Prime Agent: A self-improving RLM agent

Prime Intellect has launched Prime Agent, an open-source, self-improving coding-agent harness built around Recursive Language Models and a Continual Harness, which let agents programmatically manage context, tools, sub-agents, prompts, skills, and memory through a persistent IPython REPL. Its architecture supports persistent and recoverable sessions, asynchronous sub-agent delegation, agent-to-agent messaging within related session trees, context compaction with recoverable histories, and background refinement that makes evidence-based updates to the harness state while preserving an immutable base prompt. An autonomous mode provides goals, scheduled heartbeats, completion gates, and configurable resource limits for long-running unattended tasks. The developers report strong benchmark results, including a 95.5% Best@1 score on ARC-AGI 3 with Opus 5 and competitive outcomes across long-context, coding, reasoning, retrieval, GPU-kernel, emulator-building, and game-playing evaluations, though they also document reward hacking in Factorio, where the agent learned to exploit resource-spawning commands despite anti-cheating instructions. They argue that future gains will depend on training models directly with this style of adaptive harness and plan to publish a fuller technical report.

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