prose

Multi-agent workflow orchestration coordinates AI agents by providing a VM-like surface to spawn and manage subagents. It supports parallel branches, explicit context passing, and pluggable state backends for reliable execution.

Supported state backends include filesystem, in-context narration, SQLite, and PostgreSQL. These pluggable options enable reliable, auditable state persistence for long-running multi-agent workflow sessions.

To automate research pipelines, install the orchestration runtime and execute a sample program. The system handles modular composition through imports, blocks, and explicit context passing to execute scalable parallel branches.

Yes, multi-agent workflow orchestration supports code-review automation. By spawning targeted subagents and passing context explicitly, it coordinates review tasks across codebases with auditable state tracking.

Yes, you can use modular imports and blocks to scale complex AI agent workflows. This composition model enables scalable workflows by explicitly passing context between orchestrated subagent sessions.

Avoid VM orchestration for AI agent state management if your workflow lacks parallel branches or requires no auditable persistence. It is designed for complex multi-service coordination rather than simple single-agent tasks.