LangChain 不看好 OpenAI AgentKit：世界不需要再来一个 Workflow 构建器

Core Viewpoint - OpenAI's AgentKit is a comprehensive toolset for developers and enterprises, but it is critiqued for being a visual workflow builder rather than a true agent builder, lacking the necessary autonomy and predictability for complex tasks [2][3][10]. Group 1: Purpose and Functionality - The primary goal of low-code workflow builders is to enable non-technical users to create agents independently, reducing reliance on engineering teams [7]. - Visual workflow builders, including OpenAI's AgentKit, are fundamentally workflow builders and not true agents, which limits their effectiveness in handling complex tasks [10]. Group 2: Differences Between Workflows and Agents - Workflows are characterized by fixed processes with complex branching logic, while agents operate with simplified logic abstracted into natural language, allowing for more autonomous decision-making [8][9]. - The trade-off between predictability and autonomy is crucial; workflows sacrifice autonomy for predictability, whereas agents do the opposite [8]. Group 3: Challenges of Visual Workflow Builders - Visual workflow builders face challenges due to limited engineering resources in many companies, making it difficult to meet all technical demands [12]. - Non-technical users often have a clearer understanding of the agents they need, which complicates the development of effective visual workflow tools [12]. Group 4: Solutions for Different Complexity Levels - For high-complexity scenarios, a code-based workflow is necessary to ensure reliability, as these situations often require intricate workflows with multiple branches and parallel processing [14]. - In low-complexity scenarios, simple agents (Prompt + tools) can reliably address issues, and building these agents without code is simpler than creating workflows [16]. Group 5: Future Directions - The industry does not need more workflow builders; instead, the focus should be on enabling users to easily create stable and reliable agents without code [22]. - Optimizing code generation models to better assist in writing LLM-driven workflows and agents is a key area for future development [23].