Technology and Infrastructure → AI-Ops and Autonomous Systems

AI-Ops and standalone systems

1) What is AI-Ops

• detect problems earlier (counterposition of incidents),
• auto-remediation
• optimize cost and performance (predictive scaling, intelligent routing),
• speed up the analysis of incidents (signal correlation, generation of post-mortems).

1. feel (collect telemetry),

2. understand (models, heuristics, rules),

3. act (make safe changes in the product by ranbooks),

4. learn (close the feedback loop through post-incident analytics).

2) Layers of AI-Ops architecture

1. Telemetry collection (Observability 3-in-1): metrics (Prometheus/OTel), logs (Loki/ELK), trails (OTel/Jaeger).
2. Enrichment and feature engineering: aggregations, sliding windows, seasonality, business tags ('partner', 'game _ id', 'region', 'VIP', 'psp').

• anomaly detection (STL, Prophet, Isolation Forest, auto encoders),
• cause and effect graphs (dependency correlations),
• incident classification and prioritization (ML + SRE domain rules).
• 4. Solutions and actions: running books, auto retras, traffic switching, auto-scaling, changing limits, follow-routing.
• 5. Human-machine circuit: LLM-copylot for NOC/SRE, chat commands, "what-if" simulations.
• 6. Governorship and safety: approvals, change windows, catastrophic stop conditions, audit.

3) Data sources and features

Infrastructure: CPU/Memory/IO/Latency, network errors, K8s pods/nodes, limits/requests, node pressure.
Services: RPS/P50/P95/P99, 4xx/5xx, saturations, retray errors, circuit-breaker events.
Business signals: registratsiya→depozit (CR), GGR/Net Deposits, PSP failure by code, VIP traffic, tournaments, promotional campaigns.
Extra factors: releases/feature flags, regulatory reports, bank payment windows, matches/events (betting peaks).

Useful features: weekly/hourly seasonality, lags, rolling stats, rate-of-change, error-mix by codes, versiya→versiya deltas, saturation-score.

4) Application cases

4. 1 Early detection of incidents

Abnormal growth of 'psp _ decline _ rate' failures in the region → an automatic failover to a backup PSP, limited by segments (not touching VIP).
A non-standard pattern of trace delays in the 'web → gateway → wallet' chain → auto-encrypt traffic to healthy pods, warm up the cache, restart degrading instances.

4. 2 Predictive Capacity Management

RPS forecast, taking into account match schedules and promo → proactive scaling of K8s, warming up connections to the database/cache, cloud billing quotas.
Savings: reduced off-peak oversizing while maintaining SLO.

4. 3 Self-healing

Stuck payouts queue error → runbook: consumer pause, deduplication, DLQ process, idempotency control.
Degraded shard DB → evacuation of reads, switching writer, throttling background jobs.

4. 4 Correlation and RCA

ML-clustering of alerts (alert storms) + causal graphs → one "incident card" instead of dozens of messages.
Auto-generation of incident timeline and postmortem draft.

4. 5 Copilot for NOC/SRE (LLM)

Command: "show everything that changed 15 minutes before the 5xx spike by/v2/payouts in the EU region."

Answer: diff configs, release notes, metric deltas, affected pods, hypotheses + "start ranbook" buttons.

5) Decision patterns

Safe-automation: action only in the "green corridor" (gardrail: max% of traffic, max scale pitch, list of allowed commands).
Human-in-the-loop: critical steps (switching the master database, mass phicheflags) - with on-call confirmation.
Multisignality: trigger is not one alert, but consistency: metrics + trails + log pattern + release sign.
Canary remediation: first apply to 1-5% of traffic, then escalate.
Rollback-by-design: Each action has a backward step and timeout.

6) Runbooks and playbooks

Runbook structure: condition → verification of → action → validation → rollback → log.

• PSP waiver> X% in country Y: switch to route B, lower 'retry _ budget', activate limit cache, open ticket to PSP.
• Growth of latency in wallet service: increase replicas, warm up Redis keys "limits," enable "read-only" mode for heavy reports, limit third-party aggregations.

7) Models: Simple to Mature

1. Basic rules and STL seasonality: fast start, few folk positives.
2. Supervised incident-history models: criticality/subsystem classifier, RCA hint.
3. Unsupervised/Deep: Autencoder/Isolation Forest for complex patterns.
4. Policy-Learning: training of remediation policies (offline simulations + limited online experiments).

Important: models ≠ magic. Do hindsight, drift control, champion-challenger, and keep features/labels.

8) A/B and experimentation in operations

Experiments on operational solutions: different retray/timeout strategies, PSP routing, connection limits.
Success metrics: MTTR, error budget burn, cost-per-RPS,% of folk autofixes.
Stop conditions and quick stop during SLO degradation.

9) Governorship, risk and compliance

Action policy: list of permissible automations, risk areas, change windows, approval levels.
Audit and tracing: who/when/why launched the ranbook; artifacts for postmortem.
PII/PCI: masking in features/logs, data minimization, secret scans.
Regulation iGaming/fintech: transparency of decisions (explainability), the logic of payment stops/limits should be reproducible and explainable.

10) Tools (reference stack)

Observability: OpenTelemetry, Prometheus, Grafana/Tempo/Jaeger, Loki/ELK.
Catalogs and knowledge: service catalog, graph dependency, inventory of configs/phicheflags.
ML-pipelines: Feature Store, offline-DWH + online features, model-register, CI/CD models, drift-monitoring.
Automation: orchestrator of ranbooks (Argo/StackStorm/自opisnyye), K8s operators, GitOps (Argo CD/Flux).
Incidents: chat ops (Slack/Telegram/Teams), watch bots, post-mortem templates.
Security: Vault/KMS, key policy, mTLS, artifact signature.

11) AI-Ops Maturity Metrics

Detection: proportion of incidents seen before user complaints; average detection lead.
Reaction: MTTA/MTTR,% auto-remediation without escalation, RCA quality (precision/recall).
Reliability: burn-rate, SLO adherence, alerts per on-call hour.
Economy: saving $ on calculations (rightsizing), reducing deviations from the budget, cost-per-transaction.
Culture: share of incidents with post-mortems, coverage of services with runbooks, speed of implementation of rules.

12) Step-by-step implementation plan

1. Telemetry and Unified Signal Dictionary. Required labels: 'service', 'version', 'region', 'partner', 'api _ version'.
2. Anti-noise and correlation. Alert deduplication, grouped by incident.
3. Runbook library. Scenarios for top 10 risks (payments, wallet, game catalogs, tournaments, reports).
4. Primary models. STL/Prophet + rules; pilot on 2-3 services.
5. Copilot and chat ops. Natural requests, quick actions, post-mortem templates.
6. Gardrails and control. Canaries, action limits, audit trail.
7. Experiments and training. Champion-challenger, A/B, retrospective benefit assessment.
8. Scaling. Connecting all critical streams, training teams, SLO review.

13) Examples of policies and configs

13. 1 Auto-scaling policy (idea)

Proactive scaling at RPS forecast> + X% P95 last week.
Cold start: warming up connections to Redis/PSP, warming up caches.
Stop Condition: 5xx error grows after skale → rollback.

13. 2 "PSP degradation" runbook

1. Check 'psp _ error _ rate> T' and 'region in {BR, TR}'.
2. Enable smart-routing on PSP-B for non-VIP only; limit'max _ retries = 2 '.
3. Create PSP-A ticket; collect 100 requests/responses for RCA.
4. Monitor CR deposit and T2W (time-to-wallet). Rollback on degradation> Y%.

13. 3 Postmortem template (autogenerated)

Detector → Timeline → Hypotheses → Impact (Users/Revenue) → Actions → Lessons → Changes to the Runbooks/Models.

14) Anti-patterns

"Black box" without gardrails: bots rule the sale without limits and audit.
Models without data on business events: see CPU, but do not understand promo/matches.

Alert storms: lack of correlation → on-call "tunnel vision."

Auto-remediation without rollback/validation of the result.
"Eternal pilots": there is no way out to real actions, only deshboards.
Lack of post-mortems - no training system.

15) iGaming context/fintech

Load peaks (tournaments, live bets, finals): predictive scaling, warming up caches, preparing PSP limits.
Responsible Games/Limits: Models must not lift player limits automatically without matching rules.
Regulatory reporting windows: download schedules, download SLAs, queue priority.
Multi-PSP: dynamic routing by country, time of day, error codes, transaction cost.
VIP segment: individual gardrails - no aggressive actions without confirmation (human-in-the-loop).

16) Readiness checklist

1. A single layer of OTel, unified labels and tracks through the gateway.
2. Service topology.
3. Catalog of runbooks with simulations and unit tests.
4. At least one model of anomalies in the product + quality report.
5. Chat copilot capable of reading logs/metrics and launching safe actions.
6. Gardrails, canaries, kickbacks, change audits.
7. Regular post-mortems and updating of knowledge/models based on the results.

Result

AI-Ops is not a "magic AI on top of the logs," but a discipline: high-quality telemetry, understandable ranbooks, cautious automation and controlled models. By introducing a loop of observing → understanding → acting → learning with clear gardrails, you get a self-healing platform that notices risks earlier, recovers faster and costs the business less.