kal-engine

AI workflow engines use a layered structure of Session, Flow, and State to manage interactive applications. Sessions maintain user context across interactions, while Flow files define the logic routing and State tracks variable data throughout the process.

Debugging AI flow logic in JSON files involves using specific validation commands to check structural integrity and testing workflows to trace logic execution paths. This ensures flow definitions correctly route interactions and state transitions operate as intended.

To set up an AI interactive application, configure an initial_state.json file with required game variables and define separate flow JSON files for logic routing. Use engine commands to validate the structure and test the interactive behavior.

AI flow engines fail to validate session transitions when flow JSON files contain logical errors or the initial_state.json lacks required variables. Running dedicated validation commands helps identify structural mismatches between state definitions and flow logic.

Yes, JSON flow definitions manage complex state in AI applications by structuring logic into readable files. The engine processes these definitions to handle state transitions and session continuity, enabling robust interactive application behavior.

The KAL engine supports testing and validation for AI-driven workflows through dedicated commands. Developers can validate flow JSON structures against initial_state.json configurations and run tests to ensure interactive logic performs correctly.