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You orchestrate multiple AI agents by writing structured programs using a declarative language. This approach manages agent lifecycles and complex control flows like parallel execution, loops, and error handling within a centralized execution framework.

Yes, you can persist state across sessions for long-running AI agent workflows. The system supports multiple state backends including filesystem, SQLite, and PostgreSQL to maintain execution state and agent memory.

A declarative language for AI workflow orchestration allows you to define multi-agent tasks as structured programs. You need it when automating complex pipelines that require specific models, prompts, and permissions for different agents.

Yes, this AI agent orchestration tool supports parallel execution and error handling. It manages complex control flows natively, allowing multiple agents to execute concurrently while maintaining robust error management.

You manage agent memory for persistent AI assistants by utilizing the built-in state management capabilities. This ensures context and execution state are maintained across sessions using supported backends like PostgreSQL or SQLite.

The best way to automate a multi-agent code review process is defining it as a structured program. One agent identifies issues, another suggests fixes, and a third synthesizes feedback, all managed by a central orchestrator.