How to Solve the Agent Handoff Problem

Agent handoffs can fail when full transcripts transfer abandoned ideas alongside final decisions, leaving subsequent agents unable to distinguish useful context from dead ends. The post proposes using Meko, a YugabyteDB-powered context engine, to store private agent memory beyond a single session and selectively promote approved records into shared knowledge accessible across accounts within a datapack. Its Python demonstration uses chef, kitchen manager, and restaurant manager agents to show that private records remain isolated until a coded policy promotes only final menu decisions with supporting reasons, while open questions and operational shopping notes stay private. The approach relies on direct memory writes for exact, traceable decision records, records promotion actions in an audit trail, and is presented as framework-independent for systems such as Strands, LangGraph, and Pydantic AI.

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