About This Architecture

AI Developer CLI architecture integrates a local LLM client with AWS Lambda-based tool execution through an AgentCore Gateway secured by IAM SigV4 authentication. The CLI client communicates via MCP endpoint to the gateway, which routes requests to Tool Lambda functions running Java 21 on ARM64 in a private VPC subnet, with cross-account Client Lambda for downstream API access. CloudWatch and X-Ray provide comprehensive observability across the pipeline, while KMS customer-managed keys encrypt sensitive data at rest and in transit. This pattern enables developers to leverage local LLM reasoning while securely executing AWS-hosted tools with fine-grained IAM controls and audit trails. Fork and customize this diagram on Diagrams.so to adapt VPC CIDR blocks, Lambda memory configurations, or add additional tool functions for your AI agent workload.

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How do I securely integrate a local LLM with AWS Lambda tools using MCP endpoints and IAM authentication?

This diagram shows an AI Developer CLI architecture where a local LLM client connects via MCP endpoint to an AgentCore Gateway secured with IAM SigV4, routing requests to Tool Lambda functions in a private VPC subnet. Tool Lambda executes in Java 21 on ARM64, invokes cross-account Client Lambda for downstream APIs, and all activity is encrypted with KMS keys and monitored via CloudWatch and X-Ray.