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The model, the serving layer, and the agent asking questions all sit on local hardware. The first thing it debugs is the AI stack it is running on. No external inference provider in the path.

Give one agent every tool and every incident is a question of trust. This one hands each job to a worker that can only reach what that job needs. One AURA configuration defines a coordinator and three specialist workers. Qdrant stores the runbooks, Prometheus measures workload health, and Kubernetes provides inspection and remediation, and each of the three is wired to one worker.

An on-call question returns the threshold and the escalation owner from your own runbooks, and the answer comes back without a call to anyone outside. AURA runs against Bedrock as its model provider, using Claude Sonnet 5 served by AWS in the same region. Authentication is the normal AWS credential chain: a profile on a laptop, an IAM role in EKS.

The platform shows you which service is failing and which paths it touches, and stops there. Point AURA at the same telemetry and the cause comes back too. Dash0 shows the product catalog service in a failed state across the selected window, with errors on the path from the frontend service.

You ask for a routine health check and expect a clean baseline. What came back was a pod that had restarted 788 times, unrelated to the question. AURA is connected to a Kubernetes cluster and to Prometheus through read-only MCP servers, running as one coordinator with two specialized workers. The prompt is one sentence: check the health of the cluster, and confirm whether all the pods are running. What comes back is not a baseline. AURA names the state as CrashLoopBackOff and attaches the restart count to it.

Knowing why it broke is not the same as having it repaired. Point the agent at the repos behind the service and the change comes back as a pull request. A Govee integration crash-loops under Home Assistant because the container cannot write to a directory it does not own. That much was already established: the previous homelab video stopped at the root cause on purpose, so the next pass could improve the agent's configuration first.

A deployment fails validation and the sync stops. Argo CD hands the report to AURA, which finds the wrong version, fixes it, and re-runs the sync. Normally, a failed sync means a person opens the application, reads the hook logs, and works out which value is wrong. Here, the sync fail hook sends AURA a short failure report and an incident ID over the agent-to-agent protocol, then exits. It does not say how to investigate or what to change.

A scheduled Lambda quietly stops completing and nothing pages you. AURA finds the function, reads its logs, and comes back with a three-second timeout. The usual path is opening the console, tracking down the right log group, and reading CloudWatch by hand. Here AURA connects to AWS through the MCP proxy AWS publishes, run locally with uvx against an AWS CLI that is already configured, so there are no new credentials to issue.

Some agent work should pause for a person. This is the other case: a health check every two minutes, one bounded action, and a result nobody approved. Each scheduled run starts the normal AURA image in one-shot mode: check one workload, act if something is wrong, write the result to the job log, and exit. Overlapping runs are forbidden.

A coworker says checkout is broken and nothing else. That is the whole prompt. AURA reads the live logs and comes back with the payment service. Normally a message like this is the start of guessing at a service and opening dashboards until something looks wrong. Here it is the entire input: no service named, no error string, no time range.