OrchestrAI

OrchestrAI is a transparent, multi-agent AI system that automates Jira planning, progress tracking, risk analysis and reporting

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Inspiration

Software teams often lose 5–10 hours per week on repetitive project management tasks: breaking down epics, creating Jira tickets, tracking progress, and writing reports. While working with Jira, I repeatedly noticed how much time is spent on coordination instead of actual development.

I asked myself: what if AI agents could automate planning, tracking, and reporting — transparently and reliably? This question inspired OrchestrAI - a multi-agent system designed to reduce friction and save time in Jira-based workflows.

What I Learned

  • How to design a multi-agent architecture with clearly separated responsibilities.
  • Why transparent AI reasoning is critical for trust and evaluation - every decision must be explainable.
  • How to integrate Groq LLM for fast and cost-efficient inference.
  • How to orchestrate workflows using n8n and connect them with Jira Cloud.
  • How containerized agents can be scaled and deployed independently using Docker Compose.

How I Built It

  • FastAPI microservices for each agent (Planner, Progress, Risks, Digest).
  • Groq LLM (Llama 3.3 70B) for task decomposition, analysis, and summaries.
  • Jira Cloud API for automated ticket creation and progress tracking.
  • n8n workflows for automation triggers and orchestration.
  • Docker Compose for reproducible, self-hosted deployment.
  • MCP protocol for consistent and extensible agent communication.

Each agent exposes its own API endpoint and health check, making the system modular and resilient.

Challenges I Faced

  • Jira API authentication — handling tokens securely without exposing secrets in logs or repositories.
  • Latency management — balancing LLM inference time with external API calls.
  • Workflow reliability — ensuring n8n triggers worked consistently across containers.
  • Reasoning transparency — structuring agent reasoning so it is visible without leaking sensitive prompts.
  • Time constraints — delivering a stable, production-style demo within a short hackathon timeframe.

Fun with Math

I also estimated the potential time savings using a simple formula:

[ T_{saved} = (H_{manual} - H_{AI}) \cdot N_{teams} ]

Where:

  • (H_{manual}) = average manual hours per week (≈ 8)
  • (H_{AI}) = average hours with OrchestrAI (≈ 2)
  • (N_{teams}) = number of teams using the system

For just 5 teams:

[ T_{saved} = (8 - 2) \cdot 5 = 30 \text{ hours/week} ]

That’s nearly a full work week saved across teams.

Conclusion

OrchestrAI is more than a hackathon demo — it is a practical exploration of AI-powered project management. By combining multiple specialized agents, automation, and transparent reasoning, the system helps teams focus on building software instead of managing tickets.

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