Inspiration

Production LLM agents fail silently when infrastructure breaks. A Claude API brownout, an MCP server crash — users get blank screens. There's no graceful story. I wanted to build one.

What it does

SentinelAgent is a resilient multi-agent system with a 3-tier fallback chain: Claude → OpenAI → Local BiLSTM (4.9M params, 77.31% accuracy, 1.6M tweets)

When Claude fails, it automatically routes to OpenAI. When ALL cloud LLMs fail, my own PyTorch BiLSTM model serves sentiment analysis locally with zero API dependency. A live dashboard shows provider health, latency, error rates, and a real-time request log. Chaos injection buttons let you kill any provider mid-demo to watch the fallback activate instantly.

How we built it

  • Flask API with /chat, /status, and /chaos endpoints
  • Health monitor with circuit breaker logic per provider
  • TrueFoundry AI Gateway for multi-LLM routing
  • PyTorch BiLSTM model as offline fallback
  • MCP client for tool augmentation (skipped gracefully when down)
  • Vanilla JS dashboard with 3-second status polling

Challenges we ran into

  • Making BiLSTM load conditionally so the app runs without torch installed
  • Circuit breaker recovery logic — knowing when to retry a dead provider
  • MCP failing silently vs loudly — had to distinguish connection errors

Accomplishments that we're proud of

  • A real trained ML model as fallback, not a mock
  • The chaos demo is visually compelling — judges can break it live
  • Built in one night from scratch, fully working

What we learned

  • How to design agentic systems that degrade gracefully
  • Circuit breaker patterns applied to LLM infrastructure
  • TrueFoundry AI Gateway routing and observability

What's next for SentinelAgent

  • Full TrueFoundry Gateway integration for observability dashboard
  • Streaming responses
  • Deploy on AWS EC2 with public URL

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