This epic introduces an AI-driven troubleshooting layer into the benchmark execution process. Today, when benchmarks complete, the determination of whether a run truly failed, partially failed, or simply requires a rerun is largely manual. Engineers must inspect logs, correlate signals, and reason about infrastructure, configuration, and model constraints.

In practice, benchmark failures can be caused by a wide range of factors such as unsupported images, insufficient GPU memory, communication issues, temporary lack of resources, or misaligned configurations. In many cases, a reported “benchmark failure” does not indicate a real issue with the benchmark itself and can be resolved by a simple rerun, a different image, or a configuration adjustment. In other cases, the failure is legitimate, for example when a model is too large for the target GPU.

The goal of this epic is to introduce an AI agent that automatically performs an initial investigation once a benchmark completes. The agent analyzes logs, signals, and execution metadata to infer the real status of the benchmark and classify the failure accordingly.

Today, benchmark troubleshooting and validation is a fully manual process. For each model, engineers spend approximately four hours analyzing benchmark results, inspecting logs, and determining whether a failure is actionable, retryable, or fundamentally invalid. This process does not scale and creates significant operational overhead as benchmark volume grows.

Target State:{}

An AI agent automatically performs first-level root cause analysis when benchmarks complete. The agent classifies failures into meaningful categories such as retryable issues, configuration mismatches, infrastructure or resource limitations, or genuine benchmark invalidity. Where possible, the agent can also recommend corrective actions, such as rerunning the benchmark with a different image or configuration.

The target outcome is to reduce manual troubleshooting time by at least 90%, shifting engineers from log-level investigation to final validation and decision-making.

Outcome / Value:{}

Dramatically reduced benchmark turnaround time, consistent and repeatable failure classification, faster iteration cycles, and improved scalability of the benchmarking process as volume and model complexity continue to grow.