Full sprint lifecycle from planning to completion with task carryover. Burndown charts, velocity tracking, AI-powered workload balancing, and sprint completion prediction all in one place.
TARO handles every stage of the sprint lifecycle from pulling work out of the backlog to closing the sprint and carrying unfinished tasks forward automatically.
Plan
TARO's sprint planning pulls directly from your backlog already ranked by Auto Prioritization. Set the sprint name, goal, start and end dates, then drag tasks from the backlog into the sprint. TARO shows capacity signals for each team member as you add tasks, so you commit to a sprint that reflects what the team can actually deliver not an optimistic stack that collapses by day 3.
Track
TARO generates a live burndown chart for every active sprint comparing ideal completion rate against actual task completion in real time. Alongside the burndown, TARO tracks velocity across all completed sprints: how many story points or tasks your team delivers per sprint, and whether that number is stable, improving, or declining.
AI
TARO's AI runs two operations continuously during an active sprint: workload balancing and completion prediction. Workload balancing watches team capacity and surfaces reassignment suggestions when one member is overloaded. Completion prediction uses current velocity, remaining tasks, and known blockers to predict whether the sprint closes on time with a confidence level shown against the commitment.
Close
When you close a sprint, TARO presents every unfinished task with three options: carry over to the next sprint, move to the backlog, or cancel. Tasks marked for carryover automatically appear at the top of the backlog for the next sprint planning session prioritised above new work. The closed sprint's data is preserved in full for retrospectives, velocity tracking, and historical comparison across the sprint history.
Sprint planning that uses real velocity data, burndown charts that update live, and an AI that tells you on day 3 whether the sprint closes on time not on day 9.
TARO's burndown chart recalculates the moment any task status changes. The ideal line and actual line stay in sync with real work not a static chart generated at sprint start that nobody looks at again.
When TARO's sprint planning panel tells you your team averages 26 tasks per sprint, adding 38 tasks to the commitment is a visible choice with visible consequences not an optimistic guess made in the abstract at 9am on a Monday.
LTARO's AI predicts sprint completion at any point during the sprint not just at the end. Seeing an 87% confidence prediction on day 3 gives you 7 days to act. Seeing a 42% confidence on day 8 gives you one.
Sprint planning balances the work at the start. TARO's AI watches the sprint in real time and surfaces rebalancing suggestions as things shift new tasks added, people blocked, velocity dropping so the distribution stays healthy all the way through.
Every unfinished task at sprint close gets an explicit decision: carry forward, backlog, or cancel. Nothing silently disappears. Nothing accidentally gets committed to the next sprint without review. The carryover list is presented before the sprint closes so the team sees exactly what didn't make it.
Every closed sprint retains its full data burndown shape, velocity, completion rate, carryover count, blocker history. Retrospectives stop being based on memory and start being based on the actual numbers from the sprint that just closed.
TARO's burndown chart recalculates the moment any task status changes. The ideal line and actual line stay in sync with real work not a static chart generated at sprint start that nobody looks at again.
When TARO's sprint planning panel tells you your team averages 26 tasks per sprint, adding 38 tasks to the commitment is a visible choice with visible consequences not an optimistic guess made in the abstract at 9am on a Monday.
LTARO's AI predicts sprint completion at any point during the sprint not just at the end. Seeing an 87% confidence prediction on day 3 gives you 7 days to act. Seeing a 42% confidence on day 8 gives you one.
Sprint planning balances the work at the start. TARO's AI watches the sprint in real time and surfaces rebalancing suggestions as things shift new tasks added, people blocked, velocity dropping so the distribution stays healthy all the way through.
Every unfinished task at sprint close gets an explicit decision: carry forward, backlog, or cancel. Nothing silently disappears. Nothing accidentally gets committed to the next sprint without review. The carryover list is presented before the sprint closes so the team sees exactly what didn't make it.
Every closed sprint retains its full data burndown shape, velocity, completion rate, carryover count, blocker history. Retrospectives stop being based on memory and start being based on the actual numbers from the sprint that just closed.
Set up a sprint in minutes. TARO tracks every task from commit to close.
Engineering leads, scrum masters, and product managers all use TARO Sprint & Agile for the same reason the sprint should reflect what the team can actually deliver, not what was optimistically committed to at 9am on planning day. Burndown charts, velocity history, and AI prediction make the difference visible before it's too late to change it.
800+
trusted teams
Sprint lifecycle stages
AI prediction accuracy
Earlier overcommitment detection
Sprint history preserved
Engineering teams use TARO's sprint planning panel with the last 5-sprint velocity average displayed alongside the current backlog. When the lead tries to commit 38 tasks and the team's average is 26, TARO's capacity recommendation makes the overcommitment visible before the sprint starts not evident on day 7 when the burndown is already off track.
TARO's intelligence runs beneath every sprint predicting risks, flagging bottlenecks, and ranking the backlog before planning even starts.
Drop your backlog. TARO re-orders every task with AI reasoning based on due dates, dependencies, and strategic impact. Sprint planning starts with a ranked list not a blank canvas.
Scans for overdue tasks, stalled workflows, and blocked dependencies surfaces exact action recommendations before risks become sprint-ending incidents.
Identifies which pipeline stages have too many in progress tasks, who's a single point of failure, and which handoffs are breaking down with prescribed fixes for each.
Predicts your project's actual finish date accounting for velocity, blockers, and sprint history. Predicted date, variance, and confidence level before you commit.
Drop your backlog. TARO re-orders every task with AI reasoning based on due dates, dependencies, and strategic impact. Sprint planning starts with a ranked list not a blank canvas.
Scans for overdue tasks, stalled workflows, and blocked dependencies surfaces exact action recommendations before risks become sprint-ending incidents.
Identifies which pipeline stages have too many in progress tasks, who's a single point of failure, and which handoffs are breaking down with prescribed fixes for each.
Predicts your project's actual finish date accounting for velocity, blockers, and sprint history. Predicted date, variance, and confidence level before you commit.
Common questions from engineering leads, scrum masters, and PMs evaluating TARO's sprint management.
TARO tracks velocity as the number of tasks (or story points, if used) completed per sprint, recorded automatically when a sprint is closed. Velocity is calculated per team, per project, and per individual team member. The velocity dashboard shows a sprint-by-sprint trend line so you can see whether the team is consistently delivering, improving, or declining over time. Velocity data is used in two places: sprint planning (TARO shows the historical average and recommends a commitment range for the new sprint) and completion prediction (current-sprint velocity is compared against remaining tasks to predict the close date). Velocity is reset to zero at each sprint start and builds as tasks complete during the sprint.
Velocity data, burndown charts, AI predictions. Run the sprint the way the numbers say it should go.