Designing AI for Long-Running Tasks: Looking Beyond More Context

Model capability is moving in that direction quickly. In its February – March 2026 Frontier Risk Report, METR estimated how long a software task could take before a frontier model’s chance of completing it successfully fell to 50%. Claude Opus 4.6 reached about 17.8 hours on cleaner tasks but only 6.4 hours on messier ones involving changing environments, constrained resources, expensive feedback, or mistakes that could not easily be undone.

The difference suggests that task length alone is not the main challenge. Long-running work also requires the system to preserve progress, adapt when conditions change, and recover when earlier assumptions no longer hold.