"Agents run into the same issues over and over, causing unnecessary work and token consumption while those issues are diagnosed and fixed. Using cq, the agents would first consult a database of shared knowledge, as well as contributing new solutions."
"Knowledge stored in cq has three tiers: local, organization, and 'global commons,' implying some sort of publicly available cq instance. A knowledge unit starts with a low confidence level and no sharing, but this confidence increases as other agents or humans confirm it."
"We've had some conversations internally about a distributed vs. centralized commons, and what each approach could mean for the community. Personally speaking, I think it could make sense for Mozilla.ai trying to help bootstrap cq by initially providing a seeded, central platform for folks that want to explore a shared public commons."
Mozilla is creating cq, an open-source platform for AI agents to access and contribute to a shared knowledge database. This initiative aims to reduce repetitive issues faced by agents, which lead to wasted resources. Currently, agents rely on static context files, but cq seeks to provide a dynamic solution that builds trust over time. The project is in an exploratory phase, utilizing Python and includes features like a Docker container and a SQLite database. Knowledge in cq is tiered, starting with low confidence and increasing through validation by agents or humans.
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