AI agents are changing how organizations find and act on information, but they share one structural limitation: their knowledge is frozen at training time. When you ask an agent that relies only on its training data about today’s stock price, a sports score, or a release that shipped an hour ago, it can’t respond.

Web Search on Amazon Bedrock AgentCore, now generally available, addresses that gap. This fully managed, Model Context Protocol (MCP)-compatible web search capability lets your agents get information from the web without infrastructure overhead. It’s available as a managed target or connector that you connect to your AgentCore Gateway. Agents discover it with a standard tools/list call and invoke it like other MCP tools. There are no search APIs to provision, no outbound credentials to manage, and no result-parsing glue to maintain.

Behind that single connector sits a purpose-built web index maintained by Amazon, spanning tens of billions of documents. Amazon refreshes the index continually, reflecting new content within minutes. The privacy model makes sure that queries don’t leave AWS. Retrieval can combine a knowledge graph with semantic snippet extraction tuned for model context.

In this post, we walk through what makes Web Search on Amazon Bedrock AgentCore different, why it matters, and how to wire it in with a few lines of code.

Figure 1: Your application connects to the AgentCore Gateway (AWS Identity and Access Management (IAM) or JSON Web Token (JWT) inbound auth), which routes queries through a managed connector to the Web Search tool in the AWS service account. Query traffic stays within AWS.

Grounding agents in the web is the fix for stale knowledge, but it’s also where many teams get stuck. Building it yourself means: