A company's AI agent is frequently failing to retrieve relevant answers from the Data Library. What troubleshooting steps should they take?

In the context of an AI agent struggling to pull relevant answers from a Data Library, it’s critical to ensure that the proper retriever is configured and linked effectively. The retriever serves as the mechanism through which the AI agent accesses and retrieves information. If the retriever is not set up correctly or not connected to the Data Library, the AI will not have the necessary pathways to access the required data, leading to retrieval failures.

Configuring the correct retriever involves checking that it is designed to interface with the specific types of queries the AI is expected to handle and verifying that it can correctly access the necessary resources within the Data Library. This step is fundamental to enhancing the AI's performance and ensuring it operates effectively in providing accurate information.

The other strategies mentioned would not address the core issue of retrieval failure. Removing all retrievers would worsen the situation by eliminating structured pathways to information, and disabling AI assistance altogether would prevent the use of advanced features designed to help customers. Similarly, relying solely on manual interactions would not leverage the efficiency and scalability benefits that AI can provide when set up correctly. Thus, ensuring the retriever's proper configuration is the most viable and constructive troubleshooting step.