Claude Code at Scale
5 articles in series
Five essays on the Applied AI team's enterprise patterns for Claude Code rollouts — the harness, RAG vs agentic search, the DRI problem, and more.
Agentic Search vs. RAG: Why Claude Code Doesn't Index Your Codebase (And Why That's the Point)
RAG-based code search hits a wall at scale: the index is stale before it ships. Claude Code's grep-and-read approach trades upfront indexing for live traversal — and that tradeoff scales differently than most teams realize.
CLAUDE.md Decay: The 3–6 Month Review Cadence Anthropic Just Made Official
Instructions that helped your old model can hurt your new one. Anthropic's Applied AI team just put a number on how often you should audit your CLAUDE.md, hooks, and skills — and gave two concrete examples of rules that aged badly.
LSP: The Highest-ROI Investment Most Teams Skip
Anthropic's enterprise team is direct: for multi-language codebases, Language Server Protocol integration is one of the highest-leverage investments you can make. One enterprise rolled it out org-wide before opening Claude Code access. Here's why.
The DRI Problem: Why Most Claude Code Rollouts Plateau
Bottoms-up adoption gets you to 30% engagement. Then it stops. Anthropic's Applied AI team just named the missing piece: a directly responsible individual for the Claude Code configuration. Most orgs don't have one.
The Harness, Not the Model: Why Most Claude Code Teams Are Optimizing the Wrong Variable
Anthropic's Applied AI team published their patterns from enterprise rollouts. The headline finding: the ecosystem around the model — the harness — determines outcomes more than the model itself.