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OrchestrAI Architecture
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.
