Scaling Claude Code from one team to many cleanly
Patterns from a Built-with-Opus hackathon for scaling agentic coding with Claude across an organization — a thin shared spine, champions, and inherited guardrails.
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
Patterns from a Built-with-Opus hackathon for scaling agentic coding with Claude across an organization — a thin shared spine, champions, and inherited guardrails.
Honest trade-offs from a Built-with-Opus hackathon: when agentic coding with Claude pays off, when it doesn't, and the simpler alternatives to choose instead.
The governance, trust, and safety guardrails leadership needs before scaling agentic coding with Claude — permission boundaries, data controls, and audit logging.
Change-management lessons from a Built-with-Opus hackathon: the habits, norms, and review rituals that make Claude Code adoption durable across a team.
A concrete cost model for Claude Code and Opus 4.8 ROI — where time and money savings actually come from, and the loops that quietly erase them.
A hackathon-tested playbook for moving an existing workflow onto Claude Code agents — shadow runs, canary cutover, rollback, and parity evals.
A hackathon playbook for testing Opus 4.8 agents — eval sets, rubric scoring, LLM judges, and an automated eval loop that gates every release.
Harden Opus 4.8 agents — sandboxing, least privilege, secrets handling, and prompt-injection defense from a Built-with-Opus hackathon.
Keep Opus 4.8 agent runs cheap and fast — prompt caching, batching tool calls, and context pruning from a Built-with-Opus hackathon.