kaizen

You can orchestrate multiple AI agents using sequential, parallel, or hierarchical execution strategies. The framework provides SupervisorAgent and OrchestrationRuntime components to manage complex multi-agent coordination and structured task execution.

AI agent memory management supports session, shared, and persistent memory implementations. This enables agents to maintain context across interactions, share state in multi-agent environments, and retain historical data for continuous reasoning.

Yes, you can integrate custom external tools with an AI agent. The framework allows defining and registering tools, enabling agents to interact with external systems and execute specialized functions during their orchestration workflows.

The best way to monitor multi-agent system performance is through built-in observability features. The framework provides metrics, tracing, and logging capabilities to track agent execution, identify bottlenecks, and analyze system behavior.

Python bindings for Rust-backed AI agent frameworks improve scalability by leveraging Rust's performance for core execution while maintaining Python's flexibility. This architecture supports structured, maintainable, and scalable multi-agent development.

Hierarchical orchestration for AI agents should be used when tasks require delegated authority and structured supervision. A SupervisorAgent manages sub-agents, making it ideal for complex workflows like customer support routing or multi-step research.