Risk Management for Claude Opus Agents in Claude Code
Failure modes, blast radius, and containment for Claude Opus agents in Claude Code: permissions, sandboxes, eval gates, and reliable rollback.
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
Failure modes, blast radius, and containment for Claude Opus agents in Claude Code: permissions, sandboxes, eval gates, and reliable rollback.
The skill and hiring shifts behind getting real value from Claude Opus in Claude Code: specs, evals, MCP plumbing, and agent supervision.
How to scale Claude Opus and Claude Code across an org without chaos: shared skills, standardized conventions, governed MCP, and a contribution flywheel.
Honest trade-offs for Claude Opus in Claude Code: where it wins, where cheaper models or plain scripts win, and how to classify a task before you spend.
Trust and safety guardrails leadership needs before scaling Claude Opus and Claude Code: least privilege, approval gates, audit trails, ownership.
Habits, norms, and change management to get a whole team fluent with Claude Opus in Claude Code — beyond the early-adopter demo that always works.
Where time and money savings from Claude Opus 4.8 in Claude Code actually come from, and a cost-per-outcome model leaders can defend to finance.
Move an existing workflow onto a Claude Opus agent without a risky big-bang switch — shadow mode, staged tool access, and a measured, reversible rollout.
Measure Claude Opus agent quality and gate every release with a rubric-graded eval loop, LLM-as-judge scoring, and a CI quality bar in Claude Code.