Operations and Management → AI helpers for operators

AI helpers for operators

1) Why do you need it

Operators drown in alerts, logs and scattered artifacts. The AI ​ ​ assistant turns heterogeneous signals into understandable recommendations and ready-made actions: faster triages, less manual routine, higher predictability of SLO.

• Reduce MTTD/MTTR and alert noise.
• Improve the quality of handovers and post-incident documentation.
• Automate "heavy routine" (search for context, summary, tickets).
• Record common response/communication standards.

2) Application scenarios (Top-12)

1. Triage of incidents: grouping of alerts → hypotheses of causes → priority/impact.
2. Action Hints: "what to do now" with links to the runbook and launch buttons.
3. Auto-summaries (Incident TL; DR): a brief squeeze for the Incident Channel/Stakeholders.
4. Knowledge Search (RAG): quick answers by runbook/SOP/postmortems/escalation matrix.
5. Generating tickets/updates: drafts of Jira/Status updates using a template.
6. Alert analytics: identifying "noisy rules," tuning suggestions.
7. Observability Q&A: "show p99 bets-api in 1h" → ready-made graphs/requests.
8. Vendor context: provider summary (quotas, SLAs, windows, incidents).

9. Predictive hints: "burn- rate↑ + lag↑ → prepare a PSP feiler."

10. Handover Copilot: collecting a shift package from dashboards/tickets.
11. Postmortem Copilot: chronology from logs/threads + draft Corrective/Preventive Actions.
12. Localization/tone of messages: correct, consistent client updates.

3) Solution architecture (high-level)

Sources: metrics/logs/trails (Observability), tickets/incidents, configs/phicheflags, provider statuses, SLO/OLA directory, runbook/SOP.

RAG layer (knowledge search): indexing documents with markup (domain, version, date, owner). Vyuhi "for operator."

Tools/Actions: safe operations: "scale-up HPA," "canary pause," "enable safe-mode," "switch PSP," "create ticket," "collect charts." All actions are through a broker/orchestrator with an audit.
Policy-guardrails: rights by role, HITL confirmation, limits, dry-run, magazine.
Security: KMS/Secrets, PII masks, mTLS, data access audit.
Interfaces: chat/panel in NOC, widgets in dashboards, slack slash commands.

4) UX patterns (what the operator sees)

Incident cards: "symptom → hypothesis (ranked) → 3 proposed steps → links to data → action buttons."

Single prompt field: "Form a handover packet in the last 4h for Payments."

Highlighting confidence/sources: "based on: Grafana, Postgres logs, Runbook v3."

"Dry-Run" button: show what will be done and where the risks are.
Decision history: who confirmed the step, result, rollback/success.

5) Integrations and actions (examples)

Observability: ready-made PromQL/LogsQL/Trace filters, graphs by pressing.
Feature Flags: enable safe-mode/roll back the flag (with confirmation).
Release-canary: pause/roll back; annotate the graphs.
K8s: pre-scan HPA, restart daemon, PDB/Spread check.
Providers: switching route PSP-X → PSP-Y; checking quotas.
Communications: draft update to incident channel/status page.
Tickets: Creating a Jira with pre-filled sections.

6) Security and privacy policies

Access by roles/domains: the operator sees only "his" systems and minimally sufficient data.
Action log: who/when/what confirmed, outcome, rollback.
PII/secrets: masking in answers/logs; inaccessibility of "raw" secrets.
Content storage: Versions of extracted artifacts (RAGs) with TTL and labeling.
The prohibition of "reasoning" as an artifact: we preserve conclusions and references to sources, and not internal reflections of the model.
Vendor-boundaries: a clear list of data leaving the perimeter (zero by default).

7) Quality and performance metrics

• MTTD/MTTR ↓, Pre-Incident Detect Rate ↑, Change Failure Rate ↓, Handoff Quality Score ↑.
• Alert Fatigue ↓, time to first update ↓.

• Acceptance Rate, Time Saved/Case, Precision/Recall by class (e.g. P1), Hallucination Rate, Safety Incidents = 0.

• Recall(P1) ≥ 0. 7, Precision ≥ 0. 6, Acceptance ≥ 0. 5, Time Saved ≥ 25%, Hallucination ≤ 2% with mandatory references to sources.

8) Industrial Engineering and Knowledge Management

Query templates: standardize the wording (examples below).
Context layers: (a) system rules (security, response style), (b) brief switch/domain context, (c) RAG search on fresh documents/schedules.
Knowledge versioning: each runbook/SOP has an'id @ version' and a date, AI issues a link and a version.
Validation of responses: require reference to data sources/dashboards for all factual statements.

Triage:
"You are an SRE operator. Based on [Grafana: payments, Logs:psp_x, Incidents: last 24h]
group alerts into 3-5 hypotheses with probability, effect on SLO, and brief validation steps.
Answer: hypothesis cards + links"

Handover:
"Collect handover packet in last 4h for Payments domain:
SLO, incidents (ETA), releases/canaries, providers/quotas, risks/observations, action items.
Add links to panels and tickets"

9) Process embedding (SOP)

Incidents: AI publishes TL; DR every N minutes, prepares the next ETA, suggests steps.
Releases: pre- and post-report summaries; autogate at predictive risks.
Shifts: Handover package is formed and validated according to the checklist.
Postmortems: draft by timeline + Corrective/Preventive Actions list.
Reporting: A week-long digest of noisy alerts and tuning suggestions.

10) Dashboards and widgets (minimum)

AI Ops Overview: accepted recommendations, time saved, success/rollback of actions.
Triaging Quality: Precision/Recall by Class, Controversial Cases, Top Bugs.
Knowledge Health: runbook/SOP coverage, legacy versions, spaces.
Alert Hygiene: noise sources, tuning rule candidate.
Safety & Audit: log of actions, failed attempts, dry-run reports.

11) Anti-patterns

"The magic box will solve everything" - without RAG and links, with "guessing" facts.
Automate irreversible actions without HITL/roles/limits.
Blending of prod/stage artifacts in search.
Secrets/PII in the assistant's answers and logs.
Lack of quality metrics and post-benefit assessment.
"One chat for all tasks" - without cards, statuses and action buttons.

12) Implementation checklist

• Domains and scripts (triage, summaries, handover, tickets) are defined.
• RAG configured: runbook/SOP/postmortem/escalation matrix index (with versions).
• Integrations: Observability, Flags, Release, Tickets, Providers - through secure tools.
• Policies: roles, HITL, log, dry-run, PII/secret masking.
• UX: Incident cards, action buttons, confidence, and links.
• Metrics: AI-KPI and Ops-KPI + dashboards.
• Processes: SOPs for incidents/releases/shifts/post-mortems involving AI.
• Operator training plan and "communication rules" with the assistant.

13) Examples of "safe" auto-actions

TL publication; DR/ETA to Incident Channel.
Creating/updating a ticket, linking artifacts.
Generation/launch of reading metrics and logs (without changes in the system).
Annotations of releases/flags on graphs.
Preparing the playbook dry-run (which will be done upon confirmation).

14) Roles and responsibilities

Ops Owner: business outcomes (MTTR, noise), SOP approval.
Observability/SRE: RAG, integrations, safety and quality metrics.
Domain Leads: validation of recommendations, relevance of runbook/SOP.
Training/Enablement: onboarding operators, "how to communicate with AI," exams.
Compliance/Security: data policy, audit and log storage.

15) 30/60/90 - start-up plan

• Pilot on one domain (for example, Payments): triage, TL; DR, tickets.
• Knowledge Indexing (RAG) and Incident Cards, dry-run activities.
• Basic metrics: Acceptance/Time Saved/Precision/Recall.

• Add handover/postmortem copilot, integration with Flags/Release.
• Include predictive hints (burn-rate, lag) and alert tuning suggestions.
• Spend two game-days using the assistant.

• Extension to Bets/Games/KYC, unification of templates.
• Formalize SOPs with AI, enter KPIs in quarterly targets.
• Economic effect optimization (cost/incident, overtime reduction).

16) Examples of assistant responses (formats)

Symptom: p99 payments-api ↑ up to 420 ms (+ 35%) in 15 minutes
Hypotheses:
1) PSP-X timeouts (probable 0. 62) - outbound_error_rate growth, quota 88%
2) DB-connections (0. 22) — active/max=0. 82
3) Cash evikshens (0. 16) — evictions>0
Steps:
[Open PSP-X panel] [Check quota] [Enable safe-mode deposit]
[Payments-api canary pause]
References: Grafana (payments p99), Logs (psp-x), Runbook v3

SLO OK/Degraded, incidents: INC-457 ETA 18:30, canary bets-api 10%, PSP-X quota 85%.
Action items: @ squad-payments check out the feilover before 7 p.m.

Impact: deposit conversion − 3. 2% at 5pm-5.25pm
Timeline: 16:58 alert p99; 17:04 canary pause; 17:08 PSP- X→Y
Root cause: slow PSP-X responses when 90% quota is reached
Actions now: breaker tuning, auto-predictor quota> 0. 85, alert hygiene

17) FAQ

Q: What to automate first?
A: Briefs/tickets/knowledge search - safe and immediately saves time. Then - predictive clues and semi-automatic actions with HITL.

Q: How to deal with "hallucinations"?
A: Only RAG, only answers with links, prohibition of answers without sources, offline quality assessment, controversial answers to mark and disassemble in retro.

Q: Is it possible to give an assistant the right to "press buttons"?
A: Yes - for reversible and low-risk steps (annotations, summaries, dry-run, pre-scale), the rest - through HITL and roles.