Google released something that could significantly accelerate how developers build AI agents:
Google Agents CLI
Combined with:
- Google ADK (Agent Development Kit)
- Claude Code (or Gemini CLI / OpenCode)
it creates one of the fastest workflows currently available for building, testing, evaluating, and deploying multi-agent systems.
In this project, I built a full Multi-agent Customer Support team in under 30 minutes.
What I Built
A production-style customer support team powered by four specialized AI agents:
🎧 Concierge Agent
- First point of contact
- User intent classification
- Request routing
📦 Logistician Agent
- Order status
- Shipping updates
- Inventory checks
🎭 Stylist Agent
- Product recommendations
- Catalog discovery
- Personalized suggestions
🛡️ Resolver Agent
- Returns
- Refunds
- Human escalation for high-value disputes
Full Video Walkthrough
Core Stack
Google ADK
Google’s Python-native Agent Development Kit that provides:
- Agent abstractions
- Tool integration
- Session handling
- Multi-agent architecture patterns
Google Agents CLI
A workflow layer that enables:
- Scaffold
- Build
- Validate
- Deploy
Claude Code
Your implementation accelerator:
- Writes code
- Generates tests
- Creates evals
- Performs security audits
- Assists deployment
Workflow
1. Scaffold the Foundation with Google Agents CLI
The process starts by using Google Agents CLI to rapidly initialize and scaffold the entire multi-agent project structure.
This includes:
- Base architecture
- Agent framework setup
- Development workflow
- Deployment pathways
Instead of manually creating boilerplate, the CLI provides a production-oriented foundation from day one.
2. Define the Multi-Agent System Through Natural Language
Next, Claude Code acts as the implementation engine.
By providing detailed system requirements in plain language, I specified:
- Individual agent roles
- Responsibilities for each specialist
- Agent-to-agent communication patterns
- Human-in-the-loop workflows
- Session memory requirements
- Mock data sources
- Deployment targets
This transforms high-level business logic directly into executable architecture.
3. Rapid End-to-End System Generation
From those instructions, Claude Code + Agents CLI collaboratively generated:
System Design:
- Full design specification
- Agent hierarchy
- Routing logic
- Communication workflows
Development Assets:
- Agent definitions
- Tool integrations
- Mock datasets
- Core application code
Quality Assurance:
- Unit tests
- Integration tests
- Evaluation suites
- Security audit recommendations
4. Deployment
The system successfully:
- Containerized the application
- Pushed to Artifact Registry
- Configured IAM
- Deployed to Google Cloud Run
- Created GitHub Actions CI/CD workflows
Which means every future code push can:
Test → Eval → Deploy automatically
This workflow creates a streamlined path from concept → validated production prototype in dramatically less time than traditional development workflows.
Key Takeaway
The hardest part is no longer building AI agents.
It’s deciding what to build.
That’s a massive shift.
As tooling matures, developer leverage increases dramatically.
Production Advice
If you’re planning to use this stack seriously:
Prioritize:
- Prompt injection defenses
- Adversarial evals
- Human oversight
- Security hardening
- Guardrails
- Monitoring
Fast building does NOT remove production responsibility.
Final Thoughts
Google Agents CLI + Claude Code feels like an early glimpse into the future of AI product development.
For:
- AI engineers
- Startup founders
- Automation builders
- Developer tool creators
This workflow could meaningfully compress idea-to-production timelines.
Full Code Repository
👉 https://github.com/vivekshetye/google-adk-multi-agent-customer-support