AI can plan and complete long, multi-step work

Can it maintain task state over long horizons?

Tests whether the system preserves requirements, prior decisions, and unfinished work across extended workflows.

Long-horizon state retention is still brittle, especially once context becomes large and tasks branch.

Can it track dependencies and intermediate outputs?

Measures whether prerequisite work is respected and intermediate deliverables are handled in the right order.

Dependency tracking works on cleaner plans, but still misses hidden coupling and delayed prerequisites in larger workflows.

Can it re-plan after failure or changing conditions?

Tests whether the system updates its plan sensibly after delays, failed steps, or changing requirements.

Re-planning is often useful, but models still over-anchor on earlier structure when the task needs a deeper reset.

Can it deliver end results with limited supervision?

Measures whether the system can finish extended work with milestone-level oversight instead of dense step-by-step rescue.

Autonomous completion still drops sharply on real long-horizon work once priorities shift or errors compound.