Aegil is an autonomous SRE/DevOps agent for Kubernetes that uses deterministic log analysis, a chat interface, and safety guards to observe, diagnose, and remediate infrastructure issues.

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2026-07-13

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FR-AI / ANALYSIS

Why it is worth attention

It combines a language model with deterministic safety boundaries (command classifier, anti-looping, audit trail), enabling autonomous operations while keeping human control for destructive actions; the RCA pipeline works from verbatim log evidence.

Who it is for

  • SRE and DevOps engineers managing Kubernetes clusters
  • Platform engineering teams seeking automated incident response
  • Organizations with existing Loki/Grafana observability wanting AI-assisted root cause analysis
  • Security-conscious teams that need auditable, constrained automation

Use cases

  • Autonomous detection and remediation of pod/node failures in production clusters
  • Chat-based cluster troubleshooting without granting direct kubectl access
  • Root cause analysis from log aggregation with deterministic evidence
  • Safe automation of routine repairs like service restarts and disk cleanup

Strengths

  • Deterministic RCA pipeline normalizes logs and attributes every finding to verbatim log lines
  • Safety-by-design: dry-run mode, deterministic command classifier separate from the LLM, rate limits, and circuit breakers
  • Active learning improves the lightweight SetFit classifier from model-escalated cases
  • All tests run offline with no external dependencies, simplifying CI

Considerations

  • Early version (0.1.0) with incomplete parameterization and carried-over manifests that need review per cluster
  • Requires external Loki and a compatible LLM endpoint (vLLM, Ollama), adding deployment complexity
  • No Helm chart yet and the application-adapter contract remains undocumented

README quick start

Aegil

English | Русский

An autonomous Site Reliability Engineering (SRE) and DevOps agent for Kubernetes with deterministic log analysis. The product connects to any cluster and to logging and monitoring systems, observes state on its own, diagnoses problems from facts and remediates infrastructure issues, while the operator talks to it through an ordinary natural-language chat. The language model acts through real tools (cluster commands and node commands), but operates within deterministic bounds: a command-danger classifier, guards against looping, confirmation for irreversible actions, and a full audit trail.

The product was carved out as a standalone offering from an audio-processing platform where it grew (decisions ADR-0032 logging and RCA, ADR-0033 the panel, ADR-0037/0038 the autonomous agent, ADR-0041 the command executor and node-agent, see docs/adr). The extraction boundary is described in docs/BOUNDARY.md.

What it does

It observes the cluster (pods, nodes, events), the nodes (disks, processor, memory, processes via the privileged node-agent), the logs (Loki) and the traces (Tempo, optional). The deterministic Root Cause Analysis (RCA) pipeline normalizes logs, aggregates facts in a single pass, runs a catalog of detectors, computes confidence and assembles a verdict with an evidence registry in which every assertion rests on a verbatim quote from a log line. A lightweight trained SetFit classifier routes requests and is further trained on the hard cases escalated to the large model (active learning). Incidents are stored permanently with an explicit lifecycle. The chat agent understands an arbitrary request, inspects and repairs on its own, and explains every step.

Components

The product is four images plus an optional observability stack.

services/node-agent is a privileged DaemonSet that executes commands on node hosts through nsenter strictly as an argument list without a shell, is closed off by a token, and is not published externally. It gives the agent root on the nodes for disk cleanup, process inspection and memory inspection.

services/agent-panel is a FastAPI panel: the chat interface, the agentic tool-use loop (agent_exec), the deterministic policy classifier (policy), the anti-looping guards (guards), the incident center (incidents), the cluster client (k8s), the status summaries (status), an

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