Google Vertex AI Agents is the managed agent-building capability within Google Cloud's Vertex AI platform, introduced and expanded through 2024–2025 as part of Google's broader push into enterprise AI. The service lets organizations build conversational AI agents and automated task pipelines using Google's Gemini family of models, with native access to Google Search grounding, Workspace data, and the full catalog of GCP services.
The platform has evolved from Dialogflow's enterprise conversational AI roots into a more capable agentic system — one where agents don't just respond to questions but can orchestrate multi-step tasks, retrieve information from enterprise data sources, and take actions through connected APIs.
Key Features#
Gemini Model Access Vertex AI Agents runs on Google's Gemini model family: Gemini 1.5 Pro, Gemini 1.5 Flash, Gemini 2.0 Flash, and Gemini 2.0 Pro. Gemini's industry-leading context window (up to 2 million tokens for 1.5 Pro) is a genuine differentiator for applications involving long documents, complex codebases, or multi-turn conversations requiring extended memory.
Google Search Grounding A unique capability in the Vertex AI ecosystem: agents can ground responses in real-time Google Search results. This means agents stay current with recent events, latest product information, and time-sensitive data without requiring a separate RAG pipeline. For applications where information freshness matters, this is a meaningful competitive advantage.
Vertex AI Search Integration For enterprise data grounding, Vertex AI Agents connects to Vertex AI Search (formerly Enterprise Search), which indexes and retrieves from documents in Cloud Storage, BigQuery tables, and website content. This managed RAG service handles embedding, indexing, and retrieval without requiring vector database management.
Extensions and Function Calling Vertex AI Agents supports tool use through Extensions (pre-built Google and third-party integrations) and custom function calling (define an OpenAPI spec, Vertex handles the orchestration). Extensions for Google Workspace (Gmail, Calendar, Drive), Google Maps, and common SaaS tools are available out of the box.
Agent Builder and Playbooks The Agent Builder console provides a no-code/low-code interface for configuring agent behavior, defining conversation flows (playbooks), and testing agents. This supplements the programmatic API approach, making the platform accessible to teams without deep Python expertise.
Multi-Modal Capabilities Gemini's multimodality flows into Vertex AI Agents. Agents can analyze uploaded images, process PDFs and documents, review code, and handle mixed text/image inputs within a single conversational context. This is valuable for use cases like document processing, visual customer support, and code assistance.
Pricing#
Vertex AI Agents pricing is based on model inference consumption:
- Gemini 1.5 Flash: ~$0.075/million input tokens, ~$0.30/million output tokens (cost-optimized)
- Gemini 1.5 Pro: ~$1.25/million input tokens, ~$5.00/million output tokens (for complex tasks)
- Gemini 2.0 Flash: Competitive pricing for the newest model; check cloud.google.com/vertex-ai/pricing for current rates
- Vertex AI Search: ~$2.50/1,000 queries for retrieval
- Google Search Grounding: Per-grounding-request fees apply; check current pricing
New Google Cloud accounts receive $300 in free credits applicable to Vertex AI usage.
Who It's For#
Google Vertex AI Agents is the right choice for:
- GCP-native organizations building AI applications within the Google Cloud ecosystem
- Google Workspace users who want agents that integrate with Gmail, Drive, Docs, and Calendar
- Applications requiring information freshness where Google Search grounding eliminates custom news/web retrieval pipelines
- Multimodal use cases that need agents to process images, documents, and code alongside text
- Teams evaluating Gemini's long-context capabilities for applications involving very large documents
It is less suitable for organizations on AWS or Azure as their primary cloud, for teams needing open-source model flexibility, or for applications where Anthropic's Claude or other non-Google models are required.
Strengths#
Google Search grounding is a genuine differentiator. The ability to ground agent responses in real-time web search without building a custom RAG pipeline saves significant engineering work for information-freshness-sensitive applications.
Gemini's context window. Two million tokens of context is practically unlimited for most business applications. Long-document analysis, cross-repository code review, and extended conversation history that would require complex chunking strategies in other systems are handled naturally.
Google Workspace integration depth. For Workspace-native organizations, native read/write access to Gmail, Calendar, Drive, and Docs through Extensions makes Workspace automation more natural than with other platforms.
Enterprise GCP compliance. Vertex AI inherits Google Cloud's compliance certifications (SOC 2, ISO 27001, HIPAA, PCI DSS) and VPC Service Controls for data exfiltration prevention — essential for regulated enterprise deployments.
Limitations#
GCP ecosystem dependency. Like AWS Bedrock, Vertex AI Agents creates strong coupling to Google's cloud infrastructure. Teams wanting cloud-portable applications need additional abstraction layers.
Gemini-only model access. Unlike AWS Bedrock's multi-vendor model catalog, Vertex AI Agents primarily runs on Gemini models. While Gemini is highly capable, teams wanting Anthropic Claude or open-source Llama models must use separate services or model access points.
Console complexity. Google Cloud's console is notoriously complex, and Vertex AI has many interconnected services (Vertex AI Search, Extensions, Agent Builder, Playbooks). New users face a steep navigation learning curve relative to AWS or more developer-friendly platforms.
Related Resources#
Explore the full AI Agent Tools Directory for cloud and managed agent platform options.
Related profiles: Amazon Bedrock Agents for the AWS-native alternative and IBM watsonx for a non-hyperscaler enterprise option.
Comparisons: Google Vertex AI Agents vs Amazon Bedrock Agents: Cloud AI Platform Comparison and Google Vertex AI vs Azure AI Foundry: Enterprise AI Comparison.
For implementation guides, see Building Grounded AI Agents with Vertex AI and Google Search and GCP AI Agent Architecture Patterns for Enterprise.