The article outlines the technical architecture required to build an AI "second brain," focusing on three core components: knowledge bases, voice profiles, and persistent memory systems. Knowledge bases leverage Retrieval-Augmented Generation (RAG) to ground AI responses in external, updatable documents, enabling domain specialization without retraining the underlying model. Advanced implementations, termed Agentic RAG, allow the agent to autonomously decide when and how to query the knowledge store, breaking complex questions into sub-queries. Memory systems are categorized into three tiers: working memory (active context per session), episodic/semantic memory (accumulated facts and history), and long-term personality/traits (stable behavioral rules). OpenAI's evolution from simple saved memories (launched in April 2024) to the Dreaming architecture (June 2026) exemplifies this progression, achieving marked improvements in factual recall (41.5% to 82.8%), preference adherence (31.4% to 71.3%), and time-sensitive recall (9.4% to 75.1%). Voice profiles, governed by structured system prompts, define agent communication styles using numeric scales for tone and explicit vocabulary rules. The integration of these components into a unified system involves a layered architecture: a system prompt for identity, a retrieval layer for knowledge grounding, and a memory layer for contextual continuity. However, risks include error propagation, stale context, prompt injection attacks, and regulatory scrutiny under GDPR and the EU AI Act, with Italy fining OpenAI €15 million in 2024 for data processing violations. The article concludes that while RAG and basic memory are production-ready, challenges remain in automatic memory revision, security, and compliance, making memory quality and governance the emerging bottleneck for AI agent development.