Case study · AI-assisted sprint planning
Sprint planning in 25 minutes: a Costa Mesa fintech's predictive-velocity agent
A 60-engineer fintech in Costa Mesa was burning a half-day every two weeks on sprint planning, with story-point estimates that drifted 30–40% from actuals. Implement Agentic built a thin LangGraph agent over Jira + Confluence that grooms the backlog, estimates points against historical velocity, flags risk, and drafts release notes — all as a Jira draft a human approves.
- 4.5h → 25min
- Planning ceremony time
- −68%
- Estimate-to-actual drift
- +22%
- On-time release rate
- 8 wks
- Pilot to production
The challenge
- Sprint planning consumed 4–5 hours every two weeks across 6 squads.
- Story-point estimates drifted 30–40% from actual completion; release commitments slipped quarterly.
- Customer requirements from sales calls rarely made it into backlog grooming on time.
The stack
- LangGraph
- Anthropic Claude Sonnet
- Jira MCP server
- GitHub MCP server
- pgvector
- Granola
- Langfuse
The closed loop, applied
Capture
MCP servers ingested Jira, Confluence, GitHub PRs and Granola transcripts of customer calls into a unified context layer.
Normalize
Past tickets enriched with actual cycle time, reopen rate, and squad ownership; transcripts entity-tagged for product surface.
Memory
A predictive-velocity model per squad calibrated story points against 18 months of completion history.
Reason
LangGraph sub-agents handled backlog grooming, point estimation, risk flagging, and release-note generation.
Act
Output landed as a draft sprint in Jira — never auto-committed. PMs approved or edited in their normal flow.
Evaluate
Langfuse compared estimated vs actual points each sprint; deltas fed back into the velocity model weekly.
"We stopped arguing about story points. The agent shows us what last quarter's data says, and we spend planning on the actually hard tradeoffs."
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