AI SRE agents: incident context and diagnosis before the team joins the call
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An AI SRE agent gathers logs, metrics, recent changes and incident history to investigate critical alerts. It presents hypotheses, recommends runbooks and executes only authorized automation. Loonar designs, deploys and supports agents connected to observability, on-call workflows and team chat, with practical training and explicit boundaries for autonomous action.
In short
- Every hypothesis needs traceable evidence.
- Autonomy follows risk and authorization.
- Every action must leave an audit trail.
How do you know you need AI SRE agents?
Starting investigations from scratch
An overnight alert wakes the responder without enough context to understand service health. Basic queries must be repeated before impact can be assessed, while pressure to provide an update competes with the investigation itself.
Context scattered across tools
Logs, metrics, change records and incident history live in different systems with inconsistent identifiers. Responders spend valuable time gathering evidence and checking relationships that could already be prepared, while relevant signals age between screens.
Knowledge held by a few experts
Only a few engineers understand service dependencies and recurring failure patterns. When they are unavailable, response relies on trial and error or informal messages, making it harder to share on-call duties without increasing uncertainty and fatigue.
Runbooks that drift
Procedures become outdated as architecture, permissions and commands change. Following a familiar instruction can worsen an incident when nobody owns the content or checks its assumptions, particularly during a high-pressure response outside normal working hours.
How does it work?
Workflow-triggered triage
Start context collection from incident workflows. The agent identifies the affected service, severity and owners, recording missing information before it recommends the next investigative step to the human responder.
Evidence-backed hypotheses
Correlate operational signals with recent changes to propose likely causes. Show sources, timestamps and analytical limits, separating observed evidence from inferences that still require validation by the response team.
Controlled runbook execution
Recommend procedures and execute approved automation according to risk. Sensitive actions require human authorization, precondition checks and explicit criteria for stopping or rolling back execution if expected conditions fail.
Response inside team chat
Bring context and approval requests into Slack and Microsoft Teams. Preserve responder identity and link chat decisions to incident records so that operational commands remain attributable and traceable during response.
Incident timeline
Organize events, queries, decisions and actions into a verifiable sequence. Produce a postmortem draft for human review without presenting the agent’s working hypotheses as confirmed causes of the incident.
MCP and API integrations
Connect observability, CI/CD and cloud systems through authorized interfaces. Restrict tools and credentials by service, protect sensitive data and record queries and actions for later operational review and evaluation.
How does Loonar deliver?
Service and runbook assessment
Loonar maps services, owners, signals and existing procedures. We identify gaps in observability and operational knowledge, prioritizing scenarios with sufficient evidence and clear criteria for assessing the agent’s assistance.
Integration and autonomy design
We define context sources, permissions and autonomy levels by action. The design establishes human approval, execution boundaries, data handling and stop conditions before connecting the agent to production systems.
Deployment and controlled pilot
We connect observability, on-call workflows and chat for selected services. The pilot starts with queries and recommendations, comparing hypotheses against human analysis before enabling automation with greater operational risk.
Training and operational validation
We train responders to review evidence, approve actions and challenge recommendations. Exercises validate runbooks, escalation and agent suspension, preparing the team to work with assistance without becoming dependent on it.
Expansion and continuous improvement
We expand scope according to observed quality, safety and usefulness. Integrations, permissions and procedures are reviewed after incidents, with recurring evaluation of recommendations and the effects of authorized production automation.
Which technology does Loonar use to deliver it?
What results do we measure?
- MTTA
- Measures time from alert to incident acknowledgment using an agreed definition.
- MTTR
- Tracks time to service restoration across the incidents included in the assessment.
- Diagnosis time
- Records the interval until a useful, validated hypothesis guides the response.
- Incidents resolved with automation
- Counts confirmed resolutions involving authorized automation.
- Repetitive work hours eliminated
- Estimates avoided effort by comparing equivalent tasks before and after assistance.
How much autonomy should an AI SRE agent have?
Match autonomy to action risk, evidence quality and the ability to reverse changes.
| Aspect | Assisted | Supervised | Autonomous |
|---|---|---|---|
| Investigation | Collects context | Investigates and proposes actions | Investigates within authorized scope |
| Execution | Human executes | Agent executes after approval | Runs preauthorized routines only |
| Approval | Human decides each action | Human approves execution | Previously approved policy limits actions |
| Exceptions | Hands off to a responder | Stops and requests a decision | Stops when boundaries are exceeded |
Where do we apply it?
Financial services
Financial operations teams can use an agent to assemble critical service context without delegating sensitive decisions. Queries follow access and data handling rules. Recommendations include evidence, while production changes retain approval and recording requirements, allowing faster investigation without confusing analytical speed with permission to act on customer-facing systems.
Retail and e-commerce
Campaign traffic makes checkout failures especially time-sensitive. An agent collects dependency signals and recent changes, organizing hypotheses for the on-call responder. Approved runbooks can handle repetitive checks, while decisions about rolling back releases or changing capacity remain subject to the boundaries established for each service and operating environment.
SaaS
SaaS businesses run distributed services with shared dependencies across engineering teams. The agent links alerts to available history and changes, preparing an investigation with verifiable sources. Its timeline supports later review, while execution boundaries separate environments, customers and permissions to prevent assistance for one incident from affecting unrelated services.
Frequently asked questions
What is an AI SRE agent?
It is software that uses AI to investigate operational signals, gather context and support incident response. Unlike a fixed script, it can develop hypotheses from authorized sources, but must expose evidence and limitations. Loonar designs, deploys and supports the AI SRE agent, training responders to evaluate recommendations while keeping people accountable for operational decisions and the resulting production changes.
Can AI execute fixes in production?
It can run authorized routines when technical and governance conditions are defined. That does not mean unrestricted access or permission to improvise commands. Every action needs a scope, least-privilege access, preconditions and recorded outcomes. Sensitive changes require human approval. Start with investigation and recommendations, then expand execution only after validating behavior and mechanisms for stopping unsafe or unexpected activity.
How do we prevent incorrect actions?
There is no guarantee of error-free behavior. The design limits exposure through permitted tools, restricted credentials, input validation and risk-based approval. It also separates retrieved content from authorized instructions, preventing logs or documents from expanding the agent’s permissions. Adversarial scenario tests, execution boundaries and evidence review help expose failure modes before the team grants broader operational autonomy to the agent.
Do we need mature observability?
You do not need perfect coverage to begin, but the agent needs accessible, trustworthy signals. Without service identifiers, consistent timestamps and change context, its hypotheses will be limited. Assessment selects services with enough evidence for a useful pilot and identifies gaps to address. An agent does not replace instrumentation, clear service ownership or disciplined incident recording and review practices.
What is MCP and why does it matter?
MCP is a protocol that connects AI applications to tools and context sources through structured interfaces. For incident response, it can simplify controlled access to queries and procedures. It does not automatically provide security: every integration still needs authentication, authorization, validation and auditing. Its value depends on the exposed tools and the policies governing how an agent can use them.
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