XenonStack Case StudyAI Agents for Site Reliability in a Payments Platform, On-Premises

In brief

• The reliability team wanted to know whether AI agents could detect anomalies, correlate incidents and draft root-cause analyses on its own infrastructure, without sending logs to a public AI service.
• XenonStack deployed AgentSRE on-premises, connected to the client's existing Prometheus and Loki, using language models through private network endpoints only.
• The agents covered the full path from signal to ticket: observability, anomaly detection, correlation, root-cause analysis, remediation suggestions and Jira updates.

1. AgentSRE, deployed on-premises

The agent stack ran on the client's own Kubernetes environment, next to its user-acceptance-testing systems, not in a XenonStack cloud.

2. Connected to the monitoring the team already uses

AgentSRE read logs from Loki and metrics from Prometheus, so engineers needed no new tool and the agents worked from signals the team already trusts.

3. Private access to language models

The agents used large language models on Amazon Bedrock through private network endpoints only, so log data never crossed the public internet.

4. A chain of specialised agents

• Observability agent: answers questions about logs and metrics in plain language.
• Anomaly agent: spots unusual behaviour, with thresholds tuned during the engagement to cut noise.
• Correlation agent: groups related alerts and signals into one incident.
• Root-cause agent: drafts the analysis and suggests remediation for an engineer to approve.

5. Built into the incident workflow

Findings flowed into Jira as ticket and incident updates, so the agents worked inside the team's existing process rather than beside it.