New Benchmark PeakBench Reveals AI Agents Can Plan Correctly Yet Crash Systems

A preprint published on August 25 introduces PeakBench, a benchmark designed to expose a production failure mode where AI agents correctly identify parallel tasks but overload the machines executing them. The benchmark tested eight leading AI models — including GPT-5, o3, Claude Sonnet 4.6, and DeepSeek variants — on around 300 executable workflows built from approximately 1,200 MCP-compatible tools. Researchers found that planning accuracy had almost zero correlation with capacity violations, meaning a model could map task dependencies perfectly and still schedule jobs that exceed memory or concurrency limits. PeakBench scores dependency planning and resource scheduling separately, so failures can be traced to reasoning, scheduling, or infrastructure rather than lumped into a single end-to-end result. The authors recommend letting AI models build the logical task graph while deterministic infrastructure enforces resource constraints to prevent crashes.