AI finds the fastest path
through your plan.
Most plans are written as a straight line — one step after another, even when the work isn’t truly dependent. Spaces reads the whole dependency graph and re-wires it: parallel paths, decomposed tasks, tighter ordering. Same scope, shorter critical path — and you review every change before it takes effect.
Optimization is a graph rewrite — the same work, re-ordered to run as wide as its real dependencies allow.
Plans — AI-generated or hand-authored — default to a straight line. Tasks are stacked sequentially even when nothing forces the order, and large tasks blur across service boundaries. The critical path bloats with false constraints.
Spaces reads the full graph and proposes parallel paths, decomposition, and tighter ordering — without touching scope. You stay in the loop: review every diff, accept what helps, ignore the rest. AI finds the path; you choose whether to take it.
Optimization isn’t a black box.
Every suggestion is a specific, reviewable change to your plan — never a silent rewrite. Pick any operation to watch exactly how it rewires the graph before anything executes.
Parallel Path Discovery
The AI builds a topological view of your graph and surfaces tasks that look sequential but are not truly dependent. False constraints come off — real parallelism shows up — without changing what ships.
Reversible — right up until execution
Every optimization pass writes a versioned snapshot. Before execution begins, compare any two versions, see exactly what changed, and roll back in one click. Once work starts, those versions stay as auditable history — and when scope shifts, you re-optimize without losing the trail.