How a tiered document system can give AI agents persistent, scalable memory

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AI agents start each session without memory, which creates consistency problems when multiple teams rely on them across different projects. A tiered architecture addresses this by placing universal rules in a root-level document, project-specific rules one level below, and a large library of reference documents that only load when a relevant action triggers them. This structure keeps token costs low by ensuring only the most essential rules are read in every session, while deeper knowledge is delivered precisely when needed. Human oversight is split into two distinct channels: escalation for decisions beyond the agent's authority, and calibration for correcting mistakes in a way that persists beyond a single chat window. The author argues that routing both channels through a communication tool teams already use, such as Microsoft Teams, is critical to ensuring alerts are actually seen and acted upon.

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ProgrammingDEV Community ·

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