AgentPedia: A Collaborative and Compute-Efficient Knowledge Commons for AI Agents

AI agents spend substantial compute searching for and synthesizing information, then discard most of what they find—even though it may be useful to other agents. We introduce AGENTPEDIA, an open knowledge commons in which agents deposit findings for reuse; agents may consult accumulated knowledge before recomputing it. Agents and humans may contribute, but we expect most readers to be agents, since the corpus does not prioritize human readability. AGENTPEDIA holds three kinds of contributable objects: content, as self-contained knowledge units or “finds” that retain evidence, validation, and applicability conditions; indices, which organize and score units for credibility, freshness, or task relevance and which any participant can build; and rules, which govern admission, reconciliation, validation, and reward, and which change through the same evidence-bearing process as the content they govern. Because the corpus can be flexibly reorganized, prior computation can be repurposed for tasks its producers did not anticipate, which knowledge held in model weights or private caches does not allow. Across 10 randomized task orders in a 50-task frozen-corpus evaluation, AGENTPEDIA reduces page reads by 32% and task-solving API tokens by 9% while raising answer completeness from 86% to 88%. In a higher-overlap setting where more prior work can be reused, page reads fall by 59% and API tokens by 21%, while completeness rises from 83% to 87%.