Risk Management for Claude Agent SDK Deployments
Map agent failure scenarios, limit blast radius, and contain mistakes — least privilege, approval gates, fail-safe tools, and kill switches with the Claude Agent SDK.
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
Map agent failure scenarios, limit blast radius, and contain mistakes — least privilege, approval gates, fail-safe tools, and kill switches with the Claude Agent SDK.
The real skills, roles, and hiring shifts that make Claude Agent SDK projects succeed — tool design, context engineering, evals, and failure thinking.
Scale agents built on the Claude Agent SDK from one team to many — shared platform, reusable skills, a tool registry, central evals, and clear ownership.
Honest trade-offs for the Claude Agent SDK — when an agent is right, when a single prompt or deterministic workflow wins, and when to use no AI at all.
Permissions, approval gates, audit trails, evals, and kill switches — the governance and safety controls leadership needs before scaling Claude Agent SDK agents.
The habits, norms, and review rituals that make Claude Agent SDK adoption stick across an engineering team — and the lone-expert trap to avoid.
A concrete cost model for agents built on the Claude Agent SDK — where token, labor, and cycle-time savings actually come from, and how to defend the budget.
A staged playbook for moving an existing workflow onto the Claude Agent SDK — shadow mode, gradual rollout, guardrails, and tested rollback.
Build an eval loop for Claude Agent SDK agents — score trajectories, use an LLM judge, and gate releases on measurable quality thresholds.