Key Takeaways From Google Cloud Next 2026 For Agentic AI Architects

With the rise of the Agentic era, Google Cloud Next continued the 2026 trend with significant announcements focused on Agentic AI and Universal Data & Security. This article highlights key areas from an Agentic AI architect's perspective:

# 1 — Unified Enterprise Agentic Platform for the agentic era

While Vertex AI was launched in 2021 (as a unified MLOps platform), it has evolved into a technology platform for building Generative AI and Agents. Google has now launched **Gemini Enterprise Agentic Platform* (rebranded Vertex AI and making a sub-component) with expanded & comprehensive offerings packaged within Build, Scale, Govern, and Optimize *buckets.

Source: Google Cloud

What does it mean for enterprise agentic architects?

Pros:

• Providing a design choice to use an end-to-end Agentic platform with out-of-the-box capabilities to launch Agentic Apps quickly
• Minimizing the involvement of engineering dependencies, particularly in Governance, Security, and AgentOps capabilities
• Focusing on building business-driven Agentic apps rather than technical capabilities, particularly when you are aligned with Google Cloud architecture.

Cons:

• Trade-offs in terms of technical features, cost, and control, and reliance on Google Cloud for features that are not yet available (particularly preview or beta features, dependencies need to be accounted for)
• Vendor dependency, if you want to build a truly vendor-neutral multi-cloud or hybrid agentic platform
• Lack of optionality when choosing from the best-of-the-breed solution (e.g., Agentic memory management alternatives)

# 2 — Agentic Workflows are the new reality

We recently executed a particularly complex code migration using a system of agents with three different types of roles: Planners, Orchestrators, and Coders — making migration 6X faster and now applying the entire development lifecycle. — Sundar Pichai

Google shared agentic automation from coding migration to the security operations (frontier threat intelligence) automation, and more. Google’s recent acquisition of Wiz accelerated the launch of security capabilities with a suite of security agents (Threat Hunting agent, Detection Engineering agent, and Third-Party Context agent. Click here to read more.

Wiz **Red, Blue, and Green Agents **are a suite of specialized, AI-powered systems designed to automate the entire security lifecycle — detection, investigation, and remediation — within the Wiz Cloud-Native Application Protection Platform.

Source: Google Cloud

These are a useful set of pre-built, customizable Agentic blueprints with source code, configuration files, and applied best practices as part of Agent Garden:

Source: Gemini Agent Platform

What does it mean for enterprise agentic architects?

Pros:

• End-to-end integration of SDLC with Agentic workflow from Google Agent Platform to the Antigravity and Gemini CLI
• Accelerate threat detection, threat analysis, threat response, and incident management
• Preview agents are yet to be released for production use-cases, and program planning needs to account for the release

Cons:

• Vendor dependency and planning for integration, particularly in the case of existing security tools and enterprise ecosystem

#3 — Agentic capabilities challenges Build vs. Buy Design Considerations

Gemini Enterprise Agent Platform’s capabilities challenges the enterprise’s design decisions to build their own building blocks for Agentic development such as:

• Agent Gateway
• All GCP Services as MCP Servers
• Agent Observability
• Agent-to-Agent Orchestration
• Zero-trust Verification and Agent Identity
• Gemini-native Agentic SOC
• Dark Web Intelligence
• Model Armor for Protection
• Developer Tools
• Projects in Gemini Enterprise
• Multi-agent Collaboration

Source: Google Cloud

What does it mean for enterprise agentic architects?

Enterprise Agentic Architects have a suite of design considerations in their toolkit with the new set of Gemini Enterprise Agent Platform capabilities. This opens up multiple Architecture/Design Decision Records (ADRs) as options for enterprise architects, such as (do we need to build or use out-of-the-box capabilities or use best-of-the-breed or other vendors)

• Design Option 1— Agent Gateway (Build Custom or Use Gemini Enterprise Gateway or similar offering)

Agent Gateway enables policy enforcement for all agent-to-agent and agent-to-tool connections at an enterprise level. It governs enterprise agent traffic and supports agent protocols like MCP and Agent2Agent (A2A) to inspect and secure every agent interaction. In addition, Agent AI Gateway has a broader perspective — click here to read more.

• Design Option 2 — Agent Memory (Build Custom or Use Gemini Platform or similar offering for episodic (events), semantic (facts), procedural (skills), and working memory (active reasoning) memory
• Design Option 3— Agent Identity, and Agent Registry (Build Custom or Use Gemini Platform or similar offering for Agent Governance)
• Design Option 4— Agent Simulation, Agent Evaluation, and Agent Observability (Build Custom or Use Gemini Platform or similar offering for Agent Observability and Testing)

#4 — Data and Context are the key mantras for Agentic Apps

With the growing need for production-ready Agentic Apps, Data Engineering has emerged as a key focus area for enterprises to prevent hallucinations. Google acquired Dataplex (a data catalog and governance solution) in 2021, and it has been elevated/rebranded as **Google Cloud Knowledge Catalog, which unifies catalogs from a variety of data sources, provides data enrichment capabilities, and provides universal search capabilities operating at a context layer as an enterprise context graph**:

Source: Google Cloud

What does it mean for enterprise agentic architects?

Pros:

• The launch of Cross-cloud Lakehouse as part of **Agentic Data Cloud helps to solve data unification across diverse data sources. The shift to a system of action** is a great way to empower Agentic apps with data.
• Applying the principle of bringing AI to the Data is evident, and the trusted context applicability is the architecture principle to adhere to. Automated Data Engineering (shift-left mindset) with zero-copy architecture elevates the possibility of leveraging Universal Data Layer.
• Universal context with Agentic Data Cloud is an emerging pattern across enterprises, and the demo elevated the applicability — if Agentic Apps have the semantic layer with universal context, that will drastically improve the quality of the output.

Cons:

• Vendor lock-in needs to be cautious about, along with dependency on a single platform
• Data ingress/egress cost across the cloud is an open question and needs to be analyzed in the context of an enterprise

To conclude, as anticipated, the Google Cloud Team has launched a series of Agentic capabilities accelerating enterprise adoption of Agentic Apps in the AI era. Looking forward to experimenting with the newly launched capabilities and continued implementation in the enterprise context!

Relevant Articles

• Top Ten Technology Trends for 2026
• A Reference Blueprint for an Agentic AI Assistant Built with Google ADK & Vertex AI
• Agentic AI Gateway: The Proven Architecture Pattern for Enterprise GenAI Security and Governance
• Unlocking Agentic Apps with the Model Context Protocol (MCP) For Financial Services [A Complete Guide]

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All data and information provided on this blog are for informational purposes only. The author makes no representations of the accuracy, completeness, correctness, suitability, or validity of any information on this blog and will not be liable for any errors, omissions, or delays in this information or any losses, injuries, or damages arising from its display or use. This is a personal view, and the opinions expressed here represent my own, not those of my employer or any other organization.