• AIgileCoach is an open-source Jira intelligence platform that combines real-time dashboarding with AI-powered coaching across 21 dedicated agile views — from sprint planning to retrospectives, dependency tracking to compliance checks.
• Automatic urgency detection flags overdue, stale, blocked, and unassigned tickets before they become fires, giving teams a single glance at what needs attention now.
• Pluggable AI providers let you choose between Claude, OpenAI, Ollama (local), or Claude Code CLI — no vendor lock-in, and a mock provider for demos and testing.
• Multi-server and multi-team support means one deployment can serve an entire organization, connecting to multiple Jira instances with per-team color coding and project mappings.
• The project is actively under development — new features and bug fixes land regularly. AI capabilities are improving fast, so star the repo and stay tuned.

What Is AIgileCoach?

If you have ever stared at a Jira board and thought “I know the information is in here somewhere, but I have no idea what actually matters right now” — AIgileCoach was built for you.

At its core, AIgileCoach is a Next.js dashboard backed by an Express API that connects to your Jira instance and transforms raw issue data into structured, actionable views. But calling it a dashboard undersells it. It is closer to a full agile operating system — 21 purpose-built pages that cover every ceremony and metric an agile team needs, each with an embedded AI coaching panel that can analyze your data and surface insights on demand.

The tool groups issues by Epic, calculates real-time urgency flags (overdue, due soon, stale after 7 or 14 days, blocked, unassigned), and presents everything through a clean stats bar so you can jump straight to what needs your attention. No more hunting through filters. No more “let me check” during standup.

The 21 Views: One Tool, Every Ceremony

AIgileCoach is not a single dashboard — it is a toolkit. Here is what you get:

Day-to-day operations:

• Dashboard — Epic-based overview with urgency filtering (All / Critical / Overdue / Stale)
• Epic Board — Deep-dive into any epic with child issues, progress bars, and status breakdowns
• Hierarchy — Full issue tree from Epic down to Subtask
• Standup — Recent activity summary, ready to share on screen
• Backlog Refinement — Story estimation and grooming support

Planning and tracking:

• Sprint Goals — Define and track what the sprint is actually trying to achieve
• Planning — Sprint planning with capacity management
• PI Planning — Program Increment board for scaled agile teams
• PI Compliance — Track whether the PI is on course
• Gantt — Visual roadmap for longer-horizon planning

Analytics and flow:

• Analytics — Burndown charts, velocity trends, and custom metrics
• Flow — Cycle time distribution and cumulative flow diagrams
• Analyze — Deep-dive analysis with custom JQL queries

Team health and improvement:

• Sprint Review — Review completed work with the team
• Retro — Run retrospectives with voting, directly in the tool
• Health Check — Team health scoring through structured surveys

Governance and risk:

• Definition of Ready (DoR) — Checklist validation before stories enter a sprint
• ROAM Board — Risk management (Risks, Obstacles, Actions, Mitigations)
• Compliance — Project compliance and governance checks
• Dependencies — Cross-project dependency discovery and visualization
• Architecture — Technical dependency mapping

Every single one of these pages includes the AI Coach Panel — a sidebar where you can ask questions about the data you are looking at, get recommendations, or generate summaries.

AI Coaching: Your Agile Copilot

The AI integration in AIgileCoach works through a pluggable provider system built as a standalone library (ai-lib/). You pick your provider, configure an API key, and the coach is ready.

Five providers ship out of the box:

The AI coach builds context-aware prompts that include the current page data, the type of view you are on, and your question. It then returns structured insights: executive summaries, blocked ticket analysis, risk assessments, team workload distribution, and concrete recommendations.

For ticket-level analysis, the coach returns a tl;dr, status insight, required actions, risk level with reasoning, and staleness assessment. For board-level analysis, you get an executive summary, lists of blocked and stale tickets, workload distribution across the team, and prioritized recommendations.

Getting Started in Five Minutes

AIgileCoach runs with Docker Compose. Here is the setup:

1. Clone and configure:

git clone https://github.com/gruion/AIgile.git
cd AIgile
cp .env.example .env

2. Start everything:

docker compose up -d --build

This spins up four containers: the Next.js frontend (port 3010), the Express API (port 3011), a Jira instance (port 9080), and PostgreSQL.

3. Connect to Jira:

Open http://localhost:3010, log in with your Jira credentials (base URL, username, and API token), and you are in.

4. Seed sample data (optional):

cd api && npm install && npm run seed

This creates 5 epics with 33 realistic tickets — mixed statuses, due dates, comments, and assignments — so you can explore every feature without touching your production Jira.

5. Enable AI coaching:

Add your preferred provider to .env:

AI_PROVIDER=anthropic
ANTHROPIC_API_KEY=sk-ant-...

Restart the API container, and the AI Coach Panel lights up across all 21 views.

Multi-Server, Multi-Team: Built for the Enterprise

One of AIgileCoach’s standout features is its multi-tenancy architecture. Through environment variables or the in-app configuration panel, you can:

• Connect multiple Jira instances — useful for organizations running separate Jira servers per division or for consulting teams managing multiple clients.
• Define teams with custom colors, project mappings, and server associations — the dashboard visually distinguishes work across teams.
• Configure Program Increments with start/end dates, sprint counts, and duration — enabling SAFe-style PI tracking across multiple teams and projects.
• Save JQL bookmarks for frequently used queries, shared across the team.

Configuration persists to a config.json file, but every setting can also be driven through environment variables — making it straightforward to manage through Kubernetes ConfigMaps or CI/CD pipelines.

Current Status: Actively Under Development

AIgileCoach is not production-ready yet — and that is worth being upfront about. The project is in active development with new features and bug fixes shipping regularly. Here is what to expect:

• The core dashboard and agile views are functional and already useful for day-to-day team work.
• AI coaching features are still maturing — prompt quality, response parsing, and provider-specific tuning are all areas seeing rapid improvement.
• Bug fixes land frequently as the tool gets tested across different Jira configurations, project structures, and team sizes.
• Kubernetes deployment manifests (GKE and OpenShift) are included but should be treated as starting points, not battle-tested production configs.

The architecture is stateless by design — session data lives in memory with 24-hour expiration, configuration in a mounted volume, and all Jira data is fetched in real-time. The foundation is solid, and the pace of progress is fast.

Star the repo on GitHub to follow along: github.com/gruion/AIgile

Why This Matters

Most Jira dashboards show you data. AIgileCoach interprets it. The combination of automatic urgency detection, structured agile views, and AI-powered coaching means teams spend less time navigating Jira and more time acting on what they find.

Whether you are a Scrum Master running daily standups, a Release Train Engineer tracking PI compliance, or a Tech Lead trying to spot blocked dependencies before they cascade — AIgileCoach gives you the view you need with the intelligence layer to make sense of it.

The pluggable AI architecture also means you are never locked into a single vendor. Start with the mock provider for evaluation, move to Ollama for air-gapped environments, or plug in Claude or GPT-4o for maximum capability. The interface stays the same.

This is a project worth watching. A lot of progress is underway, and the roadmap is ambitious. If you want to try it, contribute, or just keep an eye on where it is heading — now is a great time to get involved.

Sources

• AIgileCoach on GitHub

Want help deploying AIgileCoach for your team, or need a fractional DevOps engineer to integrate AI-powered tooling into your agile workflow? Talk to Gruion.