How PagerDuty automatically categorizes incidents using predefined rules and machine learning.

Outline

• Opening hook: why automatic categorization matters in incident response.

• Why it benefits teams: speed, consistency, and focus on resolution.

• The two engines that power auto-categorization: predefined rules and machine learning.

• Deep dive into predefined rules: how Rule Builder works, examples, and governance.

• Deep dive into machine learning: learning from historical incidents, when to trust ML, and the human-in-the-loop balance.

• PagerDuty specifics: how Rule Builder and Event Intelligence fit into the workflow.

• Practical tips: keeping rules clean, monitoring outcomes, and testing approaches.

• Common pitfalls and how to avoid them.

• Pulling it together: a pragmatic mindset for automatic categorization in real-world incidents.

Ever wondered how a fresh alert becomes a meaningful category without someone babysitting it 24/7? If you’ve ever wrestled with noisy incident streams, you know: automatic categorization isn’t a luxury. It’s a productivity multiplier. It helps teams move from “What is this?” to “What do we do about it?” faster, with steadier consistency across incidents and teams. In PagerDuty, auto-tagging incidents hinges on two reliable engines: predefined rules and machine learning. Let’s unpack how each works, why they matter, and how you can tune them for real-world reliability.

Why automatic categorization matters in incident response

Think of incident categorization as the first real decision in triage. If you mislabel an outage as a minor performance hiccup, you risk routing it to the wrong on-call rotation, wasting time and triggering delays in remediation. If you get it right, you shorten the clock to containment, you improve prioritization, and you free engineers to focus on fixes instead of taxonomy. Automatic categorization helps standardize this step so humans can act on what truly matters.

Two engines behind auto-tagging: predefined rules and machine learning

In PagerDuty, you typically combine two approaches. The rule-based layer gives you explicit, controllable behavior. The machine-learning layer brings adaptive intelligence that learns from past incidents and improves over time. Together, they balance predictability with reflection of real-world patterns.

Predefined rules: predictable, explainable, solid

Rule-based categorization is all about setting up patterns you expect to see. You write rules that say, in effect:

• If the incident targets Service X and contains keywords Y or Z, categorize as “X-related outage.”

• If the incident comes from Monitoring Tool A with high severity, categorize as “Critical on-call event.”

• If the alert arrives during Maintenance Window, categorize as “Maintenance-related.”

How this works in practice

• You define fields you care about: service names, impacted regions, affected components, severity levels, source tools, and even specific error codes.

• You attach logic to those fields. Rules can be simple (one condition) or more nuanced (multiple conditions with AND/OR logic).

• You assign a default category when nothing specific matches, preserving a safe fallback.

• You can set prioritization and routing adjustments based on category, so responders get the right escalation path automatically.

Good rule design is part art, part science. Start with a few well-understood patterns—common service failures, recurring error codes, or known maintenance windows—and then broaden as you observe real-world data. The benefits are tangible: fewer manual picks, consistent categorization across teams, and a growing baseline you can audit and improve.

Machine learning: when history informs the future

If predefined rules are the knobs you twist with your own hands, machine learning is the system that learns from the room. ML looks at historical incident data—what categories were used, how quickly teams responded, and what patterns preceded the incident—and starts to mimic those decisions.

Where ML adds value

• Handles nuance: incidents that don’t fit clean, one-line rules but share a broader pattern across many events can be categorized more accurately.

• Improves over time: as you collect more incidents, the model refines its suggestions and reduces mislabels.

• Reduces repetitive toil: analysts don’t have to craft every new rule for every new kind of incident; the model can generalize from existing patterns.

Important caveats

• ML isn’t magic. It’s data-driven and requires clean, representative historical data. If your past categorization was noisy, the model may echo that noise.

• A human-in-the-loop approach tends to work best. Let the model propose a category, then let a human confirm or adjust it. Over time, trust grows as the model learns from corrections.

• ML shines for complex, evolving environments where patterns shift, and manual rule creation would be slow or brittle.

PagerDuty specifics: how Rule Builder and Event Intelligence come into play

PagerDuty blends these approaches in a practical, usable way. Two core components often surface in teams’ workflows:

• Rule Builder (predefined rules)

This is where you craft the explicit logic. The Rule Builder lets you map events to categories using fields like service, component, source, severity, and message content. It’s the guardrail that keeps categorization stable when things go sideways. You can layer multiple rules, set precedence, and implement default fallbacks. The result is a predictable, auditable categorization path that you can explain to stakeholders and adjust when your service catalog evolves.

• Event Intelligence (machine learning and intelligent triage)

Event Intelligence adds a learning layer on top of the raw event stream. It analyzes patterns across past incidents, identifies likely categories, and can suggest categorizations or autosuggest them for review. It’s particularly helpful in environments with high volume or where incidents evolve beyond simple rule-based patterns. The ML component gets smarter as more incidents roll in, so you end up with fewer manual corrections over time.

Real-world flavor: what this looks like day to day

• A server in a distant data center spikes in response times. A rule says: if the incident involves Service A and region Europe with latency above threshold, categorize as “Performance degradation for Service A.” The incident lands in the right queue, the on-call rotates smoothly, and engineers jump into remediation.

• Over several weeks, a rare error code appears across multiple services but always with a similar root cause. The Rule Builder might miss this because it’s a new pattern. Event Intelligence analyzes the history, starts suggesting the “Root cause: database contention” category more often, and your team begins to see faster triage without endlessly tweaking rules.

• There are edge cases. A maintenance window triggers a flurry of alerts that look urgent but aren’t. A rule can catch that scenario and assign a low-priority categorization or silence duplicates, reducing unnecessary fire drills.

Tips to keep categorization accurate and useful

• Start lean, then expand: implement a small set of high-confidence rules first, then add more as you observe real incident flow.

• Keep categories meaningful: a good category should drive an actionable response. If a category doesn’t map to a clear remediation path, reconsider its usefulness.

• Audit and adjust regularly: review weekly or biweekly what was auto-categorized, what was overridden by humans, and where mislabels crept in. Use those insights to tune rules or retrain the ML model.

• Balance specificity with generalization: very narrow rules can miss patterns, but too broad rules invite misclassification. Aim for middle ground that captures the majority of cases accurately.

• Protect governance and compliance: document why a rule exists, who approved it, and how exceptions are handled. This helps when audits or post-incident reviews come around.

Common pitfalls and how to avoid them

• Overfitting rules to past incidents: a rule that perfectly matches a few old events may fail on new ones. Regularly test rules against a fresh data slice.

• Ignoring data quality: ML thrives on clean data. If incident logs are noisy, take steps to standardize fields and reduce duplicates before feeding them to the model.

• Relying on automation to the exclusion of human insight: automation should augment human judgment, not replace it. Maintain a clear workflow where analysts review auto-categorization when confidence is low.

• Underestimating the maintenance burden: rules require upkeep as services evolve, teams change, and environments shift. Schedule time for periodic reviews.

A pragmatic mindset for automatic categorization in real-world incidents

Let me explain it this way: you’re not choosing between a rule-based map and a magic ML wand. You’re building a map and training an apprentice. The rules give you a map with landmarks you trust. The ML model is the apprentice who notices paths you might have missed and gently suggests better routes as it learns. Together, they help you move from scattered alerts to clear, decisive steps.

If you’re new to PagerDuty, here’s a straightforward way to start:

• Define a small, critical rule set for your most important services. Focus on obvious patterns—service name, severity, and a couple of key keywords.

• Enable Event Intelligence as a companion, with a low-confidence threshold. Let it propose a category, and review a subset to understand where it shines and where it falters.

• Create a simple governance loop: monthly reviews of auto-categorization outcomes, with a punch list of rule adjustments and ML retraining prompts.

• Monitor impact: track how auto-categorization affects mean time to acknowledge (MTTA) and mean time to resolve (MTTR). If numbers trend in the right direction, you’re on the right track.

To wrap it up

Automatic categorization in PagerDuty isn’t a one-and-done feature. It’s a layered capability that combines the predictability of predefined rules with the adaptive power of machine learning. When tuned well, it trims the noise, aligns responses with real-world patterns, and keeps your incident response lean and focused. It’s not about replacing human judgment; it’s about making the first, best decision as often as possible so the team can do what they do best: fix issues and restore services quickly.

If you’re exploring PagerDuty as part of your incident response toolkit, think of auto-categorization as a smart co-pilot. It can lighten the cognitive load, reduce repetitive decisions, and help your team stay aligned when the heat is on. And as you gain experience, you’ll notice the system not only categorizes faster but does so with increasing accuracy—the mark of a well-tuned, data-driven incident program.