Google Vertex AI Agent Builder
by Google Cloud
Managed platform for building, deploying, and scaling multi-agent systems with built-in MCP support and enterprise infrastructure
See https://cloud.google.com/vertex-ai/generative-ai/docs/agents/agent-builder
Core Problem Solved
Building production multi-agent systems requires:
- Agent development framework
- Connections to enterprise systems
- Orchestration of multiple agents
- Secure deployment infrastructure
- Memory and context management
- Security and governance
Vertex AI Agent Builder bundles all of this as a managed service, eliminating infrastructure complexity.
Architecture: Three-Layer Stack
Layer 1: Agent Development Kit (ADK)
Flexible agent building across frameworks:
- Build with Google ADK
- Use LangChain, LangGraph, or other open-source frameworks
- Import agents from other tools
- Platform-agnostic development
Agents are built to solve specific tasks and can be orchestrated together.
Layer 2: Model Context Protocol (MCP)
Standardized tool integration via Model Context Protocol:
- MCP connects agents to external systems, APIs, data sources
- Over 100 pre-built connectors (ERP, HR, CRM, procurement, etc.)
- Custom RAG engines (local files, Google Drive, Slack, Jira, etc.)
- Apigee integration for existing API investments
- Unified communication layer across all integrations
Instead of building custom integrations per tool, MCP standardizes how agents connect to everything.
Layer 3: Agent Engine (Managed Runtime)
Production deployment without DevOps overhead:
- Automatic infrastructure provisioning
- Security management
- Container orchestration
- Scaling and monitoring
- Deployment targets: Cloud Run, Kubernetes, local, on-premise
- Custom build-time scripts and security configuration
Key Capabilities
Enterprise Connectivity
100+ Pre-built Connectors:
- ERP systems (SAP, Oracle)
- CRM platforms (Salesforce, Microsoft Dynamics)
- HR systems (Workday, SuccessFactors)
- Procurement (Coupa, Ariba)
- Business applications (Jira, ServiceNow)
- No custom integration work required
Custom RAG Engines:
- Local file systems
- Google Cloud Storage
- Google Drive
- Slack
- Jira
- Any enterprise data source
API Management:
- Reuse existing APIs managed in Apigee
- Secure API governance
- No need to rebuild integrations
Data Grounding & Knowledge Integration
Multiple grounding options:
- Google Search (external grounding)
- Google Maps data
- Vertex AI Search (ready-to-use RAG)
- Vector Search (hybrid search for precision)
- Custom enterprise data sources
Agents ground responses in organizational knowledge, not hallucinations.
Multi-Agent Orchestration
Agent-to-Agent (A2A) Protocol:
- Agents communicate with each other
- Prevents vendor lock-in
- Specialized agents for specific domains
- Dynamic collaboration on complex tasks
Workflow:
- User request comes to primary agent
- Agent routes to specialized agents as needed
- Agents coordinate via A2A protocol
- Results aggregate and return to user
Benefit: Reduce development time from weeks to days by composing specialized agents.
Memory and Context Management
Built-in memory services:
- Sessions – Short-term context across interactions
- Memory Bank – Long-term memory from previous conversations
- Maintains agent state across conversations
- Human-like continuity
Agents remember previous interactions and maintain coherent conversations.
Security and Governance
Identity and Access Management (Preview):
- Agent identity via Google Cloud IAM
- Centralized governance
- Agent permissions and access control
- Full audit trails
Safety Controls:
- Safety filters
- Action monitoring
- Complete tracing of agent decisions
- Compliance-ready audit logs
Knowledge Management
- Structured and unstructured data support
- Semantic understanding
- Precision through hybrid search
- Integration with organizational knowledge
Deployment Model
Flexibility in Targets
| Target | Use Case | Characteristics |
|---|---|---|
| Cloud Run | Serverless, auto-scaling | Managed, cost-effective |
| Kubernetes | Enterprise, control | Full control, complexity |
| Local | Development | Fast iteration |
| On-premise | Privacy, compliance | Full control, infrastructure |
Path from Development to Production
- Local debugging – Build and test locally
- Containerized deployments – Package agent with custom build scripts
- Security configuration – IAM and VPC controls
- Production scaling – Automatic infrastructure management
Clear and reliable path with reduced operational overhead.
Competitive Positioning
vs. LeanMCP
- Vertex AI: Managed infrastructure, 100+ connectors, Google ecosystem
- LeanMCP: Faster deployment (minutes), more lightweight, vendor-agnostic
vs. AWS/Azure
- Vertex AI: Google Cloud-native, tight integration with Google services
- AWS: SageMaker, existing AWS investments
- Azure: Copilot integration, Microsoft ecosystem
vs. Open-Source Frameworks
- Vertex AI: Managed service (no DevOps)
- Open-source: Full control, more complexity
Enterprise Use Cases
- Multi-agent customer support – Different agents for billing, technical, sales
- Business process automation – Cross-system workflows with multiple agents
- Decision automation – Specialized agents for different decision types
- Knowledge work acceleration – Agents augment teams with data access
Technical Specifications
Supported AI Models:
- Google Gemini family
- Custom models
- External model integrations
Monitoring and Observability:
- Full tracing
- Error tracking
- Performance metrics
- Agent behavior analysis
Integration Standards:
- Model Context Protocol – Standard for tool integration
- REST APIs – Standard API communication
- OpenAPI – API definitions
Future Roadmap
Coming soon:
- Computer-use capabilities (agents executing code)
- Simulation environment (testing with diverse personas)
- Enhanced memory management
- Additional pre-built connectors
Strategic Considerations
Advantages
- Managed infrastructure (no DevOps needed)
- 100+ pre-built connectors (faster integration)
- Multi-agent orchestration built-in
- Google ecosystem integration
- Enterprise security and governance
Limitations
- Google Cloud lock-in (vendor specific)
- Complexity of multi-agent systems
- Pricing (managed services premium)
- Learning curve for multi-agent patterns
Market Position
Vertex AI Agent Builder positions Google Cloud as the managed agent platform alternative to:
- LeanMCP (lightweight, faster deployment)
- AWS (SageMaker, Bedrock agents)
- Azure (Copilot, Logic Apps)
- Self-hosted (open-source frameworks)
The thesis: Enterprise organizations want managed infrastructure + multi-agent orchestration + 100+ connectors, and are willing to accept Google Cloud lock-in for operational simplicity.
Getting Started
- Create Google Cloud project
- Enable Vertex AI Agent Builder API
- Choose development framework (ADK, LangChain, etc.)
- Connect via MCP and pre-built connectors
- Deploy to Cloud Run or Kubernetes
- Monitor via Vertex AI console
Resources
- Official Documentation
- Agent Development Kit
- MCP Integration Guide
- Google Cloud Console (for deployment)
Related
- Model Context Protocol – Standard integration layer
- LeanMCP – Lightweight MCP deployment alternative
- Google Cloud Vertex AI – Parent platform
- Zapier MCP – Integration alternative (pre-built connectors)
- OpenAI Frontier – Competing enterprise agent platform (OpenAI)