prose

You orchestrate multiple AI agents for workflow automation by defining and executing structured programs using the OpenProse language, which supports agent configuration, session management, parallel execution, and error handling. This turns AI sessions into programmable computers.

Multi-agent coordination is the process of configuring and managing several AI agents to execute complex tasks together. You need it when automating repeatable processes like code review, research synthesis, or content generation that require specialized agent roles.

To start building a multi-agent workflow with OpenProse, run the example program 'examples/01-hello-world.prose' provided by the skill. This quick start approach demonstrates the basic structure for defining agents and executing declarative programs.

Yes, you can use loops and parallel execution for complex task automation. The OpenProse language natively supports parallel execution, loops, error handling, and program composition via imports to facilitate intricate agent coordination and workflow logic.

Yes, agent orchestration works for multi-step processes like code review. You can define one agent to identify bugs, another to check style compliance, and a third to synthesize feedback into a single report, automating the entire workflow repeatable.

The declarative programming approach for agent coordination requires structuring your logic within the OpenProse language syntax. While it supports imports for program composition, highly dynamic or unstructured tasks may not fit well within its structured execution and session management constraints.