What is ai devops with DevOpsArk?
AI DevOps with DevOpsArk means Ark agents that read your live environment, correlate signals across metrics, logs, deployments and configuration, explain their reasoning as a visible execution trace, and take action only with explicit approval.
What this solution addresses
- Generic AI assistants know DevOps in general and nothing about your estate.
- A model that cannot show its working cannot be used for an operational decision.
- Automation that acts without approval is a risk nobody signs off.
- Signal correlation is done by a human reading four dashboards.
- Incident knowledge lives with whoever was on call last time.
Answers come from platform queries, not from model recall.
The reasoning can be checked rather than trusted.
Agents propose scoped plans; people authorise them.
Stage by stage, with the modules that deliver each one
Every stage links to the modules that implement it, so the path from outcome to capability is explicit.
Detect without rules
Behavioural baselines and log pattern novelty surface conditions nobody wrote a rule for.
Explain the reasoning
Every conclusion arrives as an execution trace: the queries run, the sources hit, the evidence found.
Act with approval
Remediation is proposed as a scoped plan requiring approval, and every action is attributed and reversible.
Measure
Delivery, infrastructure, testing and experience scored from live data so improvement is evidenced.
Everything involved in this solution
ArkChat
Ask Ark about your own infrastructure
AI Log Analysis
Find the line that matters
Anomaly Detection
Detection without hand-written thresholds
360 DITE
Delivery, infrastructure, testing and experience in one score
Monitoring
Infrastructure and application monitoring
Alerting
Alerts that are worth waking up for
AI DevOps: frequently asked questions
Agentic DevOps describes software agents that observe an environment, reason about what they find, and take or propose action, as opposed to automation that only executes a script someone wrote in advance. The distinguishing property is that the agent decides what to investigate next.
AIOps generally means applying machine learning to operational data for detection and correlation. Agentic DevOps includes that and adds the ability to plan and carry out multi-step work: gathering evidence, forming a hypothesis, proposing a remediation.
Not by default. Ark proposes a scoped plan and requires approval. Specific vetted playbooks can be allowed to run automatically where you decide the risk is acceptable, and every execution is recorded and attributed.
You check the trace. Every conclusion is presented with the queries that produced it and the evidence they returned, which is why the trace is a first-class part of the interface rather than a debug view.
No. Your environment data is used to answer your questions and build your baselines. It is not used as training data.
No. It removes the data-gathering phase from the start of every incident, which is where most of the time goes. The judgement about what to do remains with the engineer, which is also why approval is required.
Background on this topic
What is agentic DevOps?
A precise definition of agentic DevOps, how it differs from scripted automation and from AIOps, and the conditions under which an agent is safe to give real access.
AI agents in DevOps: where they help and where they do not
A practical assessment of where AI agents genuinely improve infrastructure work, where they are oversold, and how to introduce them without creating a new class of incident.
AI-powered incident response: compressing the first ten minutes
Where incident time actually goes, which parts an agent can take over safely, and how to structure incident response so the automation helps rather than adds noise.
AI versus traditional automation: choosing the right one
A decision framework for when a deterministic script is the correct answer and when a reasoning agent earns its extra complexity and risk.
Talk through ai devops for your estate
A 30-minute conversation with a platform engineer about what you have and what would actually change.