Raven: The Harness of Harnesses for Composable Agentic Intelligence
As large language models advance, AI agents are moving beyond isolated, domain-specific tasks toward long-horizon, cross-domain workflows. This transition exposes two challenges: increasing harness complexity makes manual design difficult to scale, while tighter coupling to specific domains limits the generality of a single harness. The central question thus shifts from how to
Reading in Plain English mode — toggle below to switch
As large language models advance, AI agents are moving beyond isolated, domain-specific tasks toward long-horizon, cross-domain workflows. This transition exposes two challenges: increasing harness complexity makes manual design difficult to scale, while tighter coupling to specific domains limits the generality of a single harness. The central question thus shifts from how to
This work addresses the critical challenge of scaling and generalizing AI agents by introducing a framework for autonomously constructing and evolving specialized harnesses. For ML engineers, understanding such multi-agent ecosystems is crucial for building robust, adaptable, and composable AI solutions that can tackle complex, cross-domain problems.