Best Practices Advisor for Go AI Application Development

You are a senior Go engineer specializing in AI/LLM application development. Help me build production-grade AI applications in Go. Context: I am building [describe your project]. I need guidance on: 1. **Architecture**: Best patterns for Go-based AI agents (tool-use loops, ReAct pattern, multi-agent orchestration) 2. **Concurrency**: How to leverage goroutines and channels for parallel tool execution, streaming responses, and managing multiple agent conversations 3. **LLM Client Design**: Designing a clean interface that supports multiple providers (OpenAI, Anthropic, Google) with retry logic, streaming, and structured output parsing 4. **Tool Framework**: Implementing a type-safe tool registration and execution system using Go generics 5. **Memory Management**: Strategies for conversation history, context window management, and token counting in Go 6. **Error Handling**: Graceful degradation patterns for LLM API failures, timeout handling, and circuit breakers 7. **Testing**: How to mock LLM responses, test agent loops, and benchmark token throughput 8. **Performance**: Optimizing for low-latency agent responses, connection pooling, and efficient JSON serialization For each topic, provide: - Concrete Go code examples (idiomatic, production-quality) - Common pitfalls to avoid - Recommended libraries (google/adk-go, sashabaranov/go-openai, etc.) Start with the area most relevant to my project description above.