Google Cloud Next 2026: AI Innovations for Business

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Key Takeaways

1Google Cloud unveils the Cross-Cloud Lakehouse, facilitating access to multi-cloud data without transfer.

2Google's new deep research agents analyze and synthesize enterprise data via the Gemini API.

3A red team agent uses AI to defend information systems against rapid cyberattacks.

4Gemini Embedding 2 enables multimodal vectorization, enhancing data querying by LLMs.

💡Why it matters — These innovations strengthen the efficiency and security of businesses in the face of growing digital challenges.

Google Cloud Next 2026: AI Innovations Transforming Business

At the 2026 edition of Google Cloud Next in Las Vegas, several announcements made a significant impact. While the Gemini Enterprise Agent Platform was at the center of discussions, other less-publicized innovations could have a substantial effect on companies' AI projects. These new developments span various fields such as cloud, cybersecurity, and data management. Here are four announcements that could transform the daily operations of adopting businesses.

1. Multi-Cloud AI

Generative AI requires extensive access to data to be truly effective. In 2026, companies, especially large ones, store their data across various cloud providers. This complicates the use of agent-based applications that need to access dispersed data. Google has introduced the Cross-Cloud Lakehouse, an infrastructure designed for agent-based applications, allowing AI agents to retrieve data wherever it resides, whether on AWS, Azure, or Google Cloud, without moving it. Karthik Narain, Google Cloud's product director, emphasized on stage that this eliminates the need for data transfer and reliance on providers, thus offering unprecedented freedom to businesses.

2. Deep Research Agents for Enterprise Data

Among the significant announcements, Google unveiled new versions of its autonomous search agents: deep research and deep research Max. Available through the Gemini API, these agents can perform web searches, query BigQuery, and synthesize internal documents to provide comprehensive and sourced analysis with a simple API call. Unlike traditional approaches, these agents do not merely process raw data. They reason within an enriched business context, integrating mapped entities, relationships between sources, and verifiable citations. These tools, integrated into Gemini Enterprise, adhere to the company's governance and access policies.

3. An Agentic Red Team

The speed of cyberattacks has significantly increased due to AI. Francis deSouza, Google Cloud's Chief Operating Officer, warned that the time between an initial intrusion and the relay to a second group of attackers has dropped from eight hours to 22 seconds in three years. To counter this threat, Google has developed an agentic team composed of three specialized agents:

a Red Agent for continuously testing information systems in an offensive manner,
a Blue Agent for defensive monitoring,
a Green Agent that manages complete remediation, identifying the problematic line of code, generating a fix, and sending it directly as a pull request to the developer, without human intervention.

Together, these agents form an autonomous defense loop.

4. Multimodal RAG Becomes Reality

Finally, Google announced the general availability of Gemini Embedding 2. While foundational models like Gemini 3.1 attract attention, embedding models play a crucial role. They enable the vectorization of data to make it queryable by an LLM. Until now, most embedding models only worked on a single modality at a time. With Gemini Embedding 2, Google allows the vectorization of various types of content (audio, video, text…) within the same model. This means that a RAG can now be natively multimodal. By projecting all modalities into a common vector space, the coherence and accuracy of AI responses are significantly improved.