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A handoff pattern for Claude Code and other AI agent harnesses allows tasks to be delegated to fresh sessions, avoiding usage caps, performance degradation, and high costs by generating a script for another session to execute specific tasks.
A comprehensive breakdown of memory architecture in major AI agent platforms (Claude Code, OpenAI Codex, Copilot, Windsurf, Devin, etc.), discussing how memory is managed, current shortcomings, and future directions.
New research on scaling laws for agent harnesses reveals that most token and tool call volume does not matter; the work introduces an effective approach.
A research paper from PwC finds that grep-style text search, when properly integrated into agent harnesses, can match or beat embedding-based retrieval for coding-agent tasks, suggesting vector databases may not be essential for many use cases.