AETHERIS AI — Autonomous Incident Resolution

Problem When production incidents occur, developers still follow a manual treadmill: investigate logs and traces, write a patch under pressure, hope tests catch regressions, wait for review, deploy, and frequently roll back. The process is stressful (often at 3 a.m.), error-prone under fatigue, and leaves almost no verifiable audit trail of what changed and why. Mean time to recovery stays high and risk of secondary failures remains real. Solution AETHERIS AI is a safety-gated autonomous repair pipeline for production incidents. It is deliberately not “AI writes patches.” It is “AI resolves incidents safely — or knows when to stop.” The system runs a deterministic state machine: Incident → Diagnose → Patch → Test → Audit → Gate → Canary → Rollout or Quarantine. Three hard constraints keep autonomy bounded: 1. 3-attempt hard cap — after three failed repair attempts the incident is quarantined and handed to a human with full evidence. 2. HMAC-signed evidence at every step — diagnosis, patch, test results and gate decisions are cryptographically tagged so the release gate can verify integrity before any rollout. 3. Deterministic safety gates — GO / NO-GO is decided by explicit policy checks (test pass rate, audit trust, security/regression risk, evidence validity), not by an LLM’s opinion. Two live demo paths prove the contract: • Safe Repair (NULL_ERROR) — diagnosed, patched with a defensive null-guard, tests passed, audit signed, canary healthy → ROLLED_OUT. • Controlled Failure (SCHEMA_DRIFT) — three progressive failures, gate blocked, attempt limit reached → QUARANTINED with complete evidence for human review. Vague or non-actionable free-text input is rejected with a clear “needs clarification” response instead of producing a fake successful repair. Target users SRE, platform and backend engineers who own production services and want faster recovery without sacrificing safety or auditability. How users interact A dark-themed enterprise dashboard lets users launch preset scenarios or paste a real error description / stack trace. The full pipeline runs in real time; every state transition, Bob activity log, patch diff, test result, audit finding and cryptographic evidence is visible. Users can verify the HMAC signature and reset demos. What makes it unique Most “AI coding” demos stop at generating a patch. AETHERIS treats the patch as only one step inside a closed-loop, fail-closed, evidence-backed repair system. Autonomy is bounded by policy and attempt limits; humans remain in control through quarantine and full audit trails. The system was built with IBM Bob IDE as the primary engineering partner and uses watsonx.ai / Granite patterns for the diagnose–patch–audit agents at runtime. Impact Minutes instead of hours for many classes of incident, elimination of blind “ship and hope” rollouts, and compliance-ready auditability by default.