Getting started with Web Answer on Amazon Bedrock AgentCore

AWS Machine Learning Blog · 人工智能

Today, we are announcing the general availability of Web Answer on Amazon Bedrock AgentCore. With Web Answer, agent builders get a synthesized, source-cited answer to a natural-language question with a single tool call. They no longer build a search, retrieve, synthesize, and citation pipeline themselves. If you have built agents that need to answer questions from current web sources, you know the pattern: call a search API, decide which results to trust, retrieve and clean page content, prompt a model to synthesize, and engineer citation extraction so the output traces back to a source. Every step is code you maintain, and every step is another place things can go wrong: hallucination when evidence is insufficient, stale content when the index moves, and broken attribution when a claim and its source drift apart. The Web Answer collapses that entire pipeline into one request. You send a question. You get back a cited answer in seconds. In this post, we explain what Web Answer does, compare it with Web Search on AgentCore, show how it works and what it returns, and walk through adding Web Answer to a gateway and calling it from an agent. What Web Answer does Figure 1: Request flow from an agent through the Gateway to Web Answer and back Your agent sends a question to your AgentCore Gateway, a capability of Amazon Bedrock AgentCore. The Gateway authenticates the call with AWS Identity and Access Management (AWS IAM) or a JSON Web Token (JWT). The Gateway calls Web Answer, a managed connector that AWS owns, using its own service role. Web Answer plans its searches, runs several in parallel, and reads key pages to check the facts. When the evidence is insufficient, it searches again. It then writes an answer that ties each claim to its source URL. The Gateway returns the answer and its citations to your agent as a Model Context Protocol (MCP) tool result. Web Answer is a managed AgentCore Gateway tool that takes a natural-language question and returns a synthesized ans

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