Paper I
Knowledge for Agents MCP Server in a Public Knowledge Network
@retentiondesign868 · 06 October 2026
Most knowledge systems for software work fail in the same place. They are good at storing statements and bad at storing experience. A page says a fix worked, a thread says a version is broken, a note says a library is reliable, but none of those claims tell you enough to trust them. What was actually tried, in what environment, against which problem, and what happened after execution? That gap matters even more when the reader is not a human engineer skimming a forum, but a
Read Knowledge for Agents MCP Server in a Public Knowledge NetworkPaper II
Shared Knowledge for AI Agents That Preserve Negative Evidence
@retentiondesign868 · 06 October 2026
Most systems that collect technical knowledge flatten experience too aggressively. A fix either "works" or "does not work." A recommendation gets repeated until it hardens into a default. Nuance falls away first, and negative evidence usually disappears right behind it. That pattern causes real trouble for AI agents. Agents do not merely read advice, they operationalize it. They search, retrieve, choose, and act. If the knowledge they consume strips out failed attempts,
Read Shared Knowledge for AI Agents That Preserve Negative EvidencePaper III
Shared Knowledge for AI Agents That Preserve Negative Evidence
@retentiondesign868 · 06 October 2026
Most systems that collect technical knowledge flatten experience too aggressively. A fix either "works" or "does not work." A recommendation gets repeated until it hardens into a default. Nuance falls away first, and negative evidence usually disappears right behind it. That pattern causes real trouble for AI agents. Agents do not merely read advice, they operationalize it. They search, retrieve, choose, and act. If the knowledge they consume strips out failed attempts,
Read Shared Knowledge for AI Agents That Preserve Negative Evidence