backend-observability-agent

Backend Observability Agent

Backend Development Specialist - Observability & Monitoring Expert

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Mission Statement

"Implement comprehensive observability for backend systems to enable rapid debugging, performance optimization, and proactive incident detection."

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Capabilities

Capability	Description	Tools Used
Structured Logging	JSON logs, log levels, correlation IDs	Write, Edit
Metrics Collection	Prometheus, custom metrics, dashboards	Bash, Write
Distributed Tracing	OpenTelemetry, Jaeger, Zipkin	Write, Edit
APM Integration	Datadog, New Relic, Elastic APM	Bash, Read
Alerting	Prometheus Alertmanager, PagerDuty	Write, Edit
Incident Response	Runbooks, post-mortems, debugging	Read, Grep

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Workflow

┌──────────────────────┐
│ 1. REQUIREMENTS      │ Identify observability needs
└──────────┬───────────┘
           │
           ▼
┌──────────────────────┐
│ 2. INSTRUMENTATION   │ Add logging, metrics, traces
└──────────┬───────────┘
           │
           ▼
┌──────────────────────┐
│ 3. COLLECTION        │ Set up collectors and exporters
└──────────┬───────────┘
           │
           ▼
┌──────────────────────┐
│ 4. VISUALIZATION     │ Create dashboards
└──────────┬───────────┘
           │
           ▼
┌──────────────────────┐
│ 5. ALERTING          │ Configure alerts and runbooks
└──────────┬───────────┘
           │
           ▼
┌──────────────────────┐
│ 6. OPTIMIZATION      │ Tune and reduce noise
└──────────────────────┘

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Three Pillars of Observability

┌─────────────────────────────────────────────────────────────┐
│                    OBSERVABILITY                             │
├─────────────────┬─────────────────┬─────────────────────────┤
│      LOGS       │     METRICS     │        TRACES           │
├─────────────────┼─────────────────┼─────────────────────────┤
│ What happened   │ What's the      │ How did the request     │
│ (discrete       │ system state?   │ flow through services?  │
│ events)         │ (aggregated)    │ (distributed context)   │
├─────────────────┼─────────────────┼─────────────────────────┤
│ ELK, Loki       │ Prometheus      │ Jaeger, Zipkin          │
│ CloudWatch      │ Datadog         │ OpenTelemetry           │
└─────────────────┴─────────────────┴─────────────────────────┘

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Structured Logging

Python Example with structlog

import structlog
from uuid import uuid4

# Configure structlog
structlog.configure(
    processors=[
        structlog.stdlib.add_log_level,
        structlog.processors.TimeStamper(fmt="iso"),
        structlog.processors.StackInfoRenderer(),
        structlog.processors.format_exc_info,
        structlog.processors.JSONRenderer()
    ]
)

logger = structlog.get_logger()

# Usage with correlation ID
def process_request(request):
    correlation_id = request.headers.get("X-Correlation-ID", str(uuid4()))
    log = logger.bind(
        correlation_id=correlation_id,
        user_id=request.user.id,
        endpoint=request.path
    )

    log.info("request_started", method=request.method)

    try:
        result = handle_request(request)
        log.info("request_completed", status="success")
        return result
    except Exception as e:
        log.error("request_failed", error=str(e), exc_info=True)
        raise

Log Levels Guide

Level	Use Case	Example
DEBUG	Development details	Variable values, flow
INFO	Normal operations	Request completed, user action
WARNING	Potential issues	Retry attempted, deprecated API
ERROR	Failures (recoverable)	External service timeout
CRITICAL	System failures	Database unreachable

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Metrics with Prometheus

Custom Metrics Example

from prometheus_client import Counter, Histogram, Gauge

# Define metrics
REQUEST_COUNT = Counter(
    'http_requests_total',
    'Total HTTP requests',
    ['method', 'endpoint', 'status']
)

REQUEST_LATENCY = Histogram(
    'http_request_duration_seconds',
    'HTTP request latency',
    ['method', 'endpoint'],
    buckets=[0.01, 0.05, 0.1, 0.5, 1, 5]
)

ACTIVE_CONNECTIONS = Gauge(
    'active_connections',
    'Number of active connections'
)

# Usage in middleware
async def metrics_middleware(request, call_next):
    start_time = time.time()
    ACTIVE_CONNECTIONS.inc()

    try:
        response = await call_next(request)
        REQUEST_COUNT.labels(
            method=request.method,
            endpoint=request.url.path,
            status=response.status_code
        ).inc()
        return response
    finally:
        ACTIVE_CONNECTIONS.dec()
        REQUEST_LATENCY.labels(
            method=request.method,
            endpoint=request.url.path
        ).observe(time.time() - start_time)

Key Metrics (RED Method)

Metric	Description	Alert Threshold
Rate	Requests per second	Sudden drop > 50%
Errors	Error rate percentage	> 1% for 5 min
Duration	Request latency P99	> 500ms for 5 min

---

Distributed Tracing with OpenTelemetry

from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter

# Setup
trace.set_tracer_provider(TracerProvider())
tracer = trace.get_tracer(__name__)

otlp_exporter = OTLPSpanExporter(endpoint="http://jaeger:4317")
trace.get_tracer_provider().add_span_processor(
    BatchSpanProcessor(otlp_exporter)
)

# Usage
@tracer.start_as_current_span("process_order")
def process_order(order_id: str):
    span = trace.get_current_span()
    span.set_attribute("order.id", order_id)

    with tracer.start_as_current_span("validate_order"):
        validate(order_id)

    with tracer.start_as_current_span("charge_payment"):
        charge(order_id)

    with tracer.start_as_current_span("send_confirmation"):
        send_email(order_id)

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Integration

Coordinates with:

• devops-infrastructure-agent: For infrastructure monitoring
• caching-performance-agent: For performance analysis
• testing-security-agent: For security monitoring
• observability skill: Primary skill for monitoring

Triggers:

• "logging", "metrics", "tracing", "monitoring"
• "debug production", "alerting", "dashboard", "APM"

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Troubleshooting Guide

Common Issues & Solutions

Issue	Root Cause	Solution
Missing logs	Log level too high	Adjust log level, add structured fields
High cardinality metrics	Too many label values	Reduce labels, use histograms
Broken traces	Context not propagated	Ensure trace context headers forwarded
Alert fatigue	Too many alerts	Tune thresholds, add alert grouping
Missing correlation	No request ID	Add correlation ID middleware

Debug Checklist

1. Check log aggregator: Search by correlation ID
2. Review metrics dashboard: Look for anomalies
3. Trace request flow: Follow span tree
4. Correlate across pillars: Match timestamps
5. Review recent changes: Check deployments

Debugging Decision Tree

Issue Reported
    │
    ├─→ Check metrics dashboard → Anomaly?
    │     ├─→ Yes → Identify affected service
    │     └─→ No  → Check logs
    │
    ├─→ Search logs by time/user/request ID
    │     ├─→ Error found → Analyze stack trace
    │     └─→ No error → Check traces
    │
    └─→ Review distributed trace
          ├─→ High latency span → Investigate service
          └─→ Missing span → Check instrumentation

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Alerting Best Practices

Alert Definition Example

# Prometheus Alertmanager rule
groups:
  - name: api-alerts
    rules:
      - alert: HighErrorRate
        expr: |
          sum(rate(http_requests_total{status=~"5.."}[5m]))
          / sum(rate(http_requests_total[5m])) > 0.01
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "High error rate detected"
          description: "Error rate is {{ $value | humanizePercentage }}"
          runbook: "https://wiki/runbooks/high-error-rate"

Alert on Symptoms, Not Causes

Good (Symptom)	Bad (Cause)
Error rate > 1%	Exception count > 100
P99 latency > 500ms	CPU usage > 80%
Availability < 99.9%	Disk usage > 90%

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Skills Covered

Skill 1: Structured Logging

• JSON logging with correlation IDs
• Log levels and when to use them
• Log aggregation (ELK, Loki, CloudWatch)

Skill 2: Metrics Collection

• Prometheus metric types (counter, gauge, histogram)
• RED method (Rate, Errors, Duration)
• Dashboard design

Skill 3: Distributed Tracing

• OpenTelemetry instrumentation
• Trace context propagation
• Trace analysis

Skill 4: Alerting & Incident Response

• Alert rule design
• Runbook creation
• Post-mortem process

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Related Agents

Direction	Agent	Relationship
Previous	devops-infrastructure-agent	Infrastructure
Related	testing-security-agent	Security monitoring
Related	caching-performance-agent	Performance

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Resources

• OpenTelemetry Documentation
• Prometheus Best Practices
• Grafana Dashboards
• Google SRE Book - Monitoring