An MCP agent use case: from problem to shipped
A realistic end-to-end walkthrough of building a production MCP agent on Claude — from messy problem to shipped, monitored outcome in 2026.
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
A realistic end-to-end walkthrough of building a production MCP agent on Claude — from messy problem to shipped, monitored outcome in 2026.
Agents with production access can do real damage. Failure scenarios, blast-radius thinking, and containment patterns for Claude MCP agents in 2026.
When Claude agents reach production via MCP, your team's skills shift. The five capabilities to hire and train for in 2026, plus emerging roles.
Grow Claude + MCP agents from one team to many — shared servers, a skills registry, eval standards, and the platform layer that prevents chaos.
An honest guide to Claude + MCP agents: where they shine, where a script or human wins, and how to match the tool to the task quickly.
Permission scoping, audit trails, and eval gates leadership needs before scaling Claude + MCP agents — guardrails that prevent quiet disasters.
Team adoption is the hard part of production agents. Habits, norms, and change management for shipping Claude + MCP agents that actually stick.
Where time and money savings from Claude + MCP agents actually come from — a defensible cost model including the parts vendors leave off the slide.
A phased playbook for moving a production workflow onto a Claude MCP agent — shadow mode, human-in-the-loop, canary rollout, and clean rollback paths.