# Retrieval & Web-Search Augmentation When the nature of a task demands current knowledge, the agent has the capability to activate an optional web-retrieval module. This can involve accessing sources like the Bing API, SerpAPI, or an internal Retrieval-Augmented Generation (RAG) store. By engaging these resources, the agent retrieves the most recent and relevant snippets of information. These retrieved snippets are then seamlessly streamed back into the active prompt. This integration enriches the ongoing task by anchoring reasoning processes in a fresh and accurate context. This updated context supports subsequent stages of mutation or evaluation, which may be required during the task execution. The integration of real-time, relevant data ensures that all actions and decisions taken by the agent are not only informed by current trends and information but are also flexible to adapt as new data becomes available. This capability significantly enhances the efficacy and reliability of the task outcomes by providing a dynamic framework for decision-making, ultimately leading to more robust and contextually grounded results. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.darwinslab.ai/agent-architecture-and-evaluator-dynamics/retrieval-and-web-search-augmentation.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.