Pulse AI Watchdog
Autonomous Self-Healing Cloud Watchdog with WhatsApp Incident Routing
Role: Lead Architect•Timeline: 2025 - 2026•Category: Autonomous AI & Observability
Mean Time to Detect (MTTD)
1.4 sec
-90% vs legacy Prometheus
Autonomous Resolution Rate
76%
Zero human intervention required
False Alarm Reduction
94%
Contextual AI verification
Incident Escalation Speed
1.2 sec
Instant WhatsApp PUSH dispatch
System Specifications
Architecture Pattern
Autonomous SRE Feedback Loop
Target Throughput
1M metric points/min
Latency Profile
45ms AI anomaly inference
Availability SLA
99.999%
Storage Subsystem
Cloudflare D1 + Vectorize Embeddings
Compute Runtime
Workers AI (GLM-5.3 & Gemma-4) + Workers Cron
The Engineering Challenge
Modern cloud architectures suffer from alert fatigue. Static threshold alerts trigger hundreds of false alarms, while genuine cascading network splits take minutes to diagnose manually.
The Architectural Solution
Designed an autonomous observability pipeline that processes vector embeddings of system metrics using Workers AI, evaluates root-cause graphs, executes automated remediation scripts, and pages staff via Evolution API.
Implementation Details
Engineered automated canary rollback triggers on anomalous error spikes.
Configured Evolution API WhatsApp interactive response buttons for immediate SRE ack.
Utilized Cloudflare Vectorize to match historical incident resolution playbooks.
Implemented fail-safe fallback models (GLM-5.3 -> Gemma-4 -> Llama-3.3).
Technologies Used
Cloudflare Workers AICloudflare D1VectorizeEvolution APITypeScriptPrometheus
anomaly-evaluator.tstypescript
export async function evaluateTelemetryAnomaly(telemetryBatch: MetricVector, env: Env) {
const embedding = await env.AI.run('@cf/baai/bge-large-en-v1.5', { text: JSON.stringify(telemetryBatch) });
const similarIncidents = await env.VECTORIZE.query(embedding.data[0], { topK: 3 });
if (telemetryBatch.errorRate > 0.05) {
const aiAnalysis = await env.AI.run('cf/zai-org/glm-5.3-flash', {
messages: [
{ role: 'system', content: 'You are an SRE incident responder. Output concise JSON diagnosis.' },
{ role: 'user', content: `Analyze telemetry: ${JSON.stringify(telemetryBatch)}` }
],
temperature: 0.2
});
await dispatchWhatsAppAlert(env, aiAnalysis.response);
}
}Technical Discussion (0)
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Available for architecture advisory and technical design reviews.