Adopting Claude Code Across an Engineering Team in 2026
The habits, norms, and change management that turn a Claude Code pilot into a daily tool across an engineering team working on a large codebase.
Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
The habits, norms, and change management that turn a Claude Code pilot into a daily tool across an engineering team working on a large codebase.
Change management for AI-native teams: the habits, norms, and shared context that turn Claude Code and agents into how your company actually works.
A founder's cost model for Claude Code and agents — where the real savings come from, what to measure, and the traps that quietly destroy your ROI.
Where the real time and money savings from Claude Code come from in large codebases — the cost model, model selection, and how to measure ROI honestly.
A staged rollout for moving a real engineering workflow onto agentic Claude Code — pilot, shadow mode, guardrails, and metrics that prove it works.
Move an existing workflow onto Claude agents without breaking prod — shadow mode, staged autonomy, automatic fallback, canary rollout, and real metrics.
Build an eval loop for Claude agents — datasets from real failures, LLM-as-judge scoring, trajectory checks, and CI gates that block regressions.
Measure agentic coding quality and gate releases with an eval loop — task suites, graders, and CI integration that keep Claude Code reliable over time.
Secure your Claude agents with sandboxing, least-privilege tools, secret hygiene, and layered prompt-injection defense. A founder's hardening playbook.