I am designing an advanced multi-agent AI system using Gemini on Google Cloud and would like guidance on best practices for architecture and deployment.
The system consists of multiple specialized agents such as:
- Planner Agent for task decomposition
- Executor Agent for tool usage and action execution
- Critic Agent for validation and self-reflection
- Memory component for short-term and long-term context
The agents collaborate to solve complex tasks autonomously using Gemini’s reasoning capabilities.
I am particularly looking for insights on:
1. Recommended multi-agent architecture patterns with Gemini
2. Orchestration and communication between agents
3. Tool calling and external API integration
4. Memory management and state persistence
5. Deployment strategies using Vertex AI / Agent Engine
6. Monitoring, reliability, and cost optimization in production
Any examples, documentation references, or real-world best practices would be greatly appreciated.