Governance for Claude Skills: guardrails before you scale
The trust, safety, and governance guardrails leaders need before scaling Claude Agent Skills — least privilege, tiered review, and audit trails.
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 Agent Skills — least privilege, tiered review, and audit trails.
The norms, rituals, and roles that turn Claude Agent Skills into a durable team habit instead of a one-off demo that goes stale.
Where the real savings from Claude Agent Skills come from, the break-even formula, and how to measure ROI honestly against prompts and MCP.
A staged playbook to move an existing workflow onto Claude Skills and MCP: shadow mode, canary ramp, fallbacks, and instant rollback.
Build an eval loop for Claude Skills and agents: pick graders, set quality gates, handle nondeterminism, and ship agentic changes with confidence.
Harden Claude Agent Skills and MCP tools: sandboxing, least privilege, secrets in the tool layer, and layered prompt-injection defense for 2026.
Keep Claude Skills and agent runs cheap and fast: prompt caching, batching, progressive disclosure, and routing across Opus, Sonnet, and Haiku.
Why Claude agents loop, call wrong tools, or hallucinate args with Skills — and the layer-by-layer tracing and guardrails that fix each failure mode.
Design prompt and context for Claude Agent Skills: what belongs in context, what to leave on disk, and why lean context makes sharper, cheaper agents.