output-meta-project-context

Output.ai scaffolds Temporal-based AI workflow projects using a directory layout that separates config, src, workflows, prompts, scenarios, and tests to keep orchestration deterministic while steps handle I/O.

To keep AI workflows deterministic in Temporal pipelines, separate orchestration from I/O operations. Workflows must remain deterministic logic, while dedicated step components handle all external data fetching, API calls, and non-deterministic side effects.

You scaffold your first Output.ai workflow by referencing the Quick Start section and inspecting the example project layout in the src/ directory, which demonstrates the recommended structure for config, workflows, prompts, and tests.

Reusable components in an Output.ai framework project include agents, commands, prompts, evaluators, and tests. The framework provides governance patterns for code reuse to ensure reproducible evaluation loops and durable workflow execution.

Yes, Output.ai supports reproducible evaluation loops by providing structured scenarios and tests within its project layout. This enables teams to build durable AI workflows and validate data processing pipelines consistently.

You should avoid deterministic workflow frameworks like Output.ai when your AI application requires highly dynamic, non-reproducible ad-hoc execution paths that cannot be constrained into predefined steps, scenarios, and structured Temporal pipelines.