Disarray agents manage the end-to-end research loop: translating high-level goals, finding relevant data, forming hypotheses, writing and running code, interpreting partial or failed results, and deciding what to try next. 

Disarray tackles two key challenges in AI research agents: generating stronger hypotheses for efficient experimentation and avoiding the failure modes that derail long-running agent loops. A key insight in Disarray’s approach is that success comes from better context, not more context. Disarray’s core differentiator is a context graph that captures rich lineage across data, code, execution history, and documentation, enabling precise retrieval of the most relevant information and multi-hop analysis that reveals novel insights from prior work. By giving agents more targeted context, higher-order understanding, and intelligent harnesses, Disarray agents form better hypotheses, stay focused over time, and ultimately deliver breakthrough results rapidly in long-horizon research.