DevOps
AIOps and AI SRE: AI for operations
Models and agents on your monitoring and Kubernetes: alert correlation, on-call hints, gated auto-actions and observability for AI workloads. The team stays accountable; the agent does not run in the dark.
Discuss a TaskWhat is AIOps and AI SRE and how does it work
AIOps and AI SRE applies models and agents to day-to-day operations: they read metrics, logs and traces together, cut alert noise, speed up incident investigation and automate routine actions only where a written runbook already exists. Classic monitoring fires a separate alarm for every threshold. AIOps groups related events, ranks a likely cause and suggests the next step to the on-call engineer. AI SRE adds the reliability discipline around that: SLOs, error budgets, postmortems, tested rollbacks, and a rule that an automated action must not make production worse. A separate layer is the reliability of AI workloads themselves — GPUs on Kubernetes, inference servers, token-cost limits, latency and failure observability for models. ITFB implements this on your stack (Prometheus, Grafana, Elasticsearch, Kubernetes, cloud providers) without replacing the team with a black box. First we map signal sources and the current incident path, then we add correlation, dashboards and auto-actions that require human confirm, and only later widen automation where it has already proven accurate. The result is fewer false night pages, shorter MTTR and a clear split of responsibility between people and agents.
About the Service
AIOps and AI SRE is not another chat on top of Grafana. It is a loop where metrics, logs and traces collapse into incidents, on-call gets a likely cause, and allowed actions run only with a runbook and a rollback.
ITFB builds this on your stack: Prometheus, Grafana, Elasticsearch, Kubernetes, cloud. We also cover LLM/GPU reliability in production — latency, token cost, GPU use — without forcing a move to vendor SaaS.
AIOps cuts noise: hundreds of thresholds collapse into a few incidents with a likely cause. AI SRE adds SLOs, error budgets and a rule that auto-action needs a runbook and a rollback. Together that is shorter MTTR without replacing engineers with a chatbot.
The second loop is AI services in production: GPUs on Kubernetes, inference, token-cost limits, model latency and failures. That is not “install ChatGPT”; it is operations with the same reliability bar as payments or the customer cabinet.
When to Request It
- alerts arrive in bursts, the team mutes thresholds and misses real outages;
- MTTR is high because every incident starts from scratch across several tools;
- you want auto-actions (restart, scale, block) without giving a model root “just in case”;
- LLM/GPU is already in production or planned, with no clear SLOs for latency and cost.
What Is Included
- inventory of signal sources: metrics, logs, traces, synthetics, tickets;
- alert correlation and noise reduction without silencing real outages;
- AI hints for on-call: likely cause, similar incidents, next step from the runbook;
- auto-actions only with human confirm, an audit log and a rollback;
- SLOs, error budgets and a postmortem after priority incidents;
- LLM/GPU observability: latency, errors, token cost, GPU utilisation.
What You Get
- fewer false night pages; incidents group instead of an alert wall;
- shorter MTTR: on-call starts from a cause and a runbook, not an empty graph;
- response automation stays gated, with no black box in production.
How We Work
- we map signals and the current incident path, then agree SLOs;
- we add correlation, dashboards and hints on your stack without swapping tools;
- we add confirm-and-rollback auto-actions only where a runbook already holds;
- we review thresholds and agent accuracy after the first incidents, then keep support.
Technologies we work with
Observability
- Prometheus
- Grafana
- Loki
- OpenTelemetry
- Elasticsearch
Incidents
- PagerDuty
- Opsgenie
- Slack
- Telegram
- Runbooks
Platform
- Kubernetes
- Terraform
- Ansible
- GitLab CI
- GitHub Actions
AI workloads
- vLLM
- NVIDIA GPU Operator
- Hugging Face TGI
- OpenTelemetry for LLM
Why ITFB
Discipline first, then the agent
Without a signal map and runbooks a model only retells chaos in nicer prose. ITFB starts with the incident path, then adds AI.
Auto-action with brakes
Restart, scale or block only by policy, with confirm and rollback. The agent does not get root “just in case”.
Your stack, no lock-in
We work with Prometheus, Grafana, Elasticsearch, Kubernetes and the cloud you already run. No forced move to a vendor SaaS for a demo.
FAQ
Does this replace the on-call engineer?
No. The agent groups signals, suggests a cause and runs only allowed steps. Production decisions stay with a person until auto-action has proven accurate on your incidents.
How is this different from ordinary monitoring?
Monitoring collects metrics and fires alerts. AIOps folds related alerts into one incident and cuts noise. AI SRE adds SLOs, runbooks and gated response automation.
Do we already need Kubernetes and GPUs?
No. AIOps also runs on classic servers and VPS. We add the GPU loop if inference is already in production or planned. We start from the stack you have.
Where do incident logs go for the model?
By default they stay in your environment. External APIs are added only with written approval, with secrets masked and no customer dumps sent to a public model.
DevOps
Need advice on this service?
Describe the task, current infrastructure state or issue. The ITFB team will assess the situation and suggest a practical work plan for your business.