Frontier Investigation Agents
They sound alike, but they solve different problems for different teams. AIOps uses AI to run IT and cloud operations. MLOps operates the machine-learning model lifecycle. Where AIOps stops at the alert, CloudThinker's Frontier Investigation Agents find the why behind every production issue and propose the fix. Your engineers approve.
AIOps (Artificial Intelligence for IT Operations) applies machine learning to operational signal (logs, metrics, traces, alerts) to reduce noise, correlate incidents, and detect anomalies. Its goal is a healthier, more self-driving IT and cloud estate. Investigation agents extend AIOps from surfacing insights to finding the root cause and proposing the fix.
MLOps (Machine Learning Operations) is the discipline of taking machine-learning models to production and keeping them healthy: data versioning, feature pipelines, model training, deployment, monitoring for drift, and retraining. Its goal is reliable, reproducible models. The thing being operated is the model, not the infrastructure.
Same three letters, different discipline. The clearest way to tell them apart is to ask what gets operated, and who owns it.
| Dimension | AIOps | MLOps |
|---|---|---|
| Primary goal | Keep IT and cloud operations healthy and self-driving | Ship and maintain reliable machine-learning models |
| What is operated | Infrastructure, services, incidents, cloud spend | Datasets, features, model versions, training pipelines |
| Primary users | SRE, platform, and cloud operations teams | Data scientists, ML engineers, data platform teams |
| Core signal | Logs, metrics, traces, alerts, events | Training metrics, data drift, model accuracy, lineage |
| Success metric | MTTR, availability, toil reduction | Model performance, retraining cadence, reproducibility |
| End state | Agent-proposed remediation under policy | Continuously deployed, monitored, retrained models |
As AI systems move into production everywhere, the two disciplines are converging in the room but not in the org chart. Teams stand up an MLOps platform to ship models faster, then discover the operational blast radius those models create (latency spikes, drift-driven incidents, cost overruns) is an AIOps problem, not an MLOps one.
Conflating them leads to the wrong tool for the job: asking an MLOps pipeline to correlate production alerts, or asking an AIOps stack to version datasets. The winning pattern in 2026 is to keep them layered. MLOps owns the model. AIOps, with investigation agents on top, owns the running system the model lives in.
Classic AIOps surfaces the incident. CloudThinker's Frontier Investigation Agents run it through the DARV loop under team policy, with brokered credentials, sandboxed execution, deterministic data tokenization and a tamper-evident audit trail. They find the why and propose the fix, and your engineers approve.
Correlate the firehose of operational signal into a single incident worth acting on.
Walk the dependency graph, reason about root cause, and pick the matching runbook.
Execute the fix in a sandbox with brokered, scoped credentials, under the team approval gate.
Confirm the incident is resolved, write the tamper-evident receipt, and update team memory.
Autonomy is graduated, not all-or-nothing. Agents move from read-only (L1) through approve-to-act (L2 to L3) to fully autonomous within a guardrail (L4), one runbook at a time, as each earns trust. MLOps has no equivalent to this, because MLOps operates models, not production change.
Choose MLOps when your problem is shipping and maintaining machine-learning models.
Choose AIOps, with investigation agents on top, when your problem is running healthy IT and cloud operations at machine speed.
Do not make one do the other's job. They are layered disciplines, not competitors. Most production AI teams end up running both.
Agents investigate every issue across application, cloud, security, database and Kubernetes, find the why and propose the fix. Your engineers approve, under your policies and audit trail.