Autonomous Incident Engineer

01 / THE PROBLEM

Production alerts often overwhelm SRE teams with fragmented telemetry, logs, and distributed traces. On-call engineers spend critical initial minutes manually correlating metrics, traversing dependency graphs, and determining whether an anomaly is transient or indicative of an architectural failure.

02 / APPROACH & WHY I BUILT IT

Built to automate the initial triage and root cause isolation workflow using deterministic agentic loops rather than unpredictable free-form chatbot prompting.

The architecture decouples telemetry ingestion, state machine reasoning, tool execution, and remediation planning into isolated asynchronous stages. The core agent evaluates incoming alerts against runtime telemetry (Prometheus, OpenTelemetry spans), verifies pod and container state, and queries service dependency maps.

03 / HOW THE SYSTEM WORKS

1. 01.Ingests alerts via webhook or event stream with metadata and cluster context.
2. 02.Constructs an incident state graph and identifies suspect downstream and upstream services.
3. 03.Executes read-only diagnostic tools (log aggregation queries, thread dump inspections, metric time-series diffs).
4. 04.Correlates anomalies against known failure signatures (memory exhaustion, connection pool exhaustion, cascading timeouts).
5. 05.Produces a structured diagnostic report with verifiable root-cause findings and reproducible remediation runbooks.

04 / KEY ENGINEERING DECISIONS

• ✦Strict tool isolation: Diagnostic tools operate with read-only permissions to prevent hallucinated destructive commands in production environments.
• ✦Deterministic state graph: Used a directed acyclic graph (DAG) for diagnostic steps to enforce systematic triage sequences instead of unconstrained LLM loops.
• ✦Structured schema outputs: All intermediate deductions and final incident reports are validated against strict Pydantic schemas before persistence.