Executive Summary

Mon, 01 Jan 0001 00:00:00 +0000

Executive Summary#

The Engagement#

A 500-developer engineering organization with strict security requirements needs to adopt Claude Code as an enterprise-wide AI-assisted development tool. Their core constraint: no code can leave their network.

This document describes a 12-week implementation across three workstreams:

Infrastructure (30% effort): Cloud LLM service (AWS Bedrock / GCP Vertex AI / Azure Foundry) + private networking + LLM Gateway
Platform Engineering (40% effort): Managed configurations, CLAUDE.md architecture, skills library, developer environments
Change Management (30% effort): Phased rollout, champion program, productivity measurement

The Architecture in One Paragraph#

Developer workstations connect through the corporate network to an internal LLM gateway (LiteLLM or Kong AI Gateway), which routes requests through a VPC endpoint via AWS PrivateLink to Amazon Bedrock. Bedrock hosts the Claude models within AWS’s data boundary. No traffic touches the public internet. No code is retained or used for training. The LLM gateway provides per-user token budgets, centralized authentication, and audit logging. Managed settings enforce organization-wide security policies that individual developers cannot override.

Architecture Overview

Mon, 01 Jan 0001 00:00:00 +0000

Architecture Overview#

End-to-End Request Flow#

┌─────────────────────────────────────────────────────────────────────┐
│ CORPORATE NETWORK │
│ │
│ ┌───────────────┐ ┌───────────────┐ ┌───────────────┐ │
│ │ Developer 1 │ │ Developer 2 │ │ Developer N │ │
│ │ claude CLI │ │ claude CLI │ │ claude CLI │ │
│ │ │ │ │ │ │ │
│ │ managed- │ │ managed- │ │ managed- │ │
│ │ settings.json│ │ settings.json│ │ settings.json│ │
│ └──────┬────────┘ └──────┬────────┘ └──────┬────────┘ │
│ │ │ │ │
│ └──────────────────┼──────────────────┘ │
│ │ │
│ ▼ │
│ ┌──────────────────────────┐ │
│ │ LLM GATEWAY │ │
│ │ (LiteLLM / Kong AI) │ │
│ │ │ │
│ │ • SSO authentication │ │
│ │ • Per-user/team budgets │ │
│ │ • Rate limiting │ │
│ │ • Request logging │ │
│ │ • Model routing │ │
│ │ • Holds AWS credentials │ │
│ └───────────┬──────────────┘ │
│ │ │
└──────────────────────────┼──────────────────────────────────────────┘
 │ (Direct Connect / Site-to-Site VPN)
 │
┌──────────────────────────┼──────────────────────────────────────────┐
│ AWS ACCOUNT (Dedicated) │
│ │ │
│ ┌───────────┴─────────────┐ │
│ │ VPC ENDPOINT │ │
│ │ (PrivateLink) │ │
│ │ │ │
│ │ com.amazonaws.{region} │ │
│ │ .bedrock-runtime │ │
│ │ │ │
│ │ Policy: InvokeModel, │ │
│ │ InvokeModelWith │ │
│ │ ResponseStream ONLY │ │
│ └───────────┬─────────────┘ │
│ │ │
│ ┌───────────┴─────────────┐ │
│ │ AMAZON BEDROCK │ │
│ │ │ │
│ │ Claude Sonnet/Opus │ │
│ │ (primary model) │ │
│ │ │ │
│ │ Claude Haiku │ │
│ │ (fast model) │ │
│ │ │ │
│ │ • No data retention │ │
│ │ • No training use │ │
│ │ • CloudTrail audit │ │
│ └─────────────────────────┘ │
│ │
│ ┌─────────────────────────────────────────────────────────────┐ │
│ │ OBSERVABILITY │ │
│ │ • CloudTrail → every InvokeModel call with IAM principal │ │
│ │ • CloudWatch → token usage, latency, error rates │ │
│ │ • Cost Explorer → per-account Bedrock spending │ │
│ └─────────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────┘

Note: This diagram shows AWS Bedrock deployment. Equivalent architectures for GCP Vertex AI (VPC Service Controls, Private Service Connect, Cloud Logging) and Azure Foundry (Private Endpoints, VNet integration, Azure Monitor) are documented in the Phase 0 infrastructure guides.