When to Use Agent Skills — and When Not To
Honest trade-offs for Claude Agent Skills — where they win, where a prompt or script beats them, and how to choose the right tool for the job.
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
Honest trade-offs for Claude Agent Skills — where they win, where a prompt or script beats them, and how to choose the right tool for the job.
The trust and safety guardrails leaders need before scaling Claude Agent Skills — least privilege, review gates, audit logs, and human oversight.
Build habits, norms, and change management around Claude Agent Skills so adoption sticks across your team instead of fizzling after the demo.
A practical cost model for Claude Agent Skills — where tokens go, how savings are created, and how to decide if a Skill is worth building.
A phased, low-risk plan to move an existing workflow onto Claude agents and Skills — shadow mode, human-in-the-loop, staged autonomy, and rollback.
Measure Claude agent quality and gate releases with an eval loop — test sets, scoring rubrics, LLM-as-judge, and CI gates that catch regressions early.
Harden Claude agents with sandboxing, least-privilege tool scopes, secret isolation, and layered defenses against prompt injection.
Keep Claude agents fast and cheap with prompt caching, batching, smart model routing, and context discipline — without sacrificing answer quality.
Diagnose the real failure modes of Claude agents — infinite loops, wrong tool calls, and hallucinated arguments — with concrete, trace-driven fixes.