Tag: Jaeger

At 2 a.m., a payment service starts throwing errors. Within minutes, your observability stack fires off 40 alerts: Elevated latency on three services, a spike in 5xx errors, a memory warning on a downstream cache and a handful of dependency timeouts. Somewhere in that noise is the one signal that actually explains what broke. Finding it manually, alert by alert, is how a 5-minute fix turns into a 45-minute outage.

This is the core problem AIOps was built to solve — not more dashboards, not more alerts but correlation. By automatically grouping related signals across metrics, logs and traces, AI-driven correlation collapses an alert storm into a single, prioritized incident with a probable root cause attached.

In this guide, you’ll learn how incident correlation actually works, how to set it up on top of your existing observability stack and how to measure whether it’s actually reducing your mean time to resolution (MTTR). By the end, you’ll have a practical framework for moving your team from manual alert triage to AI-assisted incident response.

Why MTTR Breaks Down Without Correlation

MTTR is typically broken down into four phases: Detection, triage, diagnosis and remediation. Most teams invest heavily in detection — that’s what observability tooling is good at. The bottleneck almost always sits in triage and diagnosis, for a simple reason: One root cause typically produces many alerts, and someone has to manually figure out which alerts belong together before the diagnosis of the actual problem can even begin.

A few patterns show up repeatedly:

Alert storms mask root cause. A single upstream failure can cascade into dozens of downstream alerts, each looking like an independent incident.

Signals live in silos. Metrics, logs and traces often sit in different tools or dashboards, so the engineer on call has to manually stitch together a timeline.

Tribal knowledge bottlenecks diagnosis. The person who ‘just knows’ that a cache-eviction warning usually precedes a checkout failure isn’t always the person on call.

AI-driven correlation directly targets this middle stretch of the incident life cycle — the time between ‘something is wrong’ and ‘here’s what’s actually wrong’ — which is usually where the most time is lost.

Prerequisites

Before you begin, make sure you have:

1. An existing observability stack producing metrics, logs and traces (StackGen’s ObserveNow or a Prometheus/Grafana/Loki/Jaeger-style setup)
2. Alerting already configured on key services, even if it’s currently noisy
3. Admin access to configure integrations and correlation rules
4. At least one recent incident with alert history you can use to validate correlation output
5. Estimated time: 30–45 minutes for initial setup, plus 1–2 weeks of tuning based on real incidents.

Step 1: Connect Your Observability Stack

AI-driven correlation is only as good as the data it can see. If your metrics live in one tool, logs in another and traces in a third with no shared identifiers, correlation has nothing to work with.

Start by making sure your telemetry shares common context — service name, environment and ideally a trace ID or request ID that threads through logs and traces.

# Example: Shared labeling convention across metrics, logs and traces

service: checkout-api

environment: production

trace_id: “{{request.trace_id}}”

If you’re using ObserveNow, connecting metrics, logs and traces into a single pane is largely a matter of pointing your existing Prometheus, Loki and Jaeger (or equivalent) instances at it — StackGen integrates with these directly rather than requiring a rip-and-replace.

Expected Result: A unified view where a single service’s metrics, logs and traces are queryable together, ideally with a shared identifier connecting them.

Troubleshooting: If correlation later feels inaccurate, this is almost always the first place to look — inconsistent service-naming or missing trace propagation breaks the AI’s ability to link signals correctly.

Step 2: Configure AI-Driven Correlation Rules

With unified telemetry in place, the next step is telling the correlation engine what ‘related’ should mean for your environment. Most AIOps platforms, including Aiden AI Copilot, combine two approaches:

Topology-Aware Correlation: Using service dependency maps to group alerts from services that call each other

Pattern-Based Correlation: Using historical incident data to learn which alert combinations tend to co-occur

# Example: Correlation policy

correlation_policy:

time_window: 5m

group_by:

– service_dependency

– shared_trace_id

confidence_threshold: 0.75

The time_window matters more than it looks. Set it too short and you’ll miss slower-cascading failures; set it too long and unrelated incidents start getting merged. Five minutes is a reasonable starting point for most microservice architectures — narrow it down if your services are tightly coupled with fast failure propagation, widen it for slower, more decoupled systems.

Expected Result: Incoming alerts are grouped into incidents rather than arriving as isolated tickets.

Troubleshooting: If everything gets grouped into one mega-incident, either your confidence threshold is too low or your time window is too wide. Tighten both and re-test against a known incident.

Step 3: Test Against a Real Incident

Don’t wait for a live outage to find out if your correlation setup works. Pull the alert history from a recent real incident and replay it against your new configuration.

Ask three questions about the output:

1. Did the correlation engine group the alerts that actually belonged together?
2. Did it surface a root-cause candidate that matches what you now know actually happened?
3. Did it leave out anything that should have been included or wrongly include something unrelated?

This is also where Aiden/AIOps automated root cause analysis is worth validating directly. It should be able to point to the earliest anomaly in the correlated group and rank likely causes, not just group alerts together and leave the analysis to you.

Expected Result: The correlated incident matches (or comes close to) the retrospective understanding of the incident, with the root-cause candidate ranked at or near the top.

Step 4: Monitor, Tune and Automate Remediation

Correlation isn’t a one-time setup. It improves while identifying more of your real incident patterns. Over the first few weeks:

• Review every correlated incident and confirm or correct the grouping.
• Watch for recurring false groupings (two unrelated services that keep getting merged) and adjust topology mappings.
• Track false-negative correlations — alerts that should have been grouped but weren’t.

Once correlation accuracy is consistently high, you can layer-in automated remediation for well-understood incident patterns — for example, automatically restarting a service instance when a specific correlated pattern (memory leak signature + latency spike + no recent deploy) is detected with high confidence. This is where Aiden for SRE typically comes in: Using the correlated, root-caused incident as the trigger for a pre-approved remediation action, rather than waiting for a human to read the alert and act.

Expected Result: A shrinking gap between detection and remediation, with routine incident patterns increasingly self-resolving.

Testing Your Setup

Before trusting correlation in production, run it through a structured validation pass:

• Replay 3–5 historical incidents of varying severity and complexity.
• Compare AI-correlated groupings against what your team determined manually during the actual incident.
• Check root-cause ranking accuracy — was the true cause in the top 1–2 candidates?
• Confirm the time to first correlated alert is meaningfully faster than your team’s manual triage time.

If correlation accuracy is inconsistent across incident types, it’s usually a sign that your topology mapping is incomplete for certain services, which is why it is worth revisiting Step 1 before adding more correlation rules on top of gaps in the data.

Real-World Results

Teams that move from manual alert triage to AI-driven correlation typically see improvement concentrated in the triage and diagnosis phases specifically, since that’s the part of MTTR correlation is designed to compress:

• Alert Volume per Incident: Often reduced from dozens of individual alerts down to a single correlated incident
• Time to Root-Cause Identification: Meaningfully faster when root-cause ranking is accurate, since engineers stop manually cross-referencing dashboards
• On-Call Burden: Fewer distinct pages per incident, since related alerts are grouped rather than firing independently

Your specific numbers will depend heavily on how mature your topology mapping is and how much historical incident data the correlation engine has to learn from — treat the first few weeks as a tuning period, not the final benchmark.

Next Steps

Now that you have AI-driven correlation running, you can:

• Extend correlation rules to cover infrastructure-layer signals (node health, network) alongside application-layer telemetry
• Pilot automated remediation on your highest-confidence, lowest-risk incident patterns first
• Expand from Aiden for SRE into Aiden for DevOps if pipeline and deployment failures are a significant source of your incident volume