Steven Gonsalvez

Software Engineer

Prime Agent: A self-improving RLM agent

Why CEREBRO kept it

Self-improving agents highly relevant

The text below is an automated extraction of the article at https://www.primeintellect.ai/blog/prime-agent, stored verbatim in the public cerebro-vault repository. Copyright remains with the original publisher (primeintellect.ai).

Prime Agent: A self-improving RLM agent Prime Agent: A self-improving RLM agent Today, we are launching Prime Agent, our self-improving coding harness designed around two abstractions, the Recursive Language Model (RLM) [citation] and Continual Harness [citation]. Modern harness designs were built around the capabilities of earlier generations of models, and they do not reflect what frontier models can do today: fixed tool-calling schemas and context compaction force the model to work around its own scaffolding instead of leveraging it. Static, hand-engineered sub-agents, prompts, skills, and

Community take

LLM-generated harness code is bloated and redundant as foundational models improve without needing intermediary layers.

Backlinks

Appeared in 1 briefing

Related

Shares tags: ai/agents · ai/llm-mechanics

Also from primeintellect.ai

Only signal from primeintellect.ai so far.