Why Agentic Workflows
AI models are powerful, but on their own they can be unpredictable—they may hallucinate, miss steps, or produce inconsistent outputs. In production environments—especially for teams and enterprises where reliability matters—you need more control over how AI operates. Agentic workflows solve this by embedding AI capabilities within a structured, repeatable process. Instead of relying on a single model to figure everything out, you design a flow that orchestrates models, tools, and logic step by step—with clear conditions, checkpoints, and fallback paths. The AI is still doing the heavy lifting, but within boundaries you define.Workflow vs. Chatflow
Dify offers two app types for building agentic workflows: Workflow and Chatflow. Both are built on a shared visual canvas and node system. To build a flow, connect nodes that each handle a specific step, such as calling a model, retrieving knowledge, running code, or branching on conditions. Most of the work is drag, connect, and configure—code is only needed when your logic calls for it. Their core difference is how users interact with the app:- A Workflow runs once from start to finish. It takes an input, processes it through the flow, and returns a result. Use it for tasks like automated report generation, data processing pipelines, or batch processing.
- A Chatflow adds a conversation layer. Users interact through a chat interface, and each message triggers the flow you designed before a response is generated. Use it for interactive assistants, guided Q&A, or any conversational scenario that requires structured processing behind each reply.