# Andrey Lesnikov — Technical Case Studies

## Automatic SaaS Restore System

- Problem: Restores are high-pressure, manual, and easy to execute inconsistently.
- Constraints: Cloud state, safety gates, auditability, and rollback clarity matter.
- Architecture: Repeatable restore workflow with dry-run visibility, status checks, and operational handoff.
- Result: Recovery becomes a platform capability instead of an emergency script.

## Cloud-Native AI Gateway

- Problem: AI usage needs routing, policy, budget awareness, and provider resilience.
- Constraints: Latency, observability, prompt safety, rate limits, and failover behavior.
- Architecture: Gateway layer for model selection, request shaping, telemetry, and controlled fallback paths.
- Result: AI becomes operable infrastructure, not an opaque API call.

## Kanister Backup Restore

- Problem: Application-aware Kubernetes restores need more than volume snapshots and manual runbooks.
- Constraints: Stateful services, namespace boundaries, object storage retention, test restores, and auditable recovery steps.
- Architecture: Kanister blueprints coordinate backup actions, restore actions, validation hooks, and operator handoff around Kubernetes workloads.
- Result: Restore behavior becomes repeatable, reviewable, and easier to exercise before an incident.

## GitOps ArgoCD Flux

- Problem: Teams need a clear delivery model before GitOps becomes another layer of operational confusion.
- Constraints: Multi-environment promotion, drift detection, rollback safety, secret handling, and developer feedback loops.
- Architecture: Comparison of Argo CD and Flux reconciliation patterns, sync ownership, policy boundaries, and platform team responsibilities.
- Result: GitOps decisions become explicit platform contracts instead of tool preference debates.

## SBOM Integration

- Problem: Software supply-chain data is often generated late, stored separately, and disconnected from deployment decisions.
- Constraints: CI/CD speed, artifact provenance, vulnerability context, policy gates, and developer-readable remediation feedback.
- Architecture: SBOM generation in the pipeline, artifact attachment, vulnerability enrichment, policy evaluation, and release evidence storage.
- Result: Supply-chain visibility becomes part of the delivery system, not a quarterly compliance export.

## LLM Infrastructure Runtime

- Problem: LLM workloads move faster than traditional platform controls and can quickly become expensive, opaque, and hard to operate.
- Constraints: GPU/CPU placement, model latency, token cost, prompt boundaries, provider limits, data privacy, and fallback behavior.
- Architecture: Runtime layer with model routing, request budgets, telemetry, policy checks, provider abstraction, and operational dashboards around inference flows.
- Result: LLM usage becomes a controlled platform capability with observability and operating contracts instead of isolated API calls.

## RAG Knowledge Platform

- Problem: Engineering knowledge is spread across repositories, runbooks, tickets, architecture notes, and project history.
- Constraints: Source freshness, citation quality, chunking, access boundaries, hallucination control, and explainable answers.
- Architecture: Curated ingestion pipeline with markdown exports, project metadata, embedding-ready documents, source references, and fallback local answers.
- Result: The AI assistant can answer infrastructure questions with project context, sources, and a safer boundary around what it knows.

## EKS Platform Foundation

- Problem: Kubernetes clusters become inconsistent when networking, identity, ingress, storage, and observability are assembled per project.
- Constraints: AWS account boundaries, workload identity, node lifecycle, ingress policy, autoscaling, logging, and upgrade safety.
- Architecture: EKS foundation with Terraform modules, baseline add-ons, workload identity, GitOps bootstrap, default observability, and controlled environment overlays.
- Result: Clusters become a repeatable platform product rather than a one-off infrastructure build.

## OpenTelemetry Observability Mesh

- Problem: Metrics, logs, and traces often exist separately, making incidents slower and ownership unclear.
- Constraints: Cardinality, sampling, cost, multi-service correlation, dashboard sprawl, and alert fatigue.
- Architecture: OpenTelemetry collection layer with service conventions, trace context propagation, metric normalization, log correlation, and dashboard/runbook links.
- Result: Production behavior becomes easier to understand from request path to workload to infrastructure signal.

## SLO Driven Monitoring

- Problem: Dashboards can look healthy while users experience latency, errors, or degraded workflows.
- Constraints: Service ownership, error budgets, burn-rate alerts, noisy dependencies, and product-facing reliability language.
- Architecture: SLO model with user-centric indicators, burn-rate alerts, Grafana-style views, incident thresholds, and runbook context near alerts.
- Result: Monitoring shifts from raw infrastructure charts to reliability decisions teams can act on.

## Multi Region GitOps

- Problem: Multi-region systems need repeatable promotion and rollback without turning every deployment into manual coordination.
- Constraints: Regional overlays, failover state, secret distribution, traffic switching, drift, and environment-specific policy.
- Architecture: GitOps layout with regional overlays, promotion gates, sync waves, health checks, and clear ownership between platform and application teams.
- Result: Regional delivery becomes auditable and reversible while keeping infrastructure state understandable.

## Terraform Platform Modules

- Problem: Cloud platforms drift when teams copy infrastructure snippets and adjust them under delivery pressure.
- Constraints: Module versioning, state boundaries, reviewable plans, environment variance, and provider upgrade safety.
- Architecture: Terraform module contracts for networking, EKS, IAM, storage, DNS, and platform defaults with CI-compatible plan workflows.
- Result: Infrastructure changes become reviewable product changes instead of undocumented console state.

## ArgoCD App of Apps

- Problem: As platforms grow, application onboarding, add-ons, and environment drift become hard to reason about.
- Constraints: Bootstrap order, namespace ownership, secrets, cluster add-ons, team autonomy, and rollback visibility.
- Architecture: Argo CD app-of-apps pattern with platform add-ons, application sets, sync waves, health checks, and environment-level ownership.
- Result: Platform state becomes visible in Git and easier to bootstrap, audit, and recover.

## Policy as Code Guardrails

- Problem: Security and platform rules are often discovered only after deployment or during reviews.
- Constraints: Developer experience, admission control, exception handling, auditability, and avoiding fragile gatekeeping.
- Architecture: Policy-as-code guardrails with OPA/Kyverno-style checks, CI feedback, admission policies, and documented exception paths.
- Result: Teams get fast feedback while platform standards are enforced consistently across environments.

## Secrets and Certificate Automation

- Problem: Manual secret rotation and certificate handling create outage risk and hidden operational debt.
- Constraints: Rotation cadence, Kubernetes consumption, identity boundaries, audit trail, renewals, and emergency revocation.
- Architecture: Secret delivery model with external secret sources, workload identity, certificate automation, renewal monitoring, and rotation runbooks.
- Result: Sensitive material becomes lifecycle-managed infrastructure instead of scattered manual state.

## Incident Runbook Automation

- Problem: Incidents are slower when context, dashboards, logs, and recovery steps live in different places.
- Constraints: On-call pressure, incomplete symptoms, permissions, dry-run safety, and post-incident learning.
- Architecture: Runbook-linked alerts with diagnostic commands, status checks, escalation context, safe remediation steps, and follow-up documentation hooks.
- Result: Incident response becomes calmer, more repeatable, and easier to improve after the event.

## Self Healing Infrastructure

- Problem: Transient infrastructure failures can become user-facing incidents when recovery depends on manual detection.
- Constraints: False positives, blast radius, rollback safety, observability confirmation, and human override.
- Architecture: Failure detection with health signals, bounded remediation actions, chaos validation, alert correlation, and operator approval for risky paths.
- Result: Common failure modes can recover faster while preserving control over high-risk actions.

## AI DevOps Labs Platform

- Problem: DevOps learning is often passive and disconnected from real infrastructure failure modes.
- Constraints: Safe execution, terminal UX, generated scenarios, repeatability, cost control, and guided feedback.
- Architecture: AI-generated lab platform with scenario generation, interactive terminal flow, containerized execution, scoring, and cloud-native learning paths.
- Result: Infrastructure knowledge becomes hands-on practice instead of static documentation.

## Developer Platform Interface

- Problem: Developers lose time when every deployment, environment, and infrastructure request requires platform team translation.
- Constraints: Self-service boundaries, golden paths, ownership, auditability, and avoiding an unmaintainable portal.
- Architecture: Platform interface with paved workflows, templates, environment contracts, GitOps-backed changes, and visible operational status.
- Result: Teams can ship through clear platform paths while platform engineers keep control of the underlying system.

## Cost and Token Observability

- Problem: AI and cloud costs can grow quietly when usage is disconnected from teams, services, and deployment changes.
- Constraints: Token attribution, cloud tags, model pricing, request volume, budget alerts, and developer-readable reports.
- Architecture: Cost telemetry tied to services, AI gateway requests, deployment events, dashboards, and threshold-based feedback loops.
- Result: Cost becomes an operational signal teams can understand before it becomes a finance surprise.

## FleetDM Endpoint Visibility

- Problem: Endpoint visibility is often separate from cloud and Kubernetes operations, leaving security context incomplete.
- Constraints: Device inventory, query safety, rollout control, privacy, vulnerability context, and integration with existing operations.
- Architecture: FleetDM-style visibility layer connected to inventory, policy queries, vulnerability signals, and operational reporting.
- Result: Endpoint state becomes part of the broader infrastructure picture instead of a separate security island.

## Zero Trust Service Mesh

- Problem: Internal traffic is often trusted by default, making lateral movement and policy gaps hard to see.
- Constraints: Service identity, mTLS, policy rollout, observability, latency overhead, and developer debugging.
- Architecture: Service mesh model with workload identity, mTLS, authorization policy, traffic telemetry, and progressive rollout controls.
- Result: East-west traffic becomes governed, observable, and easier to reason about during security reviews and incidents.