Retell AI Advanced Troubleshooting

Retell AI Advanced Troubleshooting

Overview

Deep debugging techniques for complex Retell AI issues that resist standard troubleshooting.

Prerequisites

• Access to production logs and metrics
• kubectl access to clusters
• Network capture tools available
• Understanding of distributed tracing

Evidence Collection Framework

Comprehensive Debug Bundle

#!/​bin/​bash
# advanced-retellai-debug.sh

BUNDLE="retellai-advanced-debug-$(date +%Y%m%d-%H%M%S)"
mkdir -p "$BUNDLE"/​{logs,metrics,network,config,traces}

# 1. Extended logs (1 hour window)
kubectl logs -l app=retellai-integration --since=1h > "$BUNDLE/​logs/​pods.log"
journalctl -u retellai-service --since "1 hour ago" > "$BUNDLE/​logs/​system.log"

# 2. Metrics dump
curl -s localhost:9090/​api/​v1/​query?query=retellai_requests_total > "$BUNDLE/​metrics/​requests.json"
curl -s localhost:9090/​api/​v1/​query?query=retellai_errors_total > "$BUNDLE/​metrics/​errors.json"

# 3. Network capture (30 seconds)
timeout 30 tcpdump -i any port 443 -w "$BUNDLE/​network/​capture.pcap" &

# 4. Distributed traces
curl -s localhost:16686/​api/​traces?service=retellai > "$BUNDLE/​traces/​jaeger.json"

# 5. Configuration state
kubectl get cm retellai-config -o yaml > "$BUNDLE/​config/​configmap.yaml"
kubectl get secret retellai-secrets -o yaml > "$BUNDLE/​config/​secrets-redacted.yaml"

tar -czf "$BUNDLE.tar.gz" "$BUNDLE"
echo "Advanced debug bundle: $BUNDLE.tar.gz"

Systematic Isolation

Layer-by-Layer Testing

// Test each layer independently
async function diagnoseRetell AIIssue(): Promise<DiagnosisReport> {
  const results: DiagnosisResult[] = [];

  // Layer 1: Network connectivity
  results.push(await testNetworkConnectivity());

  // Layer 2: DNS resolution
  results.push(await testDNSResolution('api.retellai.com'));

  // Layer 3: TLS handshake
  results.push(await testTLSHandshake('api.retellai.com'));

  // Layer 4: Authentication
  results.push(await testAuthentication());

  // Layer 5: API response
  results.push(await testAPIResponse());

  // Layer 6: Response parsing
  results.push(await testResponseParsing());

  return { results, firstFailure: results.find(r => !r.success) };
}

Minimal Reproduction

// Strip down to absolute minimum
async function minimalRepro(): Promise<void> {
  // 1. Fresh client, no customization
  const client = new RetellAIClient({
    apiKey: process.env.RETELLAI_API_KEY!,
  });

  // 2. Simplest possible call
  try {
    const result = await client.ping();
    console.log('Ping successful:', result);
  } catch (error) {
    console.error('Ping failed:', {
      message: error.message,
      code: error.code,
      stack: error.stack,
    });
  }
}

Timing Analysis

class TimingAnalyzer {
  private timings: Map<string, number[]> = new Map();

  async measure<T>(label: string, fn: () => Promise<T>): Promise<T> {
    const start = performance.now();
    try {
      return await fn();
    } finally {
      const duration = performance.now() - start;
      const existing = this.timings.get(label) || [];
      existing.push(duration);
      this.timings.set(label, existing);
    }
  }

  report(): TimingReport {
    const report: TimingReport = {};
    for (const [label, times] of this.timings) {
      report[label] = {
        count: times.length,
        min: Math.min(...times),
        max: Math.max(...times),
        avg: times.reduce((a, b) => a + b, 0) / times.length,
        p95: this.percentile(times, 95),
      };
    }
    return report;
  }
}

Memory and Resource Analysis

// Detect memory leaks in Retell AI client usage
const heapUsed: number[] = [];

setInterval(() => {
  const usage = process.memoryUsage();
  heapUsed.push(usage.heapUsed);

  // Alert on sustained growth
  if (heapUsed.length > 60) { // 1 hour at 1/​min
    const trend = heapUsed[59] - heapUsed[0];
    if (trend > 100 * 1024 * 1024) { // 100MB growth
      console.warn('Potential memory leak in retellai integration');
    }
  }
}, 60000);

Race Condition Detection

// Detect concurrent access issues
class Retell AIConcurrencyChecker {
  private inProgress: Set<string> = new Set();

  async execute<T>(key: string, fn: () => Promise<T>): Promise<T> {
    if (this.inProgress.has(key)) {
      console.warn(`Concurrent access detected for ${key}`);
    }

    this.inProgress.add(key);
    try {
      return await fn();
    } finally {
      this.inProgress.delete(key);
    }
  }
}

Support Escalation Template

## Retell AI Support Escalation

**Severity:** P[1-4]
**Request ID:** [from error response]
**Timestamp:** [ISO 8601]

### Issue Summary
[One paragraph description]

### Steps to Reproduce
1. [Step 1]
2. [Step 2]

### Expected vs Actual
- Expected: [behavior]
- Actual: [behavior]

### Evidence Attached
- [ ] Debug bundle (retellai-advanced-debug-*.tar.gz)
- [ ] Minimal reproduction code
- [ ] Timing analysis
- [ ] Network capture (if relevant)

### Workarounds Attempted
1. [Workaround 1] - Result: [outcome]
2. [Workaround 2] - Result: [outcome]

Instructions

Step 1: Collect Evidence Bundle

Run the comprehensive debug script to gather all relevant data.

Step 2: Systematic Isolation

Test each layer independently to identify the failure point.

Step 3: Create Minimal Reproduction

Strip down to the simplest failing case.

Step 4: Escalate with Evidence

Use the support template with all collected evidence.

Output

• Comprehensive debug bundle collected
• Failure layer identified
• Minimal reproduction created
• Support escalation submitted

Error Handling

Examples

Quick Layer Test

# Test each layer in sequence
curl -v https://api.retellai.com/​health 2>&1 | grep -E "(Connected|TLS|HTTP)"

Resources

• Retell AI Support Portal
• Retell AI Status Page

Next Steps

For load testing, see retellai-load-scale.