How to architect a private, multi-tenant agentic RAG system — the components, data flows, and security principles for a self-hosted chatbot that gives each user personalized, permission-scoped access to their own documents.

The principles for designing tools an agent can actually use well: clarity, specificity, strong typing, clean outputs, and errors that enable self-correction.

Agentic workflows split knowledge work across specialized agents. Learn the distinct roles of Research, Analyst, and Editor agents and how they form a repeatable production line.

A reference architecture for trustworthy agentic AI, using the CLI agent as the worked example: the shared lifecycle, planning styles, MCP tooling, and the guardrails that keep it safe.

A practical guide to building a personal AI agent: the components that matter, when to reach for no-code versus code, a lean build sequence, and the pitfalls that sink first attempts.