Scaling Multi-Agent Systems Across an Organization
Take multi-agent Claude systems from one team to many without chaos — shared platforms, reusable patterns, governance at scale, and avoiding sprawl.
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
Take multi-agent Claude systems from one team to many without chaos — shared platforms, reusable patterns, governance at scale, and avoiding sprawl.
An honest look at multi-agent Claude systems vs a single agent, a workflow, or plain code — with a decision framework engineers can actually apply.
The trust and safety guardrails leadership needs before scaling multi-agent Claude systems — permissions, audit trails, kill switches, and HITL gates.
How engineering teams adopt multi-agent Claude systems for real — norms, review habits, and change management that turn a demo into daily practice.
Where multi-agent Claude systems actually save time and money — token economics, parallelism, model routing, and the cost model leaders need.
A safe, incremental playbook for moving an existing workflow onto a Claude multi-agent system with shadow mode, canary rollout, and fallback.
Build an eval loop with golden datasets, LLM-as-judge scoring, and CI gates to measure quality and ship Claude multi-agent changes safely.
Sandboxing, least privilege, secret handling, and prompt-injection defense for Claude multi-agent systems that call real tools.
Use prompt caching, batching, and model routing to keep Claude multi-agent runs cheap and fast without sacrificing output quality.