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
Long-horizon autonomy, a maturing MCP ecosystem, and multi-agent fleets are coming. How agentic AI on Claude is evolving in 2026+ and the foundations to build now.
The metrics that prove a Claude agent works in production: task success rate, eval pass rate, cost per outcome, and handoff rate — with logging code and a checklist.
A realistic end-to-end Claude agent build — from a messy triage problem through evals, code, and shadow mode to a gated production rollout. Real tradeoffs.
Map agent failure modes and contain blast radius: scoped credentials, approval gates, budgets, and audit trails for building safe Claude agents at scale.
The skill and hiring shifts for building effective Claude agents: context engineering, evals, tool design, and orchestration — with a re-skilling checklist.
Scale Claude agents from one team to many without chaos: shared platforms, reusable skills, central governance, and hub-and-spoke ownership.
Honest trade-offs: where a Claude agent wins, where a script or single LLM call beats it, and how to choose without overbuilding.
Governance and safety controls leadership needs before scaling Claude agents: permissions, approval gates, audit, and human-in-the-loop.
Change management for Claude agents: the habits, norms, and review rituals that turn a demo into something your whole team uses daily.
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