A design for stateful AI agents that combines short-term conversational context with long-term, vector-based recall — so the agent stops forgetting.

Vector databases are specialized systems for fast similarity search in high-dimensional embedding spaces. This guide covers why ordinary databases fail at it, the core ANN algorithms (HNSW, IVF, PQ), the recall-latency trade-off, and when you actually need one.

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.