Self-Service Analytics With Claude: A Real Walkthrough
A real end-to-end walkthrough of shipping self-service analytics with Claude: spec, curated views, MCP tools, skills, evals, and a trusted rollout.
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 real end-to-end walkthrough of shipping self-service analytics with Claude: spec, curated views, MCP tools, skills, evals, and a trusted rollout.
Failure modes, blast radius, and containment for Claude agents in production — least-privilege permissions, evals, tripwires, and prompt-injection defense.
Map failure modes, cap blast radius, and contain incidents when deploying Claude Code agents and Skills. A practical risk playbook for production.
Failure modes, blast radius, and containment for Claude-powered self-service analytics: bounded tools, database access control, provenance, and eval gating.
Realistic failure scenarios in Claude Cowork and concrete controls — scoped connectors, human-in-the-loop on writes, and audit trails — to contain the blast radius.
The roles and skill shifts that make self-service data analytics with Claude work: semantic-layer owners, MCP toolsmiths, eval engineers, and translators.
The roles, skills, and interview changes needed to run an AI-native engineering org on Claude Code — what to learn, who to hire, and how to retrain.
The concrete skills and hiring shifts that decide whether Claude Cowork becomes a daily multiplier or an abandoned tab for your knowledge-work team.
What engineers must learn to build reliably with Claude Code and Agent Skills: procedural articulation, verification, and the new skill-engineer role.
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