Listen to this Post
Introduction:
In the high-stakes arena of modern software engineering and cybersecurity, the traditional model of observability—merely seeing what’s broken—is becoming obsolete. As Greg Coquillo, Product Leader at AWS, highlights, the hardest problem isn’t building the system; it’s managing the “complexity drift” that occurs after you ship. We are entering the era of “agentic observability,” a paradigm shift where systems evolve from passive data collectors into active participants in their own stability and security. By leveraging AIOps and causal analysis, these thinking systems don’t just detect anomalies; they correlate them with business impact and execute automated remediation, fundamentally altering how we defend and maintain distributed architectures.
Learning Objectives:
- Understand the technical shift from visibility-based monitoring to execution-based observability.
- Learn how to correlate telemetry data (logs, metrics, traces) into causal graphs for root cause analysis.
- Explore the implementation of automated remediation workflows to reduce cognitive load and reaction times.
You Should Know:
1. Correlating Telemetry into Causal Graphs
The foundation of agentic observability lies in moving beyond siloed data. Greg Coquillo mentions correlating “logs, traces, metrics, and events into causal graphs.” In practice, this means abandoning the practice of switching between dashboards and instead implementing a unified data ingestion strategy.
What this does:
It creates a dependency map of your system, allowing you to see that a spike in CPU metrics (from Prometheus) is causally linked to a specific error log (from Fluentd) and a slowdown in a specific API trace (from Jaeger).
Step‑by‑step guide to setting up a basic correlation pipeline using the OpenTelemetry Collector:
1. Install the OpenTelemetry Collector: Download the appropriate binary for your OS from the OpenTelemetry releases page.
2. Configure Receivers: Edit the `config.yaml` file to pull data from various sources.
receivers: prometheus: config: scrape_configs: - job_name: 'node-exporter' static_configs: - targets: ['localhost:9100']
3. Enable Trace Correlation: Ensure your applications are instrumented to send trace IDs. Configure the collector to inject these IDs into log messages. This creates the “causal link.”
4. Export to a Backend: Send the unified data to a backend like New Relic or Grafana Tempo that supports graph visualization.
Linux command to check if the collector is running and processing metrics sudo systemctl status otelcol-contrib journalctl -u otelcol-contrib -f --output cat
- Using AIOps to Reduce Alert Noise (Anomaly Detection)
AIOps applies machine learning to IT operations. The goal is to filter out the “noise” of false positives that plague security and operations teams, allowing them to focus on contextual anomalies.
Step‑by‑step guide to implementing a basic statistical anomaly detection model (using Python and Prometheus data):
1. Query Historical Data: Use the Prometheus HTTP API to pull historical CPU and memory data.
Linux curl command to query Prometheus curl 'http://localhost:9090/api/v1/query_range?query=node_cpu_seconds_total&start=2024-02-17T00:00:00Z&end=2024-02-18T00:00:00Z&step=15m' > historical_data.json
2. Analyze with Python: Use a simple Z-Score method to detect outliers in the data, which would trigger an alert.
Python script snippet for anomaly detection
import numpy as np
from scipy import stats
Assume 'data' is a list of CPU utilization percentages
z_scores = np.abs(stats.zscore(data))
Define a threshold (e.g., 3 standard deviations from the mean)
anomalies = np.where(z_scores > 3)
print(f"Anomalies detected at indices: {anomalies}")
3. Integrate with Alert Manager: Configure Prometheus AlertManager to only fire an alert if the anomaly persists for a certain duration, reducing flapping alerts.
3. Connecting Reliability Signals to Business Impact
Technical metrics are meaningless without context. Agentic observability requires mapping low-level signals (like 500 errors) to high-level business KPIs (like checkout completion rate).
Step‑by‑step guide to tagging infrastructure with business context:
- Define Business Service Tiers: Identify which microservices handle critical revenue streams (e.g., “payment-service” or “shopping-cart”).
- Implement Consistent Tagging: When deploying cloud resources, ensure they are tagged with business metadata. For AWS, use the CLI to enforce tags.
AWS CLI command to tag an EC2 instance with business context aws ec2 create-tags --resources i-1234567890abcdef0 --tags Key=BusinessUnit,Value=Checkout Key=RevenueImpact,Value=Critical
- Query with Business Filters: In your observability tool, create dashboards that filter specifically for “BusinessUnit=Checkout.” Now, a spike in latency in this specific group directly correlates to potential revenue loss.
4. Enabling Automated Remediation
The pinnacle of this shift is moving from manual triage to automated execution. If the system detects a database connection pool exhaustion, it should attempt to fix itself before a human is paged.
Step‑by‑step guide to creating a self-healing script (Linux/Windows):
Scenario: A service is running out of disk space, causing write failures. The system should automatically clean up old logs.
- Create the Remediation Script: Write a script that triggers when disk usage hits a threshold.
!/bin/bash Linux remediation script (cleanup_old_logs.sh) THRESHOLD=90 CURRENT=$(df / | grep / | awk '{ print $5}' | sed 's/%//g')</li> </ol> if [ "$CURRENT" -gt "$THRESHOLD" ] ; then echo "Disk space critical ($CURRENT%). Cleaning logs older than 7 days in /var/log/myapp/" find /var/log/myapp/ -type f -name ".log" -mtime +7 -exec rm -f {} \; Log the remediation action logger "Auto-remediation: Deleted old logs due to disk space > $THRESHOLD%" fi2. Schedule the Script: Use cron (Linux) or Task Scheduler (Windows) to run the script frequently.
Cron job to run every 30 minutes (Linux) /30 /usr/local/bin/cleanup_old_logs.sh
3. Windows Equivalent (PowerShell):
Windows PowerShell remediation script $Threshold = 90 $Drive = Get-WmiObject Win32_LogicalDisk -Filter "DeviceID='C:'" | Select-Object Size,FreeSpace $PercentFree = [bash]::round(($Drive.FreeSpace/$Drive.Size)100) if ($PercentFree -lt (100-$Threshold)) { Write-Host "Disk space critical. Cleaning IIS logs..." Get-ChildItem -Path "C:\inetpub\logs\LogFiles" -Recurse -File | Where-Object {$_.LastWriteTime -lt (Get-Date).AddDays(-7)} | Remove-Item -Force Write-EventLog -LogName Application -Source "AutoRemediation" -EventId 999 -Message "Cleaned old IIS logs." }5. Reducing Cognitive Load with Thinking Systems
The ultimate goal, as noted in the comments by Arslan Ihsan, is to prevent “complexity drift.” This requires standardizing how alerts are handled so engineers aren’t wasting brainpower on repetitive firefighting.
Step‑by‑step guide to implementing “ChatOps” for remediation:
- Integrate Alerting with Slack/MSTeams: Use webhooks to send alerts to a dedicated channel.
- Add Interactive Buttons: Configure the alert message to include buttons like “Run Pre-Checks” or “Restart Service.”
- Build a Backend Receiver: Create a simple Flask (Python) app that listens for the button clicks.
Flask endpoint for Slack interactive messages @app.route('/slack/actions', methods=['POST']) def slack_actions(): payload = json.loads(request.form['payload']) if payload['actions'][bash]['value'] == 'restart_nginx': subprocess.run(['sudo', 'systemctl', 'restart', 'nginx']) return "NGINX Restarted", 200 - Automate the Fix: The button click triggers the remediation script directly from the chat interface, allowing an engineer to fix the issue in seconds without logging into a server.
What Undercode Say:
- Key Takeaway 1: Observability is no longer a passive log aggregator; it is the control plane for system stability. By shifting from dashboards to automated execution, organizations can reclaim engineering time lost to manual triage.
- Key Takeaway 2: The integration of AIOps is critical to cut through the noise. However, the “agentic” part is what truly matters—the system must have the autonomy to execute fixes, not just surface data.
Analysis:
The discussion led by Greg Coquillo underscores a critical evolution in DevOps and cybersecurity. The traditional “break-fix” model is economically unsustainable at cloud scale. The transition to “agentic” systems reflects a broader industry move toward autonomous computing, where the infrastructure is treated as a self-regulating organism. For security professionals, this is a double-edged sword. While it drastically reduces Mean Time to Resolution (MTTR) for known issues, it introduces new vectors regarding the security of the remediation scripts themselves. If an attacker can manipulate the “thinking system,” they could potentially trigger destructive self-remediation. Therefore, securing the observability pipeline and the automation workflows (IAM roles, signed scripts, audit trails) becomes just as critical as securing the application code. This is the next frontier: building systems that are not only self-healing but also resilient against having their healing mechanisms weaponized.
Prediction:
Within the next 24 months, “agentic observability” will become a standard compliance requirement for high-security environments (finance, healthcare). Auditors will no longer ask “do you monitor for intrusion?” but rather “does your system automatically isolate compromised segments?” The future belongs to platforms that can think, decide, and act faster than humanly possible, rendering the manual “ticket-driven” incident response model obsolete.
▶️ Related Video (84% Match):
🎯Let’s Practice For Free:
IT/Security Reporter URL:
Reported By: Greg Coquillo – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅🔐JOIN OUR CYBER WORLD [ CVE News • HackMonitor • UndercodeNews ]
📢 Follow UndercodeTesting & Stay Tuned:
