Yes, chaining multiple skills together is a fundamental and common pattern in building sophisticated AI systems, particularly conversational agents, intelligent assistants, and complex automation workflows. This approach allows a system to break down intricate problems or user requests into smaller, manageable sub-tasks, each handled by a specialized skill. The output from one skill can then serve as the input for the subsequent skill, creating a sequential flow that addresses a broader objective. This modularity not only makes the system more robust and easier to maintain but also enhances its ability to perform complex actions that no single skill could accomplish on its own. For instance, a system might use a "search knowledge base" skill to retrieve relevant information, then a "summarization skill" to distill that information, and finally a "natural language generation skill" to present a coherent answer to the user.
Implementing skill chaining typically involves an orchestration layer or an agentic framework responsible for deciding which skills to invoke, in what order, and how to manage the flow of data between them. This orchestration can range from simple, predefined sequences to dynamic, AI-driven decision-making. Techniques often include explicit workflow definitions where developers pre-configure the order of skills, or more advanced approaches using Large Language Models (LLMs) that leverage "function calling" capabilities. In the latter, the LLM analyzes the user's intent and available tools (skills) ， then generates a call to one or more skills, processes their outputs, and potentially calls further skills until the task is complete. Each skill in this context would be an API endpoint, a function, or a discrete microservice with clear input and output specifications, making it reusable and composable. This enables the construction of highly adaptable systems that can respond to a wider range of user requests by intelligently combining their capabilities.
Vector databases can play a critical role in enabling dynamic and intelligent skill chaining, especially when dealing with a large and evolving set of skills. When an orchestrator or an AI agent needs to select the most appropriate skill or sequence of skills for a given task, it can embed the user's query or the current context into a vector. This vector can then be used to perform a similarity search against a collection of skill embeddings stored in a vector database, such as Zilliz Cloud . Each skill's embedding would represent its capability, purpose, and relevant keywords. By retrieving the most semantically similar skills, the system can dynamically identify potential candidates for execution, even for novel or ambiguous requests. For example, if a user asks "Book me a flight to London next month and tell me about the weather there," the system could query the vector database with this request, retrieve skills related to "flight booking" and "weather forecasting," and then chain them together, using the output of the "flight booking" skill (e.g., flight details) as context for further actions or information gathering. This method greatly enhances the system's ability to discover and utilize relevant skills without requiring explicit, pre-programmed rules for every possible combination.