DevOps Incident Agents

Multi-agent LLM systems for DevOps incident response automate the full incident lifecycle from failure detection through diagnosis and mitigation, with formal safety specifications enabling autonomous reliability engineering at cloud scale.

In cloud-scale systems, failures are the norm: distributed computing clusters exhibit hundreds of machine failures and thousands of disk failures, with software bugs and misconfigurations being even more frequent. Human-in-the-loop Site Reliability Engineering (SRE) practices cannot keep pace with modern cloud scale. Multi-agent incident response systems and STRATUS address this by deploying specialized LLM agents organized in state machines for autonomous, safety-aware failure mitigation.¹⁾

STRATUS is an LLM-based multi-agent system consisting of specialized agents organized in a state machine:²⁾

• Detection Agent: Monitors observability telemetry (logs, traces, metrics, system states) and identifies anomalies
• Diagnosis Agent: Performs failure localization and root cause analysis (RCA) using system context
• Mitigation Agent: Executes remediation actions through the cloud control plane
• Undo Agent: Provides rollback capabilities for safe exploration

Toolset: STRATUS integrates NL2Kubectl (natural language to Kubernetes commands), linting/oracles for validation, and telemetry collection across services.

STRATUS formalizes a key safety specification called Transactional No-Regression (TNR), which enables safe exploration and iteration:

<latex>\text{TNR}: \forall a \in \mathcal{A}_{mitigation}, \quad H(s_{after}(a)) \geq H(s_{before}(a))</latex>

where $H(s)$ is a system health function, $s_{before}$ and $s_{after}$ are system states before and after action $a$, and $\mathcal{A}_{mitigation}$ is the set of mitigation actions. TNR guarantees that no mitigation action makes the system worse than its pre-action state.

This is enforced through:

• Pre-action health checks: Alert clearance verification, system health baseline, workload assessment
• Post-action validation: Comparing health metrics after mitigation
• Automatic rollback: Undo agent reverses actions that violate TNR

The four-stage incident lifecycle automated by LLM agents:

1. Alert Verification and Log Retrieval: System automatically consumes alerts and gathers context via APIs
2. Knowledge Retrieval: AI accesses historical incident tickets, system logs, and runbooks via RAG
3. Reasoning and Root Cause Analysis: LLM correlates error messages with real-time logs
4. Action and Communication: System executes remediation and drafts notifications

<latex>\text{MTTR}_{agent} = t_{detect} + t_{diagnose} + t_{mitigate} \ll \text{MTTR}_{human}</latex>

from dataclasses import dataclass
from enum import Enum
 
class IncidentState(Enum):
    DETECTING = "detecting"
    DIAGNOSING = "diagnosing"
    MITIGATING = "mitigating"
    VALIDATING = "validating"
    RESOLVED = "resolved"
    ROLLED_BACK = "rolled_back"
 
@dataclass
class SystemHealth:
    error_rate: float
    latency_p99: float
    availability: float
 
    def score(self) -> float:
        return (self.availability * 0.5
                - self.error_rate * 0.3
                - self.latency_p99 * 0.2)
 
class StratusAgent:
    def __init__(self, llm, k8s_client, telemetry):
        self.llm = llm
        self.k8s = k8s_client
        self.telemetry = telemetry
        self.state = IncidentState.DETECTING
 
    def detect_failure(self, alerts: list[dict]) -> dict:
        self.state = IncidentState.DETECTING
        context = self.telemetry.gather_context(alerts)
        diagnosis = self.llm.generate(
            f"Analyze these alerts and telemetry:\n{context}\n"
            f"Identify the likely failure and affected services."
        )
        return {"alerts": alerts, "diagnosis": diagnosis}
 
    def mitigate_with_tnr(self, diagnosis: dict) -> bool:
        self.state = IncidentState.MITIGATING
        health_before = self.measure_health()
        action = self.llm.generate(
            f"Generate kubectl mitigation for:\n{diagnosis}"
        )
        self.k8s.execute(action)
        health_after = self.measure_health()
        if health_after.score() < health_before.score():
            self.k8s.rollback(action)
            self.state = IncidentState.ROLLED_BACK
            return False
        self.state = IncidentState.RESOLVED
        return True

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