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
Top comments (1)
Curious, if you had this workflow today, what real-world AI agent would you build first?
Customer support, sales automation, research assistants, internal ops, or something bigger?
The tooling is moving fast enough that execution may soon matter more than implementation complexity.
Would love to hear what others are building.