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Workflow Patterns in Dana

(This document is a placeholder. It will explore common workflow patterns and discuss how they can be implemented using Dana's language features and orchestration capabilities. This provides guidance for structuring more complex agent behaviors.)

Common Workflow Patterns to Discuss:

  • Sequential Pipeline:
  • Description: A series of tasks executed one after another, where the output of one task becomes the input to the next.
  • Dana Implementation: Simple sequence of function calls, with variable assignments passing data.

  • Example: Document processing (load -> extract text -> summarize -> save summary).

  • Fan-Out / Fan-In (Map-Reduce Style):

  • Description: A task is parallelized by distributing data to multiple instances of a worker task (fan-out), and then results are aggregated (fan-in).
  • Dana Implementation: Requires concurrency features. A loop to dispatch tasks, mechanisms to collect and aggregate results (e.g., await all([...]) if applicable).

  • Example: Processing multiple documents in parallel and then combining their summaries.

  • State Machine:

  • Description: A workflow that transitions between a finite number of states based on events or conditions. Each state can have entry actions, exit actions, and transitions.
  • Dana Implementation: Using Dana variables to hold the current state, if/else or a switch-like structure (if Dana has one, or emulated with if/elif/else) to handle state-specific logic and transitions. Dana functions can represent state-specific behaviors.

  • Example: A user interaction flow (e.g., greeting -> awaiting input -> processing input -> providing response -> awaiting next input).

  • Publish-Subscribe (Event-Driven):

  • Description: Components (tasks or agents) publish events without knowing who the subscribers are, and subscribers react to events they are interested in.
  • Dana Implementation: Would likely require a dedicated event bus/manager resource, or be built on top of Dana's concurrency primitives if they support message passing or event listeners.

  • Example: An agent reacting to changes in a monitored data source.

  • Retry and Fallback:

  • Description: If a task fails, retry it a certain number of times, potentially with backoff. If still failing, execute a fallback task or error handling logic.
  • Dana Implementation: try/catch blocks combined with loops for retries. Conditional logic for fallback paths.

  • Example: Calling an external API that might be temporarily unavailable.

  • Long-Running Workflows / Saga Pattern (More Advanced):

  • Description: Workflows that may span a long duration and involve multiple transactions, requiring compensation logic if a step fails.
  • Dana Implementation: Would require persistent state management beyond a single SandboxContext session (e.g., saving state to a database via a resource) and careful design of compensating Dana functions.

  • Example: An order processing system involving inventory checks, payment, and shipping, each being a potentially fallible step.

  • Router / Dispatcher:

  • Description: A task that routes input to one of several other tasks based on some criteria.
  • Dana Implementation: if/elif/else structures or a function that takes input and returns the name of the next function/task to call.
  • Example: An LLM classifying user intent and Dana routing to the appropriate intent handler function.

For each pattern:

  • Provide a clear description.
  • Show a conceptual Dana code snippet or describe the structure.
  • Discuss advantages, disadvantages, and use cases.

Self-reflection: Understanding these patterns will help Dana developers build more robust and maintainable agent systems. The document should focus on practical implementation using Dana's features.