foundation is solid, and the philosophy is correct. Now, they just need to fill in the gaps.

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