Runtime discovery with AWS Agent Registry can help address a problem that agent demos might hide. Hardcoded tools work fine for a handful, start to degrade at a few dozen, and become unmanageable beyond that. Enterprise systems expose hundreds of internal APIs for pricing, support, orders, inventory, and customer data. In these systems, the model can spend more tokens disambiguating tool names than reasoning about the user’s request. Each new capability requires a code change in the orchestrator, a redeploy, and a coordinated release across teams that would much rather ship on their own cadence.

This pattern can arise across enterprise software as a service (SaaS) engagements where teams expose large internal API surfaces to agentic consumers. Build-time wiring can become a bottleneck as the catalog expands. One approach is to change how the orchestrator accesses its tools: instead of compiling every tool into the agent, the agent can discover relevant tools at runtime.

AWS Agent Registry is a managed service for cataloging and discovering Model Context Protocol (MCP) servers, agents, and agent skills. Publishers register protocol-specific descriptors and searchable descriptions. At runtime, an orchestrator can search approved, discoverable records and use the returned endpoint and descriptor content to construct a client. Registry search results do not return the capability’s invocation credentials or authentication scheme.

This post walks through the Discover → Instantiate → Execute loop demonstrated by the refactored Python sample. The code deploys an order-management MCP server through Amazon Bedrock AgentCore Gateway , a capability of Amazon Bedrock AgentCore, two agent-to-agent (A2A) agents on AgentCore runtime , a Registry, and an orchestrator.

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