Decoding Google Cloud Next ’25: How Relanto Mirrors Google’s Vision for Enterprise Agentic Intelligence
Stepping into Google Cloud Next ‘25 was like catching a glimpse of enterprise AI five steps ahead. But for us at Relanto, it also felt incredibly familiar — as we’ve been building towards that future every day.
Where the Cloud Is Headed: Infrastructure, Agents, and Intelligence
The opening keynotes set the tone: AI is moving from novelty to infrastructure. Google announced that its personal global private network is now enterprise-ready, offering high-speed, secure connectivity for businesses building AI-native operations. This isn’t just about faster pipes; it’s about giving enterprises the confidence to run AI where their data already resides—with built-in governance, encryption, and low-latency performance.
For IT leaders and data architects, this unlocks something long overdue: infrastructure that’s AI-native but enterprise-grade. No more rerouting sensitive data across geographies or compromising on compliance.
AI doesn’t need to chase the data anymore. It lives where the data lives.
That’s exactly where R-SalesAssist shines. Our platform is built for hybrid and regulated environments—it is designed to work with enterprise guardrails, not around them.
The result? Predictable, secure, and scalable AI deployments that meet IT and compliance expectations from Day 1.
The Gemini 2.5 Moment: Smarter, Faster, More Reasoned LLMs
The Gemini 2.5 family was at the center of Google’s AI push.
- Gemini 2.5 Pro has introduced reasoned responses—it thinks before speaking.
- Gemini 2.5 Flash is built for low-latency, high-efficiency tasks.
- These models are now natively embedded in Vertex AI Studio, making it easier for teams to build custom, intelligent workflows.
For RevOps leaders and sales enablement professionals, this is a game-changer. It’s no longer just about text generation—it’s about thoughtful, contextual interaction, which aligns with how R-SalesAssist processes data before surfacing opportunities, forecasting deal risk, or crafting follow-up suggestions.
AI, But Multimodal
This year made it clear: enterprise AI isn’t just about text anymore.
- Veo 2 created cinematic videos from text prompts
- Imagen 3 delivered photorealistic visuals
- Chirp 3 enabled custom voice creation in under 10 seconds
- Lyria generated short music clips from a few words
The takeaway? Multimodality isn’t a creative tool—it’s a business enabler. For marketing heads, HR leaders, and CX designers, these tools offer new ways to generate internal assets, onboarding content, or product explainers at scale.
With the integration of visual content sources like pitch decks and deal briefs. This multimodal shift opens new doors for contextual deal prep, dynamic coaching, and even AI-generated competitive battle cards.
Spotlight: Google Agentspace and the Rise of Practical AI Agents
Perhaps the most exciting announcement: Google Agentspace, a framework for multimodal, enterprise-grade AI agents.
Agentspace allows secure integrations with platforms like Jira, Drive, SharePoint, and others to:
- Run unified search across tools
- Summarize large knowledge sets
- Take real-time actions using custom-built agents
The impact on productivity operations, knowledge management, and internal support is massive. From our perspective, it strongly validates the design approach behind R-SmartAssist—a key component of our platform.
Our agents already automate internal workflows, pull live CRM data, recommend upsell motions, and drive revenue playbooks across tools. Agentspace shows this isn’t theoretical anymore—it’s the new standard.
Insights from the Expo Floor: Applied AI in Action
Here are some standout demos that made us think deeply about what’s possible now:
1. Prompt Engineering & Context Optimization
A session on prompt design for LLaMA-based models emphasized how structure impacts accuracy, tone, and response granularity. This is an important lesson for teams configuring sales assistants or customer bots—nuance in prompt templates changes user trust.
We constantly test and refine prompt scaffolding inside R-SalesAssist for better deal insights and seller coaching.
2. AI-Powered IDE Agents
Developer-centric agents are becoming deeply embedded in IDEs, offering context-aware suggestions across the code lifecycle. These will reshape DevOps, product velocity, and QA workflows.
This signals to CPOs and engineering leads that AI is becoming part of the team, not just a tool.
3. AI for HR: Leave Approvals to Onboarding
One standout use of Agentspace showed how AI agents can fully power HR workflows that connect across Workday, Jira, and Workspace.
For HR heads, policy support, compliance checks, onboarding, and ticket resolution can now be automated with empathy and speed.
4. Google Cloud Observability for AI Systems
Production AI needs monitoring. A demo using Open Telemetry and platform-native metrics showed how to trace model behavior and system performance—a must-have for any team putting LLMs into production.
5. Smarter Concept Extraction
Rather than one extractor for all documents, a routing engine distributes tasks to the best-fit model (Gemini, DocAI, Azure, etc.).
This “divide and conquer” logic aligns with how we build composite AI workflows—picking the right intelligence for the job, not just the default one.
6. GraphRAG: RAG Evolved
By combining Retrieval-Augmented Generation with a knowledge graph, accuracy jumped from ~75% to 95%+.
For legal, financial services, and regulated industries, this level of reliability is non-negotiable. Our team is exploring similar integrations to boost knowledge intelligence in go-to-market workflows.
A Glimpse of What’s Next
The event didn’t just showcase what’s available—it gave us a view of what’s inevitable. For buyers, sellers, developers, and business leaders, AI Agents will soon be teammates, not tools.
At Relanto, that’s exactly the future we’re building towards.
Every product demo validated a choice we’ve already made—from our modular agent architecture to our focus on secure AI for revenue operations.
