Governance and Guardrails for Claude Code in GTM
The trust, safety, and governance guardrails leaders need before scaling Claude Code across a GTM team handling real customer data.
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 trust, safety, and governance guardrails leaders need before scaling Claude Code across a GTM team handling real customer data.
Habits, norms, and change management to make Claude Code stick across a GTM engineering team instead of letting seats sit unused.
The change-management playbook for Claude Cowork — building durable habits, norms, and team adoption for agentic knowledge work past the pilot.
A clear breakdown of the Claude Cowork cost model — where agentic knowledge work saves real time and money, and where it quietly burns tokens instead.
A grounded cost model for rebuilding GTM workflows on Claude Code: where time and money savings really come from, and what doesn't pay back.
Move an existing workflow onto a Claude agent safely with shadow runs, human-in-the-loop review, canary traffic, and a warm rollback path.
A safe rollout playbook for moving an existing GTM workflow onto Claude Code — shadow mode, incremental cutover, fallbacks, and outcome metrics.
Build an eval loop for Claude agents with golden tasks, trajectory checks, LLM judges, and CI gates that catch regressions before they reach production.
Build an eval loop for Claude Code agents — test cases, programmatic and LLM-as-judge graders, regression suites, and release gates for GTM workflows.