I built one of these RLM harnesses and a local MCP server along with logging, memories, and project rules based on directories. It worked great for a while but the foundational models have largely caught up to the point where they don't need this harness anymore. At least for my use cases. I can basically just store context in .md in the directories we work out of together and accomplish what I need.
I went the skills route - skill improvement skill and a rule to use it always. Basically it's directed that if anything causes more than a hop of thinking - failure - try something else it should flag that it needs to learn it as a skill so it never does it other than first shot again or if an existing skill fails improve it after it solves whatever problem. it then syncs the files to a shared location and updates the version and also pulls new skills. this let's a team use it or you have multiple workstations.
The core idea of the RLM paper is to make a regular LLM act more like a coding agent - offload context to something external that needs to be explicitly queried instead of filling up valuable context. The "recursion" part of the paper really only wins because they use a top-tier model for the root agent, and cheaper models for the sub-agents.
Prime Agent took the RLM idea (which is really just an academic view on how coding agents have always worked) and then added this "continual harness" idea. This part isn't super well described in the blog post, but includes some message passing between the agents, and the ability to share code.
Overall I chalk it up as neat, but not revolutionary. Another version of what most of these systems are already doing.