New Microsoft paper. Long agent runs expose failures that short benchmarks miss. Agents can look reliable at 2 or 4 steps and fall apart by 16.

every agent step has some chance of going wrong, and those small errors compound as the workflow gets longer.

Across 9 models, success usually dropped as the number of dependent steps increased.

On ToolQA, models that were near-perfect on short runs fell to just 0-33% success by 16 steps.

Long context was not the main driver: shortening the context made the decline worse, so blindly trimming history is not a reliability fix.

For builders, the recommendation is straightforward: stop treating a benchmark pass rate as proof that an agent is production-ready.

Test agents at the workflow lengths you actually expect, measure per-step reliability, and add checks or checkpoints before a bad step poisons everything that follows.