SentryMesh AI Update: Autonomous Incident Response Engine is Live!
We have reached a major project milestone for SentryMesh AI as we finalize our submission for the Nebius x NVIDIA Global AI Hackathon! SentryMesh AI is an autonomous, multi-agent incident response and code self-healing engine designed to handle the entire lifecycle of cloud infrastructure failures.
Here is a look at what we've built, how the engine evolved, and what powers it under the hood:
How SentryMesh AI Works Instead of relying on human on-call engineers to manually comb through log dumps during an outage, SentryMesh AI orchestrates a two-stage agentic workflow:
Stage 1: Autonomous Triage (NVIDIA Nemotron-3 Nano) When telemetry spikes or error logs are ingested, the Triage Agent parses the high-throughput stack traces in real time to isolate anomaly signatures and rate incident severity.
Stage 2: Code Self-Healing & Remediation (NVIDIA Nemotron-3 Ultra) The Remediation Agent evaluates system context from Stage 1, performs root-cause analysis, and synthesizes production-ready code patches along with a verification test suite.
Key Technical Features & Deployment Updates FastAPI Asynchronous Core Engine: Engineered high-throughput REST endpoints (server/main.py) to manage asynchronous agent handoffs and structured JSON state streams.
Streamlit Mission Control UI: Built an interactive operational dashboard featuring live log stream simulations, target microservice selectors (e.g., payment-processor), real-time agent output visualizations, and manual patch overrides.
Nebius Token Factory Integration: Powered entirely by NVIDIA Nemotron-3 models hosted on Nebius Token Factory for ultra-low latency inference and structured schema adherence.
Open Source Compliance: Licensed under Apache 2.0 with full setup instructions, Docker support, and containerized runtime definitions.
Code Architecture Highlight: Two-Stage Agent Handoff
Here is a snippet showing how our FastAPI engine routes telemetry from Nemotron-3 Nano (Triage) directly into Nemotron-3 Ultra (Remediation):
@app.post("/api/v1/incident/dispatch")
async def dispatch_sentrymesh_agents(telemetry: TelemetryPayload):
# Stage 1: Rapid Anomaly Triage via Nemotron-3 Nano
triage_result = await triage_agent.analyze(
model="nvidia/nemotron-3-nano",
logs=telemetry.raw_logs,
service=telemetry.service_name
)
# Stage 2: Deep Context Remediation & Patch Generation via Nemotron-3 Ultra
remediation_patch = await remediation_agent.synthesize_patch(
model="nvidia/nemotron-3-ultra",
anomaly_signature=triage_result.signature,
stack_trace=triage_result.stack_trace,
system_context=telemetry.context
)
return {
"status": "resolved",
"triage_summary": triage_result,
"code_patch": remediation_patch.diff,
"verification_tests": remediation_patch.test_suite
}
Explore & Test SentryMesh AI Live Interactive App: SentryMesh AI Streamlit Mission Control
GitHub Repository: github.com/Masngo/sentrymesh-ai
Drop a comment below with your feedback or thoughts on automated SRE workflows!
Log in or sign up for Devpost to join the conversation.