Raycaster
Public benchmark of real AI agent runs on business tasks, with cost, tokens and rubric scores.
Raycaster Eval publishes actual agent runs on real business tasks — reviewing warranty claims, editing a consulting slide deck, building merger-model sensitivities — and shows the full assignment, every agent step, the output file and the rubric result, not just a summary score. Each run lists the model and harness used, the exact run cost and input token count, and the specific rubric checks it passed, like stating a CPAM value to the cent. Some runs open into a live, read-only workspace so you can inspect the agent's actual output file yourself.

What holds up
- +Publishes the full agent trace and output file per run, not just a pass/fail score
- +Shows exact run cost and input token count per task — for example $3.64 and 2.59M tokens for one deck edit
- +Lets you open a live, read-only workspace for a run to inspect the actual output file yourself
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