Project
AI Workflow Orchestrator
Production-grade multi-agent system that autonomously triages logs, tickets, and emails, executes API actions, and replans when conditions change.
PythonFastAPIRedisCeleryPostgreSQLDockerAWS ECS
AI Workflow Orchestrator is a production-grade multi-agent system built to autonomously triage logs, tickets, and emails, execute downstream actions through APIs, and dynamically replan when operating conditions change.
Key results
- Achieved 96.7% successful task completion across production-like scenarios.
- Reduced manual incident triage effort through end-to-end automation.
- Maintained approximately 1.9 seconds average response latency with distributed execution.
What I built
- Designed a multi-agent execution loop that moves through classify, plan, execute, and replan stages with real-time decision updates.
- Implemented fault-tolerant pipelines using Celery workers, retries, and dead-letter queues.
- Built multi-model routing to balance cost and performance across LLM calls.
- Added human-in-the-loop escalation for low-confidence decisions.
Why this matters
Most AI systems stop at summarization. This system is built to execute decisions end-to-end under uncertainty, which is closer to the level of reliability and autonomy required for real operational leverage.