AgentScaffold Adds Memory, Peer Review and Learning Loops to AI Coding Agents

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A software engineer with backgrounds at Boeing, Salesforce, and Dropbox has released AgentScaffold, an open-source Python package designed to address three core weaknesses in AI coding agents. The tool tackles the lack of persistent memory across sessions, inconsistent planning discipline, and the inability to learn from past mistakes. AgentScaffold runs as an MCP server, making it compatible with tools like Cursor, Claude Code, and Windsurf, and uses DuckDB with a property graph to index codebases across eight programming languages. It also ingests governance artifacts such as plans, architecture decision records, and review findings, linking them directly to the relevant code. The framework aims to bring engineering-process discipline to AI-assisted development by ensuring mistakes are caught before shipping and standards improve over time.

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