v0.3.0

• prompt_task decorator — new first-class task type alongside function_task and executable_task. The decorated async function returns a prompt string that is forwarded to an AI execution backend (e.g. DragonVllmInferenceBackend from RHAPSODY). NoopExecutionBackend returns "Dummy Prompt Output" for testing; LocalExecutionBackend raises NotImplementedError with a clear message directing users to register an AI backend.
• Multi-backend registry — WorkflowEngine.create(backend=...) now accepts either a single backend or a list of pre-initialized named backends (each must expose a .name attribute). The first backend in the list is the default. Built-in backends (LocalExecutionBackend, NoopExecutionBackend) gain a name parameter defaulting to "default".
• Per-task backend routing — task decorators (function_task, executable_task, prompt_task) accept an optional backend="<name>" parameter to route a specific task to a named backend. Tasks without backend= are sent to the default backend.
• Parallel backend submission — when a batch contains tasks routed to multiple backends, submit() dispatches all backend calls concurrently via asyncio.gather. shutdown() similarly drains all registered backends in parallel.
• Example 04-concurrent_backends.py — demonstrates a compute + AI dual-backend workflow running document processing pipelines concurrently.
• Tests — unit tests (tests/unit/test_prompt_task.py) and integration tests (tests/integration/test_multi_backend.py) covering prompt_task registration, validation, multi-backend routing, parallel submission, and graceful shutdown.