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Gemini Enterprise Agent Platform

Overview

Gemini Enterprise Agent Platform is Google Cloud's comprehensive platform for building, scaling, governing, and optimizing AI agents in production. It is the evolution of Vertex AI — consolidating model selection, model building, and agent building capabilities from Vertex AI with new features for agent integration, DevOps, orchestration, and security. Google announced and made the platform generally available on April 22, 2026, from the stage of Google Cloud Next '26 in Las Vegas — the rename and consolidation event, not merely a 2025 preview. (Updated: earlier coverage on this page dated the announcement to 2025; that referred to preview-stage Vertex AI Agent Builder capabilities. The Gemini Enterprise Agent Platform brand, GA status, and full feature set launched at Cloud Next '26.) All Vertex AI services and roadmap evolutions are now delivered exclusively through Agent Platform, and as of May 21, 2026, Vertex AI no longer exists as a standalone product name.

The same Cloud Next '26 announcement also absorbed Agentspace into a unified Gemini Enterprise product — ending the prior three-product split of Vertex AI, Agentspace, and the Gemini API. Two related but distinct names now exist: Gemini Enterprise is the end-user app (licensed per user/month) that employees open to search company data, chat with an assistant, and run agents someone else built; Gemini Enterprise Agent Platform is the underlying developer platform covered on this page, accessible at console.cloud.google.com/agent-platform/overview. The platform provides a single destination for technical teams to build agents that can transform products, services, and operations, with delivery to employees through the Gemini Enterprise app and tight integration with IT operations for control, governance, and security at scale.

Key Components

Gemini Enterprise Agent Platform Source: Google Cloud - Gemini Enterprise Agent Platform

Build

Component Description
Agent Studio Low-code, visual interface for building and deploying agents; supports export to ADK for full-code customization
Agent Development Kit (ADK) Code-first framework for building single and multi-agent systems; processes over 6 trillion tokens monthly on Gemini models; now supports graph-based sub-agent networks
Agent Garden Curated library of pre-built agent templates (code modernization, financial analysis, invoice processing, etc.)
Model Garden Access to 200+ models including Gemini 3.1 Pro, Gemini 3.1 Flash Image, Lyria 3, Gemma 4, and third-party models (Anthropic Claude Opus/Sonnet/Haiku)
Workspaces Hardened, sandboxed environments for agents to run bash commands and manage files in isolation
Multimodal Streaming Real-time audio and video support for human-like interactions

Scale

Component Description
Agent Runtime Re-engineered runtime with sub-second cold starts; supports long-running agents that maintain state for days
Memory Bank Persistent, long-term context storage; Memory Profiles enable high-accuracy recall with low latency
Agent Sessions Session history management with Custom Session IDs mappable to internal databases and CRM records
Agent Sandbox Hardened environment for safely executing model-generated code and browser-based automation
Bidirectional Streaming WebSocket-based protocol for real-time, low-latency audio and video agent interactions
Batch & Event-driven Agents Asynchronous task execution integrated with BigQuery and Pub/Sub

Govern

Component Description
Agent Identity Assigns every agent a unique cryptographic ID, creating an auditable trail mapped to authorization policies
Agent Registry Central library of approved internal agents, tools, and skills; single source of truth for governed assets
Agent Gateway Unified connectivity layer acting as air traffic control across agent ecosystems; enforces security policies and Model Armor protections against prompt injection and data leakage
Agent Anomaly Detection Statistical models and LLM-as-a-judge framework to flag unusual agent reasoning in real time
Agent Threat Detection Visibility into malicious activity such as reverse shells or connections to known bad IP addresses
Agent Security Dashboard Powered by Security Command Center; maps agent-model relationships, automates asset discovery, and scans for OS and package vulnerabilities

Optimize

Component Description
Agent Simulation Tests agents against synthetic user interactions and virtualized tools; auto-scores on task success and safety
Agent Evaluation Continuously scores agents against live traffic using multi-turn autoraters that evaluate full conversation logic
Agent Observability Visual tracing of complex agent reasoning for real-time debugging
Agent Optimizer Automatically clusters real-world failures and suggests refined system instructions to improve accuracy

Architecture

Agent Platform follows a layered architecture:

  • Agent Runtime Layer — execution engine, tool integration, state management, and memory systems
  • Platform Services Layer — model services (Model Garden), data services (BigQuery/Pub/Sub), security services (Agent Identity/Gateway), and monitoring (Agent Observability)
  • Integration Layer — Native Ecosystem Integrations for plug-and-play connectivity to internal data and tools; support for MCP and A2A protocols; Agent Payment Protocol (AP2) for trusted agent payments

The ADK now supports a graph-based framework for organizing agents into networks of sub-agents, enabling clear, reliable logic for complex multi-agent coordination with both generative and deterministic orchestration patterns.

Use Cases

  • Customer service: Multi-agent architectures for personalized troubleshooting and self-service (e.g., Comcast Xfinity Assistant)
  • Healthcare: End-to-end care delivery with real-time eligibility and scheduling (e.g., Color Health Virtual Cancer Clinic)
  • Financial services: Autonomous expense management with long-term memory (e.g., Payhawk Financial Controller Agent)
  • Enterprise knowledge: Turning decades of project data into real-time actionable intelligence (e.g., Burns & McDonnell)
  • Commerce: Trusted agent-based payment flows (e.g., PayPal AP2 integration)

Relationship to Vertex AI and Agentspace

Gemini Enterprise Agent Platform is the direct successor to Vertex AI, and its sibling product Gemini Enterprise absorbed Agentspace. Key transition points:

  • Timeline: Announced and GA April 22, 2026, at Google Cloud Next '26; Vertex AI ceased to exist as a standalone product name by May 21, 2026.
  • All Vertex AI services are now delivered through Agent Platform — Vertex AI no longer exists as a standalone service.
  • Agentspace consolidation: Agentspace's employee-facing search/assistant/agent capabilities were folded into Gemini Enterprise (the app), ending the prior Vertex AI + Agentspace + Gemini API three-product split.
  • Existing Vertex AI capabilities (model building, agent building, model selection) are preserved and extended.
  • The ADK, previously associated with Vertex AI, is now a first-class component of Agent Platform.
  • Documentation and console access have migrated to the Agent Platform destination (console.cloud.google.com/agent-platform/overview) in Google Cloud Console.
  • No forced migration: Existing Vertex AI / Agentspace customers do not need to take migration action — the underlying services are unchanged, only the branding and console destination moved.

Best Practices

Challenge / Area Description Solution / Recommendation
Agent governance at scale Managing identity and access across many agents Use Agent Identity (cryptographic IDs) + Agent Registry as single source of truth
Long-running workflows Agents that need to persist state across days Deploy via Agent Runtime with Memory Bank and Memory Profiles
Security hardening Preventing prompt injection and data leakage Route all agent traffic through Agent Gateway with Model Armor enabled
Production quality Catching failures before and after deployment Combine Agent Simulation (pre-ship) with Agent Evaluation + Agent Optimizer (post-ship)
Multi-agent coordination Reliable orchestration of sub-agent networks Use ADK's graph-based framework; apply deterministic paths for compliance-critical flows
Cost and model flexibility Avoiding lock-in to a single model Leverage Model Garden's 200+ models including third-party options

See Also

References