Tech Ops

AI for technology operations

Agents that triage, correlate and remediate across your estate — with your engineers supervising the decisions that matter and a full audit trail behind every action.

Technology operations is where volume, noise and tacit knowledge collide. The runbook exists, but it lives in three wikis and one person's head; the alert fires, but nobody knows if it matters. Magnigent turns that tacit process into an executable specification, runs it with agents that can read your monitoring, ticketing and configuration systems, and escalates to a human the moment confidence drops below the threshold you set.

Use cases

Where tech ops gets automated first

Alert and alarm triage

Correlate related signals, suppress known noise, attach the relevant runbook, and escalate with a packaged context bundle instead of a bare page.

Incident24/7

Service desk L1/L2

Classify intake, enrich with live system state, resolve known issues end to end, and route the rest with the diagnostic work already done.

Volume

Incident command support

Assemble the timeline as it happens, draft internal and customer comms, keep the status page current, and produce the first draft of the postmortem.

Incident

Change and release operations

Draft the change record, score risk against historical outcomes, prepare the CAB packet, and verify that rollback actually works.

Governed

Cloud and infrastructure hygiene

Continuous rightsizing, orphaned resource reclamation, tag compliance, and commitment coverage analysis with owner-routed recommendations.

Cost

Data pipeline operations

Failure triage, schema drift detection, cross-system reconciliation, and data quality exception handling before the dashboard lies to someone.

Data

Vulnerability and patch operations

Triage CVEs against real exposure rather than raw CVSS, schedule remediation, and keep the exception register defensible.

Security

Documentation currency

Keep runbooks, architecture notes and API references in step with what actually shipped, flagged by diff rather than by calendar.

Knowledge

AgentOps

Monitor the AI agents you already run: latency, containment, cost per resolved task, drift, silent failure and permission scope.

AI-native

How it works

From tacit process to supervised operation

Every engagement follows the same four moves, whichever function it starts in — run by a forward deployed engineer embedded with the team that owns the process.

Connect the systems of record

Monitoring, ITSM, CMDB, source control, cloud accounts. The knowledge layer maps entities and relationships across them so an agent can reason about your estate, not a generic one.

Capture the process as a specification

Runbooks, escalation policies and the unwritten rules your best engineer applies at 3am become versioned, testable process definitions.

Run with supervision and evals

Agents execute inside scoped tool permissions. Every process carries a regression suite, so quality is measured continuously rather than asserted once.

Expand what runs unattended

Start with recommend-only. Promote steps to autonomous as the eval history earns it, and keep the human path for everything that has not.

Let's talk about the first process

Tell us the operation that costs the most and is documented the least. We will tell you honestly whether it is a good first candidate.