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Research and benchmarks

Reliabilitybench

ReliabilityBench (arXiv:2601.06112) is a benchmark for tool-using agents that measures reliability instead of a single-run success rate. It varies three things and reports all three: how often the same task succeeds on every repeat, how much success drops when the task is reworded without changing its meaning, and how much it drops when the tools underneath start failing.

The first axis is consistency. Instead of asking whether the agent can complete a task once, it runs the same task several times and asks whether it completed every time. An agent that succeeds on most runs still fails some users, and in production each user only gets one run. So the score that matters is the one where every attempt has to pass.

The second axis rewords the task. Same request, different phrasing, and the agent should behave the same. It didn't: at the heavier rewording level, success fell from 96.9% to 88.1%. The third axis breaks the tools on purpose, with timeouts, rate limits, incomplete responses, and changed response formats. Rate limits caused the largest drop. In all three, the agent is judged on whether the job actually got done, not on what it said, so a reworded task still counts as the same task.

Two caveats before citing it. It's a single-author preprint, not a multi-lab benchmark, and it covers two models over 1,280 episodes. The measurement design is worth borrowing; the specific numbers are early.

9 questions

Answered, plainly.

What is ReliabilityBench?ReliabilityBench scores tool-using agents on consistency across repeats, robustness to reworded tasks, and tolerance for failing tools, not one run's success.answer →How do you measure whether an agent succeeds consistently, not just once?Run the same task repeatedly and score whether every run passes, not whether any one does; ReliabilityBench calls this metric pass^k.answer →How do you test an agent against tool failures?Inject the actual failures a production tool throws, timeouts, rate limits, and partial responses, and measure the drop under each; rate limits hurt most.answer →What is fault injection for AI agents?Deliberately breaking a tool an agent depends on, timeouts, rate limits, malformed responses, during a test, to measure how much the agent's success drops.answer →Why does an agent that passes a benchmark still fail in production?Because a benchmark score is usually a best case: one clean try, or a few, against tools that behave; production repeats the task against tools that don't.answer →Does a light rewording of a task hurt an agent as much as a heavy one?Yes, roughly: ReliabilityBench's own numbers show nearly the whole drop happens at the lightest rewording level, and getting heavier barely moves the score again.answer →Does an agent that falls for an injected fault in a benchmark ignore available evidence?Not necessarily: in one study, of the runs where contradicting evidence eventually appeared, only 17 of 44 had it arrive before the agent already acted on the fault.answer →Does a more expensive model make an agent more reliable?Not in ReliabilityBench: GPT-4o cost 82 times what Gemini 2.0 Flash did and scored 90.42% on consistency against Gemini's 91.04%.answer →Does adding self-reflection to an agent make it more reliable?Not in ReliabilityBench: a plain ReAct agent beat the same agent with Reflexion self-reflection added, on reworded tasks and when its tools failed.answer →

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