Scaling Claude Agents From One Team to Many
Grow Claude agent workflows org-wide without chaos: shared platforms, reusable skills, MCP registries, and federated ownership that make scaling compound.
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
Grow Claude agent workflows org-wide without chaos: shared platforms, reusable skills, MCP registries, and federated ownership that make scaling compound.
Honest trade-offs: when a Claude agent is the right tool and when a script or single model call wins. A clear decision guide for agentic workflows.
Permissions, audit trails, evals, and human gates leadership needs before scaling Claude agents — the governance that makes autonomy safe enough to speed up.
Habits, norms, and change management that make Claude agent workflows stick — beyond the demo. A field guide to real team adoption and measuring it.
Where Claude agent workflow savings actually come from: a concrete cost model covering tokens, the multi-agent tax, review time, routing, and caching.
A safe playbook for moving an existing workflow onto Claude agents: strangler pattern, shadow mode, staged rollout, and tested rollback.
Build an eval loop for Claude agents: dataset design, LLM-judge scoring, and regression gating that ships changes without breaking quality.
Harden Claude agents: sandboxing, least privilege, secrets handling, and concrete prompt-injection defenses that bound the blast radius.
Make Claude agents cheap and fast: prompt caching, batching, context compaction, and model routing that cut cost without losing quality.