Team Adoption of Multi-Agent Claude: Habits That Stick
The habits, norms, and change-management moves that turn a clever multi-agent Claude demo into daily team practice that actually sticks.
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 moves that turn a clever multi-agent Claude demo into daily team practice that actually sticks.
A concrete cost model for security teams: where Claude agents save time and money against AI-accelerated offense — and where the savings quietly leak.
A practical cost model for multi-agent Claude systems: where time and money savings come from, what they cost in tokens, and how to measure real payback.
A safe playbook for moving an existing workflow onto Claude agents — shadow mode, human-in-the-loop rollout, scoped autonomy, and clean rollback paths.
A staged playbook for moving an existing workflow onto a Claude multi-agent system: mapping, shadow mode, canary rollout, and instant rollback.
Build an eval loop for Claude multi-agent systems: trajectory checks, LLM judges, deterministic gates, and CI thresholds that block regressions.
Measure agent quality and gate releases with an eval loop — outcome and trajectory grading, LLM-as-judge, regression suites, and CI gates for Claude agents.
Sandboxing, least privilege, secrets handling, and prompt-injection defense for production Claude agents — the controls that make an autonomous agent safe to ship.
Sandbox execution, grant least privilege, keep secrets out of context, and defend against prompt injection in Claude multi-agent systems.