SAFARI: Scaling Long Horizon Agentic Fault Attribution via Active Investigation

SAFARI (Scaling long-horizon Agentic Fault AttRibution via active Investigation) is a new framework designed to enhance fault attribution in autonomous agents by utilizing a tool-augmented diagnostic loop, which allows for reading and searching trajectory segments alongside a persistent Short-Term Memory (STM). This approach decouples diagnostic accuracy from the limitations of LLM context windows, achieving a 20% improvement on the Who&When dataset and a 19% improvement on the TRAIL GAIA subset within specified token budgets. SAFARI maintains a precision of 0.58 even when diagnosing faults located 5x beyond the model's native context window, addressing a critical challenge in multi-step, multi-agent task execution.