Prove AI ROI with real numbers.
Every task in Spaces carries two time references — the actual agent-assisted duration and a manual baseline estimate. The ratio is your productivity multiple. Layer in cycle-time decomposition, iteration counts, and per-task LLM cost, and you have the data to answer 'are agents actually worth it?' with numbers, not intuition.
The full picture of what agents deliver
Speed alone does not tell you whether agents are worth the investment. Spaces measures six dimensions — productivity multiples, throughput, cycle time, iterations, wait time, and cost — so you can see where the leverage is real and where the bottlenecks remain.
Productivity multiple
Manual estimate / agent-assisted actual per task
Cycle time
Clock time from start to completion, by phase
Task throughput
Tasks completed per period, per team
Iteration count
Cycles per task — agents compress each one
Wait time
Time blocked or awaiting human review
LLM cost per task
Token spend attributed to each task
Cycle time, decomposed
Agent execution is the fast part — it is only a fraction of total cycle time. The real bottleneck is everything else: human review queues, approval gates, handoff delays. Spaces decomposes every task's cycle time by phase so you can identify what is actually slowing delivery down and fix the right problem.
Agent execution is the fast part. The real bottleneck is everything else — and you cannot fix what you cannot see.
Productivity trending up — or not?
Your productivity multiple — manual baseline / actual agent-assisted time — tells you how much leverage agents are delivering. Track it week over week to see whether adoption is accelerating, plateauing, or regressing. The trend line is the signal; a single number is noise.
More iterations before you ship
When each development cycle takes hours, you get one or two passes at a problem before the deadline. When AI compresses those cycles to minutes, you can iterate on the actual product — rethink the approach, refine the UX, harden edge cases — all before it ships. Spaces tracks iteration count alongside cycle time so you can see this compounding effect.
How much more is actually getting done?
Tasks completed per week is the simplest measure of team output. When agents start contributing, throughput should visibly increase — and if it does not, that is a signal to investigate workflow bottlenecks or adoption gaps. Spaces tracks throughput by team, workflow, and time period.
Where are tasks stalling?
A task can finish execution in an hour and still take a day to deliver — because it sat in a review queue, waited on a dependency, or needed an approval nobody noticed. Spaces tracks how long each task spends in wait states and breaks it down by reason, so you can see which handoffs and queues add hours without adding value.
Know what every task costs before the invoice arrives
Every AI coding session generates token spend. Your LLM provider shows you a monthly total. Spaces attributes that cost to the exact task, model, and workflow step that incurred it — in real time, as work happens. When a single task burns more than it should, you see it that day, not next month.
Org visibility
From individual tasks to org-wide trends — one dataset
Team-level adoption
See which teams are getting the most leverage from agents — and where additional workflow tuning could help.
Trend over time
Track whether productivity multiples are climbing, plateauing, or regressing as your org matures.
Period comparison
Compare this month to last month. This quarter to last quarter. Concrete before-and-after data.
Workflow analysis
Which workflow patterns produce the highest multiples? Double down on what works.
Shareable summaries
Export hours-saved, cost, and throughput data for investment reviews and planning.
Plan-level ROI
See productivity multiple and LLM cost for each project plan, not just in aggregate.
How productivity tracking works
Classify work
During planning, apply a workflow composed of agent-assisted and manual steps to a task.
Capture execution data
Time, iteration count, and LLM cost are recorded as work happens.
Calculate the multiple
Your manual baseline estimate ÷ actual agent-assisted time = the productivity multiple.
Aggregate and trend
Roll up from tasks → plans → teams → org. Compare across periods.
The questions your team is already asking
Spaces replaces guesswork with data.