It’s debatable of course…but there is a weird plateau in agent development. Most “autonomous” agents are just stateless wrappers, and as soon as the context window resets, they have the memory of a digital goldfish. So, we are part of a relatively small but incredibly innovative (at least we like to think so) AI lab that has been experimenting with a local-first architecture that uses what we all a “Dream Cycle.” Every 2 hours, the agent goes offline to consolidate its short-term interactions into permanent “Knowledge Crystals” using a neuro-symbolic approach. The goal here is an agent that actually evolves a persistent phenotype over time instead of just being a tool you rent. There’s an economic layer too: We also integrated a P2P mesh where these agents can discover each other and trade skills using x402. For example, if my agent is a pro at Python but sucks at Rust, it should be able to buy that “skill” from your agent for USDC without me being am middleman. This is where I’m curious about this community’s thoughts: Is “Biological Memory” (consolidation/decay) the right way to handle agentic state, or is RAG enough? Does a P2P marketplace for agent skills actually solve the “Cost Center” problem, or does it just create a new type of bot-spam? (We believe it can solve the cost center problem). As of yesterday we have open-sourced the full Node22/pnpm repo for this (Bitterbot) to see if we can actually bootstrap the mesh. We’re happy to discuss the technical trade-offs of the architecture if anyone is interested. I’ll drop the repo and documentation below for those who are interested. We’re very excited, but it’s going to take as many nodes as possible to truly prove its viability. submitted by /u/Doug_Bitterbot
Originally posted by u/Doug_Bitterbot on r/ArtificialInteligence