MoEngage Scaled 400 REST APIs into Agent Tools with AgentCore Gateway

Integrating large language models with legacy enterprise systems often feels like trying to fit a square peg into a round hole. When MoEngage set out to empower their autonomous AI agents with access to more than 400 private REST APIs scattered across 7 global AWS Regions, they faced a classic architectural dilemma. Building and maintaining 400 individual Model Context Protocol (MCP) servers or exposing public endpoints would have introduced massive maintenance overhead, severe security vulnerabilities, and catastrophic context window bloat. By leveraging Amazon Bedrock AgentCore Gateway, OpenAPI specifications, fine-grained Cedar policies, and managed Amazon VPC Lattice egress, MoEngage bypassed these hurdles entirely. In this review, we examine their architecture, analyze the engineering mechanics behind this massive enterprise migration, and explore how developers can achieve similar scalability using aggregator infrastructure like n1n.ai for multi-model orchestration.

The Engineering Challenge of Enterprise API-to-Agent Translation

Modern artificial intelligence agents require tools to interact with external systems. Whether executing a database query, fetching user session data, or triggering a marketing campaign, agents rely on structured tool definitions. However, exposing 400 distinct REST APIs directly to an LLM context window is a recipe for failure.

The Context Bloat Problem

If you paste 400 OpenAPI specifications directly into an LLM system prompt, you will easily exceed the token limits of most standard models, while drastically increasing latency and API costs. Even with advanced models like Claude 3.5 Sonnet or OpenAI o3, sending hundreds of unused schemas per request degrades reasoning performance and increases token consumption.

The Security and Isolation Hurdle

MoEngage operates across 7 distinct AWS Regions, processing petabytes of user data under strict compliance frameworks. Exposing private internal REST APIs to public endpoints simply to make them accessible to AI workflows introduces unacceptable attack vectors. Security teams demand strict perimeter isolation, zero-trust network paths, and dynamic permission scoping.

Architectural Blueprint: How AgentCore Gateway Works

To solve these challenges, MoEngage adopted a centralized yet decoupled gateway pattern. Instead of writing custom middleware or managing hundreds of protocol translators, they utilized AgentCore Gateway to dynamically ingest OpenAPI specifications and transform them into secure, permission-scoped agent tools on the fly.

\{
  "gateway_config": \{
    "name": "moengage-global-gateway