ProductsOfficialSelected

MoEngage turned 400 APIs into agent tools with AgentCore Gateway

How MoEngage turned over 400 APIs into agent tools with AgentCore Gateway

Why we picked it

The key insight is that a target-resolution layer plus Cedar policies can make a large API fleet usable by agents without flooding model context with 400 tool definitions. Engineering teams exposing internal APIs to LLM agents can replicate this pattern with AgentCore Gateway instead of hand-rolling per-service MCP servers. It is a concrete reference for enterprises on AWS managing multi-region private endpoints.

MoEngage converted roughly 400 private REST APIs spanning 7 AWS Regions into permission-scoped agent tools using Amazon Bedrock AgentCore Gateway. The setup uses OpenAPI specs to register tools, Cedar policies for permission scoping, and managed Amazon VPC Lattice egress for private connectivity. The approach avoids per-service MCP servers, public endpoints, and the 400-tool context bloat problem.

Image · AWS Machine Learning Blog
Original · AWS Machine Learning Blog

How MoEngage turned over 400 APIs into agent tools with AgentCore Gateway

MoEngage turned roughly 400 private REST APIs across 7 AWS Regions into permission-scoped agent tools with Amazon Bedrock AgentCore Gateway. No per-service MCP servers, no public endpoints, and no 400-tool context bloat. See how OpenAPI specs, Cedar policies, and managed Amazon VPC Lattice egress made it work.