Intelligent agents decompose tasks in complex processes through a combination of goal analysis, hierarchical planning, and dynamic adjustment. The core idea is to break down a high-level objective into smaller, manageable subtasks that can be executed sequentially or in parallel. Here's how it works:
Goal Understanding: The agent first analyzes the overall goal or problem statement to identify the desired outcome. For example, in an e-commerce order fulfillment process, the high-level goal might be "deliver a product to a customer."
Task Identification: The agent then identifies the key steps required to achieve the goal. Using the same example, subtasks could include "verify inventory," "process payment," "pack the item," and "ship the package."
Hierarchical Decomposition: Complex tasks are often decomposed hierarchically. The agent creates a task tree where the root is the main goal, branches represent major subtasks, and leaves are atomic actions. For instance, "process payment" might further break down into "validate payment method," "authorize transaction," and "confirm payment."
Dependency Mapping: The agent determines relationships between subtasks, such as prerequisites (e.g., "verify inventory" must precede "pack the item") or parallelizable tasks (e.g., "pack the item" and "generate shipping label" can run concurrently).
Dynamic Adjustment: Intelligent agents adapt decomposition based on real-time feedback. If an issue arises (e.g., low inventory), the agent may replan by inserting a "notify supplier" subtask or prioritizing alternative actions.
Example: In a smart factory, an agent managing production might decompose "assemble a device" into:
Relevant Cloud Service: For implementing such intelligent agents, Tencent Cloud TI Platform provides tools for building AI-driven task automation, including natural language processing for goal parsing and workflow orchestration services to manage subtask execution. Additionally, Tencent Cloud Serverless Functions can handle dynamic subtask scheduling at scale.