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AnswersWhat is AIOps?

What is AIOps?

Direct answer

AIOps is the use of machine learning and statistical analysis on monitoring and event data to cut alert noise, correlate signals, and speed detection and diagnosis in IT operations. It is an analysis discipline, not an action discipline, and the field is now shifting toward agentic operations, where software also proposes or takes action under governance.

By Platform Documentation deskPublished Updated

The longer answer

Where the term came from

Gartner coined AIOps (artificial intelligence for IT operations) in the mid-2010s to describe a specific, narrower job than the word now carries in marketing copy: ingest the flood of events, logs, metrics, and traces that monitoring tools produce, then apply machine learning to deduplicate alerts, correlate related signals into one incident, detect anomalies against a learned baseline, and rank a probable root cause. The original scope was noise reduction and correlation, not decision-making.

That scope still describes most of what an AIOps tool does today. Moogsoft's platform, one of the category's original vendors, still frames its job as turning a flood of alerts into a smaller number of actionable situations. BigPanda's positioning is the same instinct applied to modern, agent-assisted IT operations.

What AIOps does and does not do

An AIOps layer typically does four things: ingests events and telemetry from many tools into one place, deduplicates and suppresses noisy or flapping alerts, correlates related signals into a single incident instead of twenty separate tickets, and ranks a probable cause or a similar past incident. What it has not traditionally done is take an action, own the ticket workflow, or carry the business consequence of the incident it is describing.

That boundary is exactly what is moving. Datadog's Bits Investigation renders a hypothesis tree and a full agent trace of every tool call it made to get there, which is analysis made inspectable rather than analysis plus autonomous action. Dynatrace argues a stronger version of the same caution: causal AI should produce the answer deterministically before any agent is allowed to act on it, not blend the two.

Why the category is being renamed

Gartner itself now frames the shift as moving "from AIOps to Agentic Ops," and pairs that framing with a pointed caution: it predicts more than 40 percent of agentic AI projects will be cancelled by the end of 2027, for cost, unclear value, and inadequate risk controls. That is not a vendor's marketing claim about the future; it is the same analyst house that named the AIOps category warning that the next phase of it is failing at scale for identifiable reasons.

Forrester's most recent AIOps Platforms Wave, from Q2 2025, still names Datadog, Dynatrace, and ScienceLogic as Leaders under the AIOps definition as it stood then, which is a useful anchor for how recently "AIOps" and "agentic operations" were the same conversation.

A worked example of what an AIOps layer actually does

Picture a payments database running low on connection-pool capacity. Left alone, that condition trips a dozen separate alerts: one from the database's own monitor, several from every service whose calls start timing out, and one or two from an uptime check watching the checkout page. Without AIOps, an on-call responder sees twelve tickets and has to notice by hand that they share a root cause. An AIOps layer's job is narrow and specific here: deduplicate the repeated timeouts, correlate all twelve into one incident, flag the connection-pool exhaustion as the most probable common cause based on timing and topology, and hand a human one incident with a ranked hypothesis instead of twelve unrelated pages.

Notice what the tool still has not done. It has not paged anyone with authority to grow the pool, has not changed a configuration value, has not verified the fix worked, and has not connected the incident to whatever business consequence followed, such as a spike in abandoned checkouts. Those steps belong to a workflow platform, a human responder, or, increasingly, an agent operating under an approval gate. That handoff point is precisely where the AIOps label stops being accurate and terms like agentic operations or control plane become the more precise ones to reach for.

Where a cross-domain decision layer fits

AIOps tools are built to reduce noise inside one telemetry stack. They generally do not carry a finding across the boundary from infrastructure into claims, contact center, workforce, or cost, because that boundary usually sits outside any one tool's data model. A connection-pool incident and a spike in customer complaints to a contact center are, to most AIOps tools, two unrelated systems with two unrelated alert streams, even when they are the same event seen from two departments.

HELM by LockedIn Labs does not replace an AIOps or observability layer; it is built to consume the signals those layers already produce and carry a finding into a governed, cross-domain decision with a named accountable owner. That is a different job, not a better version of the same job, and a buyer evaluating HELM by LockedIn Labs against a pure AIOps tool is comparing two products built to solve different halves of the same incident.

How to check this yourself

  1. Inspect the scorecards. Check whether each indicator names the field or formula it was computed from, rather than showing an unlabeled trend line.
  2. Inspect the source contracts. See how a signal source and a tool's scope are declared, independent of any one vendor's telemetry format.
  3. Review the linked research in Sources. Read the public research behind the field's analysis and governance claims.

Related questions

Sources