Sprint planning that’s a review, not a half-day marathon

It's an AI agent that takes the grind out of sprint planning. It continuously grooms your backlog, proposes a capacity-aware sprint scope based on real velocity, drafts estimates from how comparable past work went, shapes rough items into well-formed stories with acceptance criteria, and once the team agrees, sets the sprint up in your tool. Instead of a half-day meeting spent building a plan from scratch, the team walks in to a strong draft and refines it — faster, more realistic, and grounded in your actual history.

No — it proposes, the team decides. The agent prepares a groomed backlog, a suggested capacity-aware scope and draft estimates, but every decision stays with the team: what to commit to, how to size it, and what the sprint goal is. It gives you a strong starting point to review and adjust; it never imposes a plan or commits the team to anything on its own.

It scopes on reality, not hope — factoring in the team's historical velocity and true capacity for the sprint, including leave, meetings, support duty and part-time availability. That produces a realistic, achievable scope, so the team stops over-committing and missing or sandbagging and under-delivering. And because capacity is a guide rather than a quota, it's used to protect the team from over-loading, not to squeeze more out of it.

It drafts estimates by looking at how comparable past work actually played out, giving the team a grounded starting point to adjust rather than debating every item from zero. That makes estimation faster and more consistent, and anchors it in real history instead of gut feel. The team always has the final say on sizing — the agent just removes the blank-page debate.

No, and we're deliberate about this. Velocity here is a team signal used to make planning realistic — never a personal scorecard. The agent plans the work and the team's overall throughput; it doesn't rate individuals, compare their output or feed performance reviews. Using velocity to pressure or rank people breaks agile and trust, so we build against it.

Yes. Rather than leaving grooming as a painful pre-planning scramble, the agent works continuously — clarifying vague tickets, flagging duplicates and stale items, filling gaps and keeping priority current. So there's always a clean, ready backlog to plan from, and planning isn't spent untangling a mess first.

Yes. It adapts to how your team runs agile — Scrum sprints, Kanban flow or a hybrid — and to your ceremonies and definition of done. It integrates with Jira, Azure DevOps, Linear, ClickUp, Asana and Monday, plus dev tools like GitHub and GitLab for history and velocity. In-house or custom tools integrate via their API.

Both. Once the team agrees the plan, the agent sets the sprint up in your tool — moving items in, applying assignments, tags and dependency links — so the moment planning ends, the sprint is ready to start with none of the manual clicking that usually follows. You get the plan and the setup, not just advice.

It tracks velocity, estimate accuracy, scope changes and commitment-versus-delivery across sprints, and feeds that back into future scoping and estimates. So instead of repeating the same misses, the team's forecasting gets measurably more accurate over time, and planning becomes more reliable sprint after sprint.

A sprint planning agent for one team can typically pilot across a couple of sprints — we set up grooming, the capacity model and estimation, integrate your tools, then tune against real planning sessions. Cost depends on team count, integrations and complexity; we usually start fixed-scope so you see the value quickly, then expand. The initial assessment call is free.