Building personalized AI agents requires separating reasoning from execution and treating memory as a first-class system component. The architecture uses three layers: Agent Development Kit (ADK) for orchestration, Model Context Protocol (MCP) for tool boundaries, and structured long-term memory for preferences. Key patterns include treating agents as planners not doers, implementing memory admission policies to filter what gets persisted, and enforcing guardrails on tools with side effects. The approach prevents common failures like context pollution, uncontrolled tool execution, and unpredictable behavior as agents scale.
Table of contents
Why Personalization Breaks Most AI AgentsTable of ContentsPrerequisitesWhat “Personalized” Means in a Real AI AgentHow the Agent Architecture Fits TogetherHow to Design the Agent Core with ADKHow to Connect Tools Safely with MCPHow to Add Long-Term Memory Without Polluting ContextHow the End-to-End Agent Flow WorksCommon Pitfalls You’ll Hit (and How to Avoid Them)What You Learned and Where to Go NextSort: