Guide · Evaluating agentic AIOps

Most AIOps shortens the alarm list.
Agentic AIOps closes the loop.

“Agentic” has become the default label on every AIOps datasheet, and it now separates products that behave very differently — one clusters alarms and hands a shorter list to an engineer, the other plans a change, checks it, executes it on approval, and verifies it held.

This guide gives the eight criteria that tell those apart on a carrier network, what to make each vendor prove in a PoC, and — stated plainly, since this is our own site — where Ritam, MatreComm’s AIOps engine, stands on each.

Carrier-gradeMulti-vendorOn-prem & sovereign
Closed loop · WATCH → DX → PLAN → ACTRUNNING
WATCH — ingest multi-vendor signals at carrier volume
DX — collapse the storm to one root cause
PLAN — assemble the change, check blast radius
ACT — execute on approval, then verify it held
A platform that stops after DX is a correlation engine.
The testDoes it act, or only advise?
The distinction that matters

Correlation ends where the work begins.

Alarm correlation was a genuine advance — it turned thousands of events into a handful of incidents. But it stops precisely where the operational cost sits: someone still has to diagnose the cause, design the change, judge its blast radius, execute it across vendors, and confirm it worked.

Event-correlation AIOps
Agentic AIOps
Clusters related alarms and suppresses duplicates, then routes an incident to a queue.
Names a single cross-domain root cause and carries it forward as the thing to be fixed.
Suggests probable causes ranked by confidence, leaving the engineer to confirm which is real.
Assembles the specific remediation — the actual config change or action, on the actual devices.
Hands off to a runbook, a ticket or an automation tool that someone else has to maintain.
Checks the plan for blast radius and policy compliance before anything is offered for approval.
Considers the incident closed when a human closes the ticket.
Executes on approval, then verifies the fix held and learns from the outcome.
Improves the mean time to understand.
Moves the number the operator is actually measured on — mean time to restore.
The eight criteria

What to score every candidate on.

Ordered by how often they decide the outcome of a carrier deployment. The right-hand column is where Ritam stands — verify it in a PoC on your own estate rather than taking it, or any vendor's equivalent claim, on faith.

CriterionWhat good looks likeRitam
Closed-loop autonomyPlans and executes remediation, then verifies — not just ranked suggestionsWATCH → DX → PLAN → ACT, executed on approvalCore design
Deployment modelRuns where your telemetry legally and operationally has to stayFully on-premises, including model inferenceSovereign
Multi-vendor reachActs across the estate you actually run, not one vendor's kit40+ vendors in production; list on requestVendor-neutral
Data-plane scaleIngests carrier event volumes without sampling away the signalBuilt for millions of events per secondCarrier-grade
Protocol surfaceReads and writes the protocols a telecom estate really speaksSNMP, syslog, gNMI, NETCONF, flow, TL1, BGP-LSTelecom-native
Control over actionBlast-radius and policy checks before execution; nothing acts unbiddenEvery plan checked, approved, logged, verifiedYou command
Prediction, not just reactionForecasts degradation ahead of impact rather than reporting it afterTSLAM4b time-series modelPre-emptive
AuditabilityEvery action, approval and outcome reconstructable after the factFull action and approval logEvidenced

Scored against our own platform on our own site — read it as a specification of what we think the bar is, and hold every vendor, including us, to proving it on your network.

Why deployment model decides more than it looks

For an operator, where the model runs is not an IT preference.

Most AIOps platforms are SaaS: telemetry leaves your network to be reasoned over in a vendor's cloud. For a carrier that is simultaneously a data-residency question, a dependency question and a commercial one.

Residency

Network telemetry, topology and configuration describe critical infrastructure. Where an obligation binds it to a jurisdiction, an off-shore control plane is not a deployment detail — it is the reason a project stops.

Dependency

A platform that repairs your network should not need your network to reach the internet in order to think. On-prem inference keeps the loop closed during exactly the events you bought it for.

Economics at carrier volume

Per-event or per-GB pricing behaves badly at millions of events per second. It creates pressure to sample telemetry — discarding the weak signals that predictive models depend on.

The PoC

Four things to make every vendor prove.

Each of these is answerable in a scoped proof of concept on a real slice of your network, and each one separates a platform that acts from one that advises.

1

Make it name a cause

Replay a real alarm storm from your own history. Does it produce one root cause, or a shorter list of alarms?

2

Make it write the change

Ask for the actual remediation on the actual devices — config, command, API call — not a runbook reference

3

Make it show blast radius

Before approving anything: what else does this change touch, and which policy did it check against?

4

Make it prove the fix

After execution, what evidence does it produce that the condition cleared and stayed cleared?

Run the same four tests on every candidate, including us. A vendor that cannot demonstrate step 2 on your own equipment is selling correlation with an agentic label on the box.
FAQ

Frequently asked questions

What is agentic AIOps?

Agentic AIOps is AIOps that acts rather than only advises. Where conventional AIOps clusters alarms and hands a shortened list to an engineer, an agentic platform names a root cause, assembles the specific remediation, checks it for blast radius and policy compliance, executes it on approval, and then verifies that the fix held. MatreComm's Ritam runs this as a closed loop: WATCH → DX → PLAN → ACT.

How do I tell agentic AIOps from event-correlation AIOps?

Ask the platform to produce the actual change — the configuration, command or API call on the actual devices — for a real incident from your own history. A correlation engine will return ranked probable causes or a runbook reference; an agentic platform returns the remediation itself, with its blast radius and the policy it was checked against, ready for approval.

What should a telecom operator evaluate an AIOps platform on?

Eight criteria decide most carrier deployments: closed-loop autonomy, deployment model and data residency, multi-vendor reach across the estate you actually run, data-plane scale at carrier event volumes, telecom protocol surface, control over action through blast-radius and policy checks, predictive capability ahead of impact rather than reporting after it, and auditability of every action, approval and outcome.

Why does on-premises deployment matter for AIOps in telecom?

Three reasons compound for an operator. Network telemetry, topology and configuration describe critical infrastructure, so residency obligations can bind where it is processed. A platform that repairs the network should not depend on that network reaching an external control plane in order to reason. And per-event pricing at millions of events per second creates pressure to sample telemetry, discarding the weak signals predictive models depend on.

Does agentic AIOps mean the platform acts without human approval?

Not in Ritam's case. Every plan passes blast-radius and policy checks, executes on your approval, is logged, and is verified afterwards. Autonomy here refers to the platform doing the diagnosis, planning and execution work — not to it holding authority independently of the operator.

Test it against the criteria

Bring us your worst alarm storm.

A scoped PoC on a real slice of your network, measured against the four proofs above — and your own KPIs, not ours.