| name | architecture-review |
| description | Architecture evaluation criteria and technology standards for the homelab.
Preloaded into the designer agent to ground design decisions in established
patterns and principles.
Use when: (1) Evaluating a proposed technology addition, (2) Reviewing architecture decisions,
(3) Assessing stack fit for a new component, (4) Comparing implementation approaches.
Triggers: "architecture review", "evaluate technology", "stack fit", "should we use",
"technology comparison", "design review", "architecture decision"
|
| user-invocable | false |
Architecture Evaluation Framework
Current Technology Stack
| Layer | Technology | Purpose |
|---|
| OS | Talos Linux | Immutable, API-driven Kubernetes OS |
| GitOps | Flux + ResourceSets | Declarative cluster state reconciliation |
| CNI/Network | Cilium | eBPF networking, network policies, Hubble observability |
| Storage | Longhorn | Distributed block storage with S3 backup |
| Object Storage | Garage | S3-compatible distributed object storage |
| Database | CNPG (CloudNativePG) | PostgreSQL operator with HA and backups |
| Cache/KV | Dragonfly | Redis-compatible in-memory store |
| Monitoring | kube-prometheus-stack | Prometheus + Grafana + Alertmanager |
| Logging | Alloy → Loki | Log collection pipeline |
| Certificates | cert-manager | Automated TLS certificate management |
| Secrets | ESO + AWS SSM | External Secrets Operator with Parameter Store |
| Upgrades | Tuppr | Declarative Talos/Kubernetes/Cilium upgrades |
| Infrastructure | Terragrunt + OpenTofu | Infrastructure as Code for bare-metal provisioning |
| CI/CD | GitHub Actions + OCI | Artifact-based promotion pipeline |
Evaluation Criteria
When evaluating any proposed technology addition or architecture change, assess against these criteria:
1. Principle Alignment
Score the proposal against each core principle (Strong/Weak/Neutral):
- Enterprise at Home: Does it reflect production-grade patterns?
- Everything as Code: Can it be fully represented in git?
- Automation is Key: Does it reduce or increase manual toil?
- Learning First: Does it teach valuable enterprise skills?
- DRY and Code Reuse: Does it leverage existing patterns or create duplication?
- Continuous Improvement: Does it make the system more maintainable?
2. Stack Fit
- Does this overlap with existing tools? (e.g., adding Redis when Dragonfly exists)
- Does it integrate with the GitOps workflow? (Must be Flux-deployable)
- Does it work on bare-metal? (No cloud-only services)
- Does it support the multi-cluster model? (dev → integration → live)
3. Operational Cost
- How is it monitored? (Must integrate with kube-prometheus-stack)
- How is it backed up? (Must have a recovery story)
- How does it handle upgrades? (Must be declarative, ideally via Renovate)
- What's the failure blast radius? (Isolated > cluster-wide)
4. Complexity Budget
- Is the complexity justified by the learning value?
- Could a simpler existing tool solve the same problem?
- What's the maintenance burden over 12 months?
5. Alternative Analysis
- What existing stack components could solve this? (Always check first)
- What are the top 2-3 alternatives in the ecosystem?
- What do other production homelabs use? (kubesearch research)
6. Failure Modes
- What happens when this component is unavailable?
- How does it interact with network policies? (Default deny)
- What's the recovery procedure? (Must be documented in a runbook)
- Can it self-heal? (Strong preference for self-healing)
Common Design Patterns
New Application
- HelmRelease via ResourceSet (flux-gitops pattern)
- Namespace with network-policy profile label
- ExternalSecret for credentials
- ServiceMonitor + PrometheusRule for observability
- GarageBucketClaim if S3 storage needed
- CNPG Cluster if database needed
New Infrastructure Component
- OpenTofu module in
infrastructure/modules/
- Unit in appropriate stack under
infrastructure/units/
- Test coverage in
.tftest.hcl files
- Version pinned in
versions.env if applicable
New Secret
- Store in AWS SSM Parameter Store
- Reference via ExternalSecret CR
- Never commit to git, not even encrypted
New Storage
- Longhorn PVC for block storage (default)
- GarageBucketClaim for object storage (S3-compatible)
- Never use hostPath or emptyDir for persistent data
New Database
- CNPG Cluster CR for PostgreSQL
- Automated backups to Garage S3
- Connection pooling via PgBouncer (CNPG-managed)
New Network Exposure
- HTTPRoute for HTTP/HTTPS traffic (Gateway API)
- Appropriate network-policy profile label
- cert-manager Certificate for TLS
- Internal gateway for internal-only services
Anti-Patterns to Challenge
| Anti-Pattern | Why It's Wrong | Correct Approach |
|---|
| "Just run a container" without monitoring | Invisible failures, no alerting | ServiceMonitor + PrometheusRule required |
| Adding a new tool when existing ones suffice | Stack bloat, maintenance burden | Evaluate existing stack first |
| Skipping observability "for now" | Technical debt that never gets paid | Monitoring is day-1, not day-2 |
| Manual operational steps | Drift, inconsistency, bus factor | Everything declarative via GitOps |
| Cloud-only services | Vendor lock-in, can't run on bare-metal | Self-hosted alternatives preferred |
| Single-instance without HA story | Single point of failure | At minimum, document recovery procedure |
| Storing state outside git | Shadow configuration, drift | Git is the source of truth |
