Inspiration
In Formula 1, the difference between the podium and the back of the grid is often decided in the pit lane. A 2-second pit stop requires absolute synchronization, real-time data, and instant decision-making.
As developers, our "pit stops" are incidents. When production breaks, the clock starts ticking. Every second of downtime costs money and trust. Yet, most incident management feels like a disorganized pit stop: looking for tools, scrambling for logs, and confused communication.
We asked ourselves: What if we could give DevOps teams a Williams Racing-grade Race Engineer?
Inspired by the precision of F1 telemetry and the Codegeist theme, we built PitCrew AI. It transforms Jira Service Management from a static ticketing system into a high-performance race control center.
What it does
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🏎️ Inspiration: Speed, Precision, and the Pit Lane In Formula 1, the difference between the podium and the back of the grid is often decided in the pit lane. A 2-second pit stop requires absolute synchronization, real-time data, and instant decision-making.
As developers, our "pit stops" are incidents. When production breaks, the clock starts ticking. Every second of downtime costs money and trust. Yet, most incident management feels like a disorganized pit stop: looking for tools, scrambling for logs, and confused communication.
We asked ourselves: What if we could give DevOps teams a Williams Racing-grade Race Engineer?
Inspired by the precision of F1 telemetry and the Codegeist theme, we built PitCrew AI. It transforms Jira Service Management from a static ticketing system into a high-performance race control center.
🏁 What it does PitCrew AI is a Forge app that integrates Jira, Bitbucket, and Confluence to reduce Mean Time To Resolution (MTTR).
The Rovo Race Engineer (AI Agent): Using the new rovo:agent, PitCrew acts as an intelligent teammate. When a high-priority bug is reported, it scans the issue description and error logs, correlates the error timestamp with recent commits in Bitbucket, and posts a strategic comment identifying the likely culprit.
The Telemetry Dashboard: We replaced standard SLA text fields with a visual, F1-inspired dashboard using React and Tailwind CSS. It visualizes "Lap Time" (SLA countdown) and "Track Conditions" (Service Health).
Post-Race Analysis: In F1, the race isn't over until the debrief is done. When a ticket is closed, PitCrew AI automatically generates a Confluence page with a root cause analysis and timeline.
How we built it
We built this purely on the Atlassian Forge platform to ensure security and seamless integration.Frontend: We used React coupled with Tailwind CSS to create a "Dark Mode" telemetry dashboard. The UI needed to feel fast and urgent, unlike standard form inputs.Backend: We utilized Forge Functions (Node.js) to handle the business logic and API orchestration.AI & Rovo: We utilized the rovo:agent module. The prompt engineering was critical here—we had to "teach" the agent to think like a Site Reliability Engineer (SRE).The Math: To quantify team performance during an outage, we implemented a custom algorithm to calculate Incident Velocity ($V_{inc}$) based on status changes and comment density. We modeled this as:
$$V_{inc} = \frac{\Delta Status + \sum_{i=1}^{n} (Comments_i \cdot w_{relevance})}{\Delta t_{elapsed}}$$
Where $w_{relevance}$ is a weight assigned by the Rovo AI based on how helpful a specific comment was to the resolution.Rovo Dev Bonus: We utilized Rovo for Developers during the build! We used the Rovo chat in the IDE to generate the boilerplate manifest for the custom UI bridge, significantly speeding up our initial setup.
Challenges we ran into
Context Windows: Passing extensive log files to the Rovo Agent sometimes hit token limits. We had to write a pre-processor to truncate logs and only feed the "Error" stack traces to the AI.
Asynchronous State: Syncing the "Live Pit Crew" (who is looking at the ticket) required mastering the Forge Storage API to handle concurrent reads/writes without race conditions.
Accomplishments that we're proud of
Successfully building a functional Rovo Agent that provides genuinely useful debugging context, not just generic advice.
Designing a UI that breaks the mold of standard Jira apps—it looks like a cockpit, not a spreadsheet.
Getting the Bitbucket API to talk to Jira seamlessly to pinpoint specific commits.
What's next for PitCrew AI
Predictive Tyre Wear: Using machine learning to predict incidents before they happen based on deployment frequency.
Radio Check: Integrating with Slack to broadcast "Box Box Box" alerts to specific developers when their code causes a regression.
Built With
- atlassian-forge
- javascript
- jira-api
- node.js
- openai
- react
- tailwind-css
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