Yitumulin Agent for Personalized Planning
Problem
Generic chat agents were weak in long-term memory recall, user-specific consistency, and execution-ready planning.
Strategy
Designed a full chain from intent parsing, slot clarification, and memory retrieval to multi-agent coordination, generation, and tool execution.
Outcome
Built a reusable personalized agent prototype with semantic-entropy-based follow-up logic for fitness and companion-planning scenarios.
Impact
In 100+ rounds of validation, memory hit rate improved about 35%-40%, key-slot completion improved about 40%, and actionable-plan quality improved about 30%.