What Retain and Recall Actually Look Like in a Working Agent

The "Community Time Machine" project is an agent that observes a stream of community messages, such as those found in a Discord server for an open-source project, and assists moderators in converting recurring questions into lasting knowledge. The system operates through a four-step loop: remember, reuse, intervene, measure, and learn.

Two critical components of this loop are recurring question detection and FAQ generation. When a moderator asks the system about a problem that has occurred before, instead of providing a generic response, the agent recognizes the pattern, retrieves the actual historical evidence, and drafts a community-informed answer. Once the moderator publishes the answer, the agent records the event, enabling it to compare activity before and after the intervention later.

This process relies on the agent's memory, which is retained and recalled without any human intervention. The technical implementation involves writing messages and events into memory, tagged with context that allows them to be found again later based on meaning rather than keywords. Retrieval of memories, whether for generating FAQs or reconstructing timelines, is performed through a unified search method that abstracts away the underlying storage details.

The FAQ generation agent must ensure there's sufficient evidence before providing an answer, returning an "INSUFFICIENT_EVIDENCE" status if not. This status is a first-class outcome rather than a fallback message. The investigation agent is required to distinguish observations from interpretations, cite evidence using memory IDs, and explicitly state uncertainties if the evidence is weak or conflicting.