4 Commits

Author SHA1 Message Date
Jon Chery 32edb19c96 docs(P2): complete ai-ml domain phase
---ci---
project: atelier
phase: 2
milestone: v0.3
status: complete
requirements:
  covered: [ATELIER-65, ATELIER-66, ATELIER-67, ATELIER-68, ATELIER-69]
  partial: []
---/ci---
2026-08-05 03:24:13 +00:00
Jon Chery ab1289a9d9 docs(ship): P1 complete — checkpoint + roadmap status
---ci---
project: atelier
phase: 1
milestone: v0.3
status: complete
phase_tag: v0.2.1
release_id: 469
---/ci---
2026-08-05 03:21:04 +00:00
Jon Chery 47674969a1 docs(P1): complete gitops-operators domain phase
---ci---
project: atelier
phase: 1
milestone: v0.3
status: complete
requirements:
  covered: [ATELIER-60, ATELIER-61, ATELIER-62, ATELIER-63, ATELIER-64]
  partial: []
---/ci---
2026-08-05 03:20:48 +00:00
Jon Chery ce36db0579 docs(ship): P0 complete — checkpoint + roadmap status
---ci---
project: atelier
phase: 0
milestone: v0.3
status: complete
phase_tag: v0.2.0
release_id: 468
---/ci---
2026-08-05 03:17:10 +00:00
12 changed files with 1305 additions and 6 deletions
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{
"phase": 0,
"stage": "clarify",
"phase": 1,
"stage": "complete",
"milestone": "v0.3",
"phase_role": "pre_execution",
"phase_role": "execution",
"project": "atelier",
"attempts": 0,
"updated_at": "2026-08-05T03:08:00Z"
"updated_at": "2026-08-05T03:25:00Z",
"phase_tag": "v0.2.1",
"release_id": 469
}
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| Phase | Name | Type | Status | Key Deliverables |
|-------|------|------|--------|------------------|
| 0 | Pre-Execution | docs | active | Spec, clarify, research, ideate, plan, PERSONAS.md (extends platform-engineer, adds ml-engineer) |
| 1 | GitOps + Operators Domain | docs | pending | domains/gitops-operators/{first-principles, argocd, flux, operators, progressive-delivery}.md |
| 0 | Pre-Execution | docs | complete | Spec, clarify, research, ideate, plan, PERSONAS.md (extends platform-engineer, adds ml-engineer) — shipped v0.2.0 |
| 1 | GitOps + Operators Domain | docs | complete | domains/gitops-operators/{first-principles, argocd, flux, operators, progressive-delivery}.md — shipped v0.2.1 |
| 2 | AI/ML Domain | docs | pending | domains/ai-ml/{first-principles, data-versioning, model-evaluation, serving, monitoring-drift}.md |
| 3 | i18n + Compliance Domains | docs | pending | domains/i18n/{first-principles, locale-resources, formatting, rtl-bidi, testing-i18n}.md, domains/compliance/{first-principles, audit-logs, data-retention, policy-as-code, evidence}.md |
| 4 | Matrix + Review Integration | docs | pending | matrix/principles-matrix.md (+40 mappings), matrix/domain-coverage.md (incl. C-rule coverage table update), review/{agent-checklist, peer-review-checklist, anti-patterns}.md, MANIFEST.md (+ examples/ listing per ATELIER-91) |
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# Data Versioning — Derived Rules
> Derives from `domains/ai-ml/first-principles.md`. Covers P2 (Data is
> Versioned, Not Just Code) and P3 (Lineage is Traceable End-to-End).
> Referenced by `serving.md` and `monitoring-drift.md`. Scope per
> D-023: engineering discipline of versioning data, not dataset
> content design.
## Why Data Versioning (P2 Data is Versioned, Not Just Code)
- `git` versions code well and data badly. Datasets do not fit in
git, and a dataset is not recovered from a commit hash.
- A model trained on "the data" is a model trained on an unknown
input — a C1 (Correctness) violation. The dataset is a build
input; it is named, hashed, and recoverable the way any build
input is.
- Data versioning is the ML analogue of `domains/data/migrations.md`:
the schema and contents of the data evolve, every evolution is a
versioned migration, and every model points at a specific version.
## Dataset Hashing and Lineage (P3 Lineage Traceable End-to-End)
- Every dataset version has a content hash (not a filename or a
timestamp). The hash is the identity. A model's lineage record
names the dataset hash it was trained on; a serving prediction
names the model digest it came from.
- Lineage is a graph: prediction → model → training run → dataset →
source(s). Any edge missing is an orphan (`domains/observability/logging.md`
for the structured-log angle on lineage events).
- The lineage record is append-only. Editing it to "fix" a broken
trace is the same class of violation as editing an audit log.
## Train/Val/Test Split Versioning (P2, P4 Eval Defined Before Training)
- Splits are versioned with the dataset, not derived ad-hoc per run.
A split is a deterministic function of (dataset version, split
config, random seed). Two runs on the same pinned inputs produce
the same splits.
- The eval split is held out and never touched by training. A "held
out" set that leaked into training is a P4 (Evaluation Defined
Before Training) violation, not just a P2 violation — the eval
gate is measuring the training set, not the model.
- Cross `domains/data/schema-design.md` for the eval input contract:
the schema of the eval set is part of the versioned artifact.
## Tool Comparison (IDEATE-22, D-040)
| Tool | Versioning Model | Lineage | Best For | Notes |
|------|------------------|---------|----------|-------|
| DVC | Git-like pointers to content-addressed object store; `.dvc` files in git track data versions | Pipeline DAG in `dvc.yaml`; reproducibility via `dvc repro` | Teams already on git; file/directory datasets; ML pipelines | Treats data like code; shares git's history model. Object store is pluggable (S3, GCS, Azure, SSH) |
| Delta Lake | Table format with transaction log (ACID) + time travel via versioned commits; schema enforcement | Time travel queries; lineage via table history + catalog | Large tabular data; lakehouse; streaming + batch on the same table | Not a pipeline tool — pairs with Spark/Trino/Flink. Brings DB guarantees to object storage |
| LakeFS | Git-like operations (branch, commit, merge) over object storage itself | Branch model gives isolated, reproducible data branches | Data engineering teams; branch-per-experiment; CI over data | Not a table format — versions objects. Composes with Delta/Iceberg on top |
- Pick one primary versioning model per platform. Mixing DVC's
pointer model with Delta's transaction-log model fragments
operational knowledge (C4 Locality).
- All three satisfy P2; the choice is which fits the data shape and
the team's existing tooling. None is advocated over the others.
## Reproducibility Contract (P1 Reproducibility is the First Class)
A reproducible training run records, in one versioned place:
```
run_id: 2026-08-05T09:12:00Z#run-42
dataset: s3://ml-data/train@sha256:7f3a...e21
splits: dvc.yaml@commit a1b2c4d
code: git@a1b2c4d
config: configs/train.yaml@commit a1b2c4d
environment: ghcr.io/org/train-img@sha256:9c2d...f88
eval_spec: configs/eval.yaml@commit a1b2c4d
model_digest: registry/model@sha256:b5e1...aa0
```
- Lose any line and the run is anecdote, not evidence.
- The record is the lineage root: a prediction cites the
`model_digest`, which cites the `run_id`, which cites everything
above. This is how P3 (Lineage Traceable End-to-End) is satisfied
in practice.
## What Violates Data Versioning Discipline
| Violation | Principle |
|-----------|-----------|
| Dataset referenced by `s3://bucket/latest/` | P2 Data is Versioned, Not Just Code |
| Splits regenerated with an unpinned seed per run | P2, P4 Evaluation Defined Before Training |
| A production model with no dataset hash in its lineage | P3 Lineage Traceable End-to-End |
| Editing a lineage record to "clean up" a broken trace | P3 Lineage Traceable End-to-End |
| Eval split reachable from the training data path | P4 Evaluation Defined Before Training |
| Two platforms versioning the same data with different models | C4 Locality |
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# AI / ML — First Principles
> Scope per D-023: this domain covers ML **engineering discipline** —
> data versioning, evaluation methodology, serving patterns, and drift
> detection. It does **not** cover algorithm design, model architecture
> selection, hyperparameter tuning, or model-family comparison. Those
> are research choices, not engineering principles, and they have no
> derivation in the core C-rules.
## 1. The Principles
### P1. Reproducibility is the First Class
Every training run is reproducible from pinned data + code + config +
environment. An unreproducible run is an unreviewable run: you cannot
decide whether a result is correct if you cannot recreate it.
Reproducibility is the ML analogue of `domains/devops/P1
Reproducibility` and inherits its non-negotiable status. Lose any one
of data, code, config, or environment pinning, and the run is
anecdote, not evidence.
### P2. Data is Versioned, Not Just Code
Datasets, features, and train/val/test splits are first-class
versioned artifacts with content hashes and lineage. `git` alone is
insufficient — datasets do not fit in git, and a dataset is not a
commit hash. A model trained on "the data" is a model trained on an
unknown input, which is a correctness violation. Version data the way
you version code: pinned, named, and recoverable.
### P3. Lineage is Traceable End-to-End
Any deployed prediction traces back through model → training run →
dataset → source. No orphan models. A model in production with no
lineage is a correctness defect: you cannot reason about its failure
modes, you cannot roll it back to a known-good dataset, and you cannot
tell whether drift is in the model or in the data that built it.
Lineage is the audit trail of ML (`domains/observability/logging.md`).
### P4. Evaluation is Defined Before Training
Metrics, splits, and acceptance thresholds are declared a priori, in
code, before the model is trained. Cherry-picking metrics post-hoc is
a correctness violation: the evaluation is no longer measuring the
model, it is rationalizing it. The eval spec is a contract — it is
reviewable, it is versioned, and it is the gate the model must pass
before it leaves the experiment. This is the ML angle on C2 Clarity:
the intent of the model is obvious to its reader because the eval
declared it first.
### P5. Models are Versioned Artifacts
A model is a pinned, immutable, registry-tracked artifact with a
unique identifier. Never "the latest." A serving endpoint that pulls
"latest" is serving an unknown model — its behavior is undefined, its
rollback is impossible, and its lineage is broken. The model registry
is to models what a container registry is to images
(`domains/devops/P7 Immutability`): immutable, addressed by digest,
promoted by stage.
### P6. Serving is Observable
Inference latency, throughput, input distributions, and prediction
confidence are first-class signals. Silent serving is a bug. A model
in production that emits no metrics is a model you cannot operate: you
cannot see latency regressions, you cannot see input drift, you cannot
see a failing downstream consumer. Observability is designed in, not
bolted on (`domains/observability/metrics.md`).
### P7. Drift is Expected and Detected
Data drift, concept drift, and prediction drift are monitored as a
matter of course. A drift signal is an incident, not a curiosity. ML
systems decay without code changes — the world changes under the
model — so "no code changed" is not a defense against a serving
regression. Detecting drift is the ML-specific form of C7
Observability: you cannot fix a model you cannot see degrading.
### P8. Inference Inputs are Validated
The model's input contract — schema, value ranges, types, and
categorical domains — is enforced at the serving boundary.
Out-of-contract inputs are rejected, not silently scored. Scoring an
out-of-contract input is a correctness violation: the model's output
is undefined for inputs outside its training distribution, and
returning a number for it is lying to the caller. This is the ML angle
on `domains/security/input-validation.md` and inherits C1's
non-tradeable status.
### P9. Pipelines Compose, Notebooks Don't
Training and serving flows are composable pipelines with explicit
steps, named inputs, named outputs, and contracts between stages.
Notebooks are for exploration, not production. A notebook in the
serving path is a correctness defect: its state is implicit, its
order is human-dependent, and its reproducibility is whatever the last
operator remembered. Compose pipelines; keep notebooks in the lab.
### P10. Rollback Includes the Model
A serving rollback restores the prior model artifact, not just the
prior code. Promotion is reversible at the model layer. A rollback
that redeploys old code but keeps the new model has not rolled back —
the model was the thing that regressed. The rollback path must name
the prior model digest, the prior dataset version, and the prior eval
that cleared it. This is the ML angle on `domains/devops/P4 Rollback
First` and `domains/kubernetes/P10 Roll Forward, Roll Back`.
## 2. Core Principle Trace
Each AI/ML P-rule derives from one or more core C-rules (C1C8). The
matrix extension lands in P4 of the v0.3 plan; the traces below are
authoritative.
| P-rule | Core | Why |
|--------|------|-----|
| P1 Reproducibility is the First Class | C1, C5 | Correctness of results; reversibility of runs |
| P2 Data is Versioned, Not Just Code | C5, C7 | Reversibility of datasets; observability of data lineage |
| P3 Lineage is Traceable End-to-End | C7, C1 | Observability of provenance; correctness of attribution |
| P4 Evaluation is Defined Before Training | C1, C2 | Correctness of the eval gate; clarity of a-priori intent |
| P5 Models are Versioned Artifacts | C5, C6 | Reversibility of model identity; composability of registry stages |
| P6 Serving is Observable | C7 | Observability of inference |
| P7 Drift is Expected and Detected | C7, C1 | Observability of degradation; correctness of detection |
| P8 Inference Inputs are Validated | C1 | Correctness of the serving boundary (security subset) |
| P9 Pipelines Compose, Notebooks Don't | C6, C2 | Composability of stages; clarity of explicit contracts |
| P10 Rollback Includes the Model | C5 | Reversibility at the model layer |
## 3. What Violates These Principles
| Violation | Principle Breached |
|-----------|-------------------|
| A training run that cannot be replayed from pinned inputs | P1 Reproducibility is the First Class |
| A dataset referenced by a mutable path, not a hash | P2 Data is Versioned, Not Just Code |
| A production model with no record of its training data | P3 Lineage is Traceable End-to-End |
| Metrics chosen after seeing the results | P4 Evaluation is Defined Before Training |
| A serving endpoint that pulls `latest` from the registry | P5 Models are Versioned Artifacts |
| A model in production with no latency or throughput metrics | P6 Serving is Observable |
| A serving regression dismissed as "no code changed" | P7 Drift is Expected and Detected |
| An input with an out-of-range feature scored silently | P8 Inference Inputs are Validated |
| A notebook in the serving or training pipeline path | P9 Pipelines Compose, Notebooks Don't |
| A rollback that restores code but keeps the regressed model | P10 Rollback Includes the Model |
## 4. Relationship to Other Domains
AI/ML is the engineering-discipline layer for model-bearing systems.
It borrows the reproducibility, immutability, rollback, and
observability disciplines of `domains/devops/` and applies them to
the data → model → serving lifecycle. Cross-links are one-directional
(per D-026 extended):
- `domains/devops/P1 Reproducibility` ← P1
- `domains/devops/P4 Rollback First` ← P10
- `domains/devops/P5 Progressive Delivery` ← P10 (model canary)
- `domains/devops/P7 Immutability` ← P5 (model images)
- `domains/data/migrations.md` ← P2 (data versioning ↔ migration discipline)
- `domains/data/schema-design.md` ← P8 (inference input contract)
- `domains/observability/metrics.md` ← P6, P7
- `domains/observability/logging.md` ← P3 (lineage)
- `domains/security/input-validation.md` ← P8
- `domains/security/secrets.md` ← P8 (serving credentials)
- `domains/performance/backend.md` ← P6 (serving latency)
- `domains/kubernetes/workloads.md` ← P9 (serving on k8s)
- `domains/testing/first-principles.md` ← P4 (eval as a gate)
- `domains/gitops-operators/first-principles.md` ← P10 (model rollback in a GitOps loop)
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# Model Evaluation — Derived Rules
> Derives from `domains/ai-ml/first-principles.md`. Covers P4
> (Evaluation is Defined Before Training) and the eval-as-a-gate
> discipline. Referenced by `serving.md` (promotion gate) and
> `monitoring-drift.md` (online eval). Scope per D-023: evaluation
> methodology, not metric math or model-family benchmarks.
## Evaluation is a Gate, Not a Report (P4 Evaluation Defined Before Training)
- The eval spec — metrics, splits, thresholds, and pass/fail
criteria — is declared in code **before** the model is trained.
It is versioned with the data and the code; it is reviewable; it
is the contract the model must satisfy to leave the experiment.
- Cherry-picking metrics after seeing results is a correctness
violation: the eval is no longer measuring the model, it is
rationalizing it. The a-priori spec is what makes the eval
trustworthy.
- This is the ML angle on `domains/testing/first-principles.md` P1
(Tests as Specification): the eval declares the model's contract,
the model does not declare its own success.
## The Eval Input Contract (P8 Inference Inputs are Validated, cross `domains/data/schema-design.md`)
- The eval set has a schema: feature names, types, ranges, and
categorical domains. That schema is the same schema the serving
boundary enforces (`serving.md`, `domains/security/input-validation.md`).
- An eval set whose schema drifted from the serving schema is
measuring a different model than the one in production. Schema
parity is part of the versioned eval artifact.
- Cross `domains/data/schema-design.md`: the eval input contract is
a schema-design problem, versioned and reviewed like any schema.
## Holdout Integrity (P4, P2 Data is Versioned)
- The held-out eval set is never touched by training, feature
selection, or threshold tuning. A "held out" set that influenced
any training decision is not held out — it is a third training
set, and the eval is measuring memorization.
- Splits are versioned with the dataset (`data-versioning.md`).
Recreating splits ad-hoc per run breaks comparability across runs.
- Reusing a held-out set across many model iterations leaks it
incrementally. Rotate or re-split on a cadence; record the
rotation in lineage.
## Offline vs Online Evaluation (P6 Serving is Observable)
- **Offline eval** runs before promotion: held-out data, pinned
model, declared metrics, pass/fail gate. It answers "should this
model ship?"
- **Online eval** runs after promotion, on live traffic: shadow
scoring, A/B, canary metrics. It answers "is this model behaving
in production?" It is the bridge to `monitoring-drift.md`.
- A model that passed offline and regressed online is not a
contradiction — it is a signal that the offline distribution
differs from the live one (a P7 drift signal). Both eval layers
are required; neither substitutes for the other.
## Bias and Fairness Checks (Engineering Angle, P4)
- Bias/fairness checks are part of the a-priori eval spec, not an
afterthought. They are metrics with thresholds, declared before
training, gated the same as any metric.
- This doc covers the **engineering** discipline: the checks are
versioned, gated, and recorded in lineage. The choice of which
fairness metrics and what thresholds are policy decisions, not
engineering principles, and are out of scope here (D-023).
## Eval-as-a-Gate in the Pipeline (P9 Pipelines Compose)
- The eval is a pipeline stage with a contract: input = model
digest + eval dataset version; output = pass/fail + metric
report. It composes with the training stage and the promotion
stage.
- A promotion that bypasses the eval stage is a P4 violation,
regardless of who approved it. The gate is in the pipeline, not
in a human sign-off sheet.
```
train -> eval(gate) -> register(promote) -> serve
|
+-- fail -> abort, no promote
```
## What Violates Evaluation Discipline
| Violation | Principle |
|-----------|-----------|
| Metrics chosen after seeing the scores | P4 Evaluation Defined Before Training |
| Held-out set used in feature selection or threshold tuning | P4, P2 |
| Eval schema differs from serving schema | P8 Inference Inputs are Validated |
| Promotion by human approval, bypassing the eval stage | P4, P9 Pipelines Compose |
| A "passing" model with no online eval in production | P6 Serving is Observable |
| Fairness checks added after a model shipped | P4 Evaluation Defined Before Training |
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# Monitoring & Drift — Derived Rules
> Derives from `domains/ai-ml/first-principles.md`. Covers P7 (Drift
> is Expected and Detected) and the online half of P6 (Serving is
> Observable). Referenced by `serving.md` (online eval) and
> `model-evaluation.md` (online layer). Scope per D-023: drift
> detection methodology, not model retraining architecture.
## Drift is Expected and Detected (P7 Drift is Expected and Detected)
- ML systems decay without code changes. The world changes under
the model: user behavior shifts, input pipelines change,
upstream schemas evolve. "No code changed" is not a defense
against a serving regression.
- A drift signal is an incident, not a curiosity. It triggers an
alert, an investigation, and a decision (retrain, roll back, or
accept with a recorded justification). Silent drift is the same
class of bug as silent serving (P6).
- Cross `domains/observability/metrics.md` for the alerting
primitives and `domains/observability/logging.md` for the
structured events a drift signal emits.
## The Three Drift Types (IDEATE-30, D-048)
| Drift Type | What Changes | Detection Signal | Source of Truth |
|------------|--------------|------------------|-----------------|
| **Data drift** (input drift) | The distribution of inputs at serving time diverges from the distribution the model was trained on | Statistical distance between the live input distribution and the pinned training-set distribution (e.g., PSI, KL, KS test). Alert on threshold breach | Training dataset hash (`data-versioning.md`) + live input metrics |
| **Concept drift** | The relationship between inputs and the target changes — the same input now maps to a different correct output | Ground-truth lag: compare delayed labels against predictions on the same inputs. Rising error rate against a stable input distribution signals concept, not data, drift | Delayed-label feedback stream + prediction log |
| **Prediction drift** (output drift) | The distribution of the model's predictions shifts, with no change to inputs | Statistical distance between the live prediction distribution and a pinned baseline prediction distribution. Independent of inputs — catches model-internal regressions and upstream silent changes | Prediction log + baseline prediction snapshot |
- The three signals are distinct and non-substitutable. Data drift
catches the input changing; concept drift catches the world
changing; prediction drift catches the model's behavior changing.
A monitoring setup with only one is blind to two classes of
regression.
- Evidently AI and Great Expectations are the canonical tooling:
Evidently for drift/statistical reports, Great Expectations for
data-quality/contract checks at the pipeline boundary. Both
produce the metrics that feed `domains/observability/metrics.md`.
## Detection Signals in Practice
- **Data drift** compares live inputs to the **pinned training
distribution** — not to "yesterday's inputs." Without a pinned
baseline, drift is measured against a moving target and is
meaningless. Cross `data-versioning.md` for how the baseline is
pinned.
- **Concept drift** requires ground truth, which is often delayed
(days/weeks). The detection signal is the gap between
prediction-time confidence and delayed-label error. A rising
error against stable inputs is the signature.
- **Prediction drift** needs no ground truth and no input
comparison — it watches the model's own output distribution. It
is the cheapest signal and the first to fire; it is also the
least specific (any of the three drifts can move predictions).
## Alerting and Retraining Triggers (P7, P10 Rollback Includes the Model)
- A drift alert is an incident. It does not auto-trigger retraining
unsupervised — auto-retraining on drift can lock in a bad
distribution. The alert triggers a human decision: investigate,
retrain, roll back, or accept.
- Retraining is a new training run (`first-principles.md` P1): it
produces a new model digest, passes the eval gate
(`model-evaluation.md`), and is promoted through the registry
(`serving.md`). The prior model stays rollbackable (P10).
- Cross `domains/observability/metrics.md` for the alert-rule
pattern: threshold + window + severity, routed to the same
on-call path as any production incident.
## Online Evaluation Bridge (P6 Serving is Observable)
- Online eval (`model-evaluation.md`) is the live counterpart to
drift monitoring: shadow scores and A/B canaries measure a
candidate model against the incumbent, while drift monitoring
measures the incumbent against its own baseline. Both feed the
same metrics pipeline.
## What Violates Monitoring Discipline
| Violation | Principle |
|-----------|-----------|
| Only one drift type monitored | P7 Drift is Expected and Detected |
| Drift baseline is "yesterday's inputs," not pinned training data | P7, P2 Data is Versioned |
| Drift alert that auto-retrains without a human gate | P7, P1 Reproducibility |
| A serving regression dismissed as "no code changed" | P7 Drift is Expected and Detected |
| Concept-drift check with no delayed-label feedback path | P7 Drift is Expected and Detected |
| Prediction-distribution change with no alert | P6 Serving is Observable, P7 |
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# Serving — Derived Rules
> Derives from `domains/ai-ml/first-principles.md`. Covers P5 (Models
> are Versioned Artifacts), P6 (Serving is Observable), P8 (Inference
> Inputs are Validated), and P10 (Rollback Includes the Model).
> Referenced by `monitoring-drift.md` (online signals) and
> `model-evaluation.md` (promotion gate). Scope per D-023: serving
> patterns, not model architectures.
## The Model is an Addressed Artifact (P5 Models are Versioned Artifacts)
- A serving endpoint pulls a model by digest, never by `latest`. A
model pulled by `latest` is an unknown model — its behavior is
undefined and its rollback is impossible.
- The model registry is to models what a container registry is to
images (`domains/devops/P7 Immutability`): immutable, addressed by
digest, promoted by stage (staging → prod). Promotion is a
registry operation, not a file copy.
- A serving rollout names the model digest in its manifest. The
digest is part of the deploy's lineage (`data-versioning.md`).
## Inference Inputs are Validated (P8 Inference Inputs are Validated)
- The model's input contract — schema, types, ranges, categorical
domains — is enforced at the serving boundary, before the model
sees the input. Out-of-contract inputs are rejected with a
defined error, not silently scored.
- Scoring an out-of-contract input is a C1 (Correctness) violation:
the model's output is undefined outside its training
distribution, and returning a number for it is lying to the
caller.
- This is the ML angle on `domains/security/input-validation.md`:
the validation lives at the boundary, the model is downstream of
it, and the contract is versioned with the model.
## Serving is Observable (P6 Serving is Observable)
- Every inference path emits: request latency, throughput, input
distribution summaries, prediction confidence, and error counts.
Silent serving is a bug.
- Cross `domains/observability/metrics.md` for the metrics
primitives (histograms, counters, gauges) and
`domains/observability/tracing.md` for the request-level trace
that ties an input to a prediction.
- Latency SLAs are enforced via `domains/performance/backend.md`
disciplines: budget the inference path, measure the tail (p99),
alert on budget breach.
## Serving Patterns (P9 Pipelines Compose)
| Pattern | When | Notes |
|---------|------|-------|
| Inference as a service | Default; model behind an HTTP/gRPC endpoint | KServe, Seldon Core, BentoML. Scales with traffic; model is a deployable, addressable artifact |
| Batch inference | Offline scoring of large datasets | No latency SLA; throughput-bound. Same model digest, same input contract |
| Embedded / in-process | Latency-critical, single-tenant | Model linked into the app. Trades observability for latency — only when the SLA demands it |
- Canarying a model is a serving pattern, not a deployment pattern:
shift a fraction of traffic to the new model digest, measure
online eval (`model-evaluation.md`), abort to the prior digest on
regression. This is `domains/devops/P5 Progressive Delivery`
applied at the model layer.
- Rollback restores the prior model digest (P10 Rollback Includes
the Model). A rollback that redeploys old code but keeps the new
model has not rolled back. Cross `domains/gitops-operators/first-principles.md`
for the GitOps reconciliation loop that drives model rollouts.
## Tool Landscape (KServe / Seldon Core / BentoML)
| Tool | Model Packaging | Deployment Surface | Notes |
|------|-----------------|--------------------|-------|
| KServe | InferenceService CRD; runtime predictors (v2, HuggingFace, PMML, custom) | Kubernetes-native; CRD-driven | Cross `domains/kubernetes/workloads.md`. Brings the k8s reconciliation model to serving |
| Seldon Core | SeldonDeployment CRD; graph of predictors | Kubernetes-native; CRD-driven | Emphasizes inference graphs (fan-out, ensemble) as CRD structure |
| BentoML | Bento (model + runtime + deps packaged); Yatai registry | Kubernetes or bare container | Focuses on packaging + registry; the Bento is the versioned artifact (P5) |
- All three satisfy P5/P6/P8 when wired correctly; the choice is
packaging model and deployment surface, not correctness.
- None is advocated over the others.
## What Violates Serving Discipline
| Violation | Principle |
|-----------|-----------|
| Endpoint pulls `latest` from the registry | P5 Models are Versioned Artifacts |
| Out-of-range input scored silently | P8 Inference Inputs are Validated |
| Serving path emits no latency or throughput metrics | P6 Serving is Observable |
| Rollback redeploys code but keeps the regressed model | P10 Rollback Includes the Model |
| A notebook in the serving path | P9 Pipelines Compose, Notebooks Don't |
| Canary with no abort-to-prior-digest path | P10, `domains/devops/P5 Progressive Delivery` |
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# ArgoCD — Derived Rules
> Derives from `domains/gitops-operators/first-principles.md`.
> Applies P1P10 to ArgoCD specifically. For the ArgoCD-vs-Flux
> decision, see the decision matrix at the end of this doc and in
> `flux.md`.
## What ArgoCD Is (P1 Git is the Source of Truth, P3 Pull, Don't Push)
- ArgoCD is a pull-based GitOps controller for Kubernetes. It runs
inside the target cluster, pulls desired state from git, and
reconciles the cluster to match. CI never holds `kubectl` rights
against the cluster (P3).
- An Application is a declarative binding of "this git path" to
"this cluster destination." The Application CRD is the unit of
reconciliation. The cluster is a derivative of git, never the
authority (P1).
- ArgoCD supports Helm charts, Kustomize overlays, ksonnet, and raw
manifests as source formats — see `domains/kubernetes/helm.md`
and `domains/kubernetes/kustomize.md`.
## Application CRD (P2 Declarative Over Imperative, P4 Continuous Reconciliation)
- An Application declares `source` (repo, path, revision, chart),
`destination` (server, namespace), and `syncPolicy`. The
reconciler loops continuously; drift is corrected automatically,
not on-demand (P4).
```yaml
apiVersion: argoproj.io/v1alpha1
kind: Application
metadata:
name: payments-api
namespace: argocd
spec:
source:
repoURL: https://git.example.com/platform/payments
targetRevision: 1.2.3
path: manifests/prod
destination:
server: https://kubernetes.default.svc
namespace: payments
syncPolicy:
automated:
prune: true
selfHeal: true
syncOptions:
- CreateNamespace=false
```
- `automated.prune: true` deletes resources removed from git.
`selfHeal: true` corrects hand-edited drift back to git (P8).
Disable both for workloads that need manual approval gates.
## App-of-Apps (P6 Operators Encode Domain Knowledge, C6 Composability)
- The App-of-Apps pattern: one root Application points at a git
directory of child Application manifests. The root app reconciles
the children; the children reconcile the workloads. This is the
ArgoCD expression of composition — a fleet of apps as a tree of
Applications.
- Use App-of-Apps for cluster bootstrapping (one repo, many
clusters, many apps). Do not use it as a substitute for a package
manager; if you are templating hundreds of near-identical
Applications, use a generator (ApplicationSet) instead.
## Sync Waves and Hooks (P4 Continuous Reconciliation, P7 Reversibility)
- Sync waves order resources within a sync: `PreSync``Sync`
`PostSync`. Use waves to run a job before a Deployment, or a
migration before the app that depends on it.
- Sync hooks (`PreSync`, `Sync`, `PostSync`, `SyncFail`) are
Resources annotated to execute at a wave boundary. A `SyncFail`
hook runs on sync failure — the abort path (P7).
- Wave ordering is a correctness mechanism, not a performance one.
Mis-ordered waves (e.g., app starts before its migration job)
are a correctness bug.
## Health and Status (P9 Failure is Observable and Surfaced)
- ArgoCD assesses every resource's health (`Healthy`, `Progressing`,
`Degraded`, `Missing`, `Suspended`) and surfaces the aggregate as
Application status. Sync status (`Synced`, `OutOfSync`) reports
drift against git.
- Health checks are pluggable via Lua scripts for custom CRDs. An
Operator-managed CRD without a health check reads as `Progressing`
forever — write one (see `operators.md`).
- Out-of-sync or degraded status must emit a notification (Slack,
PagerDuty, webhook). Silent drift is the bug (P9). Wire status to
`domains/observability/metrics.md`.
## Diff and Drift (P8 Reconcile, Don't Mutate by Hand, P4)
- `argocd app diff` shows the diff between git and live cluster.
A non-empty diff on a synced app is hand-edit drift — the
recovery is `selfHeal`, not a manual `kubectl apply` (P8).
- Drift detection runs continuously (P4). The gap between "git
changed" and "cluster matches git" is observable, not assumed.
## RBAC and SSO (P10 Least Privilege Reconciliation)
- ArgoCD's own RBAC governs who can view, sync, and admin
Applications. Bind to SSO (OIDC, SAML) for human identity; bind
the controller's service account to a Role scoped to the
namespaces it reconciles.
- The controller's credentials must not be `cluster-admin` (P10).
Use namespace-scoped Roles via `ApplicationSet` namespaces or
cluster-wide AppProject restrictions. See
`domains/kubernetes/rbac.md` and `domains/security/authorization.md`.
- AppProjects bound the blast radius of what an Application can
deploy (allowed repos, destinations, roles). One AppProject per
team or environment; the default project is for nothing in
production.
## Multi-Cluster (P4 Locality, P10)
- ArgoCD registers external clusters by secret. The controller
pulls from git and pushes to the registered cluster's API server.
The "pull, don't push" boundary (P3) is between the target
cluster's reconciler and CI — the controller-to-apiserver hop is
internal to the platform.
- Scope each registered cluster's credentials to the namespaces
ArgoCD manages there. Do not register a cluster with cluster-admin
and call it done (P10).
## Sync Windows (P5 Reversibility, P7)
- Sync windows restrict when automated sync runs (e.g., no syncs
during business hours, or syncs only in a maintenance window).
They are a reversibility mechanism: a bad commit lands in git,
but the sync window holds it until review.
- Sync windows do not replace health monitoring (P9). A degraded
app inside a window is still an incident.
## Secrets (P10, cross-link security/secrets)
- Do not store raw Secrets in the GitOps repo. Use a sealed-secret
controller (Bitnami Sealed Secrets, SOPS, External Secrets
Operator) so the git store holds encrypted material only. See
`domains/security/secrets.md` for the general secret-hygiene
principles.
## ArgoCD vs Flux — Decision Matrix (IDEATE-21, D-039)
| Axis | ArgoCD | Flux |
|------|--------|------|
| Architecture | Monolithic controller + Application CRD | Composable GitOps Toolkit controllers (source, kustomize, helm, notification) |
| Reconciliation unit | Application (one CRD per app) | Kustomization / HelmRelease (one per deploy unit) |
| UI | Web UI + CLI (full dashboard, tree view, diff viewer) | CLI-first; UI via Weave GitOps or FluxUI (add-on) |
| Sync model | Periodic poll or webhook; sync waves + hooks | Poll + webhook; runs continuously, no explicit sync waves |
| Multi-cluster | One ArgoCD manages many clusters (hub-and-spoke) | One Flux per cluster (per-cluster autonomy) |
| Templating in repo | Helm, Kustomize, ksonnet, raw manifests, Jsonnet | Helm, Kustomize, raw manifests |
| RBAC | Built-in RBAC + SSO + AppProjects | Kubernetes RBAC (no built-in RBAC layer) |
| Progressive delivery | Argo Rollouts (sister project, tight integration) | Flagger (sister project, tight integration) |
| Best for | Teams wanting a UI, multi-cluster from one pane, App-of-Apps bootstrapping | Teams wanting composable controllers, per-cluster autonomy, minimal footprint |
| Watch out for | Monolithic controller scaling, UI as ops crutch, AppProject sprawl | No native UI, steeper learning curve, manual multi-cluster orchestration |
- Use ArgoCD when you want a UI, central multi-cluster management,
and sync-wave ordering. Use Flux when you want composable
controllers, per-cluster autonomy, and a minimal footprint.
- Both are CNCF graduated and both implement the OpenGitOps
principles. The choice is architectural fit, not correctness. See
`flux.md` for the Flux-side perspective.
## What Violates ArgoCD Discipline
| Violation | Principle |
|-----------|-----------|
| CI pipeline with `kubectl` rights pushing to the cluster | P3 Pull, Don't Push |
| `argocd app set` used as the steady state instead of git | P1 Git is the Source of Truth |
| `selfHeal: false` on a prod app with no manual gate | P8 Reconcile, Don't Mutate by Hand |
| Controller ServiceAccount bound to `cluster-admin` | P10 Least Privilege Reconciliation |
| Sync failure with no notification wired | P9 Failure is Observable and Surfaced |
| AppProject with no destination restrictions in prod | P10 Least Privilege Reconciliation |
| Raw Secret in the GitOps repo | P10, `domains/security/secrets.md` |
| Manual `kubectl edit` on an ArgoCD-managed resource | P8 Reconcile, Don't Mutate by Hand |
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# GitOps + Operators — First Principles
## 1. The Principles
### P1. Git is the Source of Truth
Desired state lives in a versioned, immutable git store. The
cluster is a derivative of git, never the authority. If a state
exists only in the cluster and not in git, it is drift, not truth.
The commit history is the audit trail and the rollback path.
### P2. Declarative Over Imperative
Express the desired cluster state, not the commands to reach it.
A manifest says what should exist; the reconciler makes it so.
Imperative `kubectl` is for inspection and incident response, not
for the steady state. This is the GitOps expression of
`domains/kubernetes/P1 Declarative Desired State` and
`domains/infrastructure-as-code/P1 Declarative Intent`.
### P3. Pull, Don't Push
Agents running inside the target pull desired state from git; the
target never accepts outside push credentials. No CI pipeline holds
`kubectl` rights against the production cluster. The cluster reaches
out to git, not the other way around. This is the security primitive
of GitOps: the blast radius of a compromised CI is bounded by what CI
can push, and a pull model gives CI nothing to push.
### P4. Continuous Reconciliation
The reconciliation loop is the primitive. Drift is detected and
corrected automatically, not on-demand. A manual `apply` is an
exception, not the workflow. The loop runs continuously; the gap
between "git changed" and "cluster matches git" is measured in
seconds, not tickets.
### P5. State is Immutable and Versioned
Every change to desired state is a commit. History is the audit
trail and the rollback path. A revert is a rollback; a force-push is
history deletion. The git store is treated like
`domains/infrastructure-as-code/P3 State is Truth` — lose it or
tamper with it, and you lose the ability to reason about the system.
### P6. Operators Encode Domain Knowledge
Operational expertise lives as CRDs plus controllers, not as
runbooks that humans must remember. An operator is a control loop
that encodes how to reconcile a specific domain (a database, a
message queue, a certificate). The operator is the deepest
expression of `domains/kubernetes/P1 Declarative Desired State`
the domain knowledge is the desired state.
### P7. Progressive Delivery is Reversible by Construction
Canary and blue-green are staged, metric-gated, and one-command
abortable. Promotion without a rollback path is a violation. A
rollout that cannot be aborted is a deploy, not a progressive
delivery. This is the GitOps extension of
`domains/devops/P5 Progressive Delivery` and
`domains/kubernetes/P10 Roll Forward, Roll Back`.
### P8. Reconcile, Don't Mutate by Hand
Manual `kubectl apply` or `kubectl edit` on a GitOps-managed
resource is an incident. The reconciler will overwrite the hand
edit on the next loop; the hand edit was never truth. Drift back to
git is the recovery, not the failure. This is the GitOps angle on
`domains/infrastructure-as-code/P9 Drift is Recoverable`.
### P9. Failure is Observable and Surfaced
Sync failures, health degradation, and rollout-stall events emit
status and notifications. Silent drift is the bug. A GitOps
controller that fails to sync without surfacing the failure has
violated the contract — you cannot fix what you cannot see
(`domains/observability/metrics.md`).
### P10. Least Privilege Reconciliation
The controller's credentials are scoped to the namespaces and
resources it reconciles. No `cluster-admin` GitOps robots. One
credential set per boundary; the reconciler sees only what it
reconciles. This is the GitOps angle on
`domains/kubernetes/P7 RBAC by Intent, Not Identity` and
`domains/security/authorization.md`.
## 2. Core Principle Trace
Each GitOps + Operators P-rule derives from one or more core
C-rules (C1C8). The matrix extension lands in P4 of the v0.3
plan; the traces below are authoritative.
| P-rule | Core | Why |
|--------|------|-----|
| P1 Git is the Source of Truth | C1, C5 | Correctness of state; reversibility via history |
| P2 Declarative Over Imperative | C2, C3 | Clarity of intent; simplicity of mental model |
| P3 Pull, Don't Push | C1, C4 | Correctness via security; locality of credentials |
| P4 Continuous Reconciliation | C7, C1 | Observability of drift; correctness of convergence |
| P5 State is Immutable and Versioned | C5 | Reversibility via version history |
| P6 Operators Encode Domain Knowledge | C6, C2 | Composability of expertise; clarity of operational intent |
| P7 Progressive Delivery is Reversible | C5, C1 | Reversibility of promotion; correctness of abort |
| P8 Reconcile, Don't Mutate by Hand | C1, C7 | Correctness of single source; observability of drift |
| P9 Failure is Observable and Surfaced | C7 | Observability of reconciliation |
| P10 Least Privilege Reconciliation | C1, C8 | Correctness via security; economy of trust |
## 3. What Violates These Principles
| Violation | Principle Breached |
|-----------|-------------------|
| CI pipeline pushes manifests to the cluster | P3 Pull, Don't Push |
| A resource exists in the cluster but not in git | P1 Git is the Source of Truth |
| `kubectl edit` on a GitOps-managed resource | P8 Reconcile, Don't Mutate by Hand |
| Reconciler with `cluster-admin` ClusterRoleBinding | P10 Least Privilege Reconciliation |
| Sync failure with no status or notification | P9 Failure is Observable and Surfaced |
| Canary with no abort/rollback path | P7 Progressive Delivery is Reversible |
| Operator runbook that exists only in a wiki | P6 Operators Encode Domain Knowledge |
| Reconciler that applies on a cron, not continuously | P4 Continuous Reconciliation |
| Force-push rewrites GitOps repo history | P5 State is Immutable and Versioned |
| Imperative deploy script as the steady state | P2 Declarative Over Imperative |
## 4. Relationship to Other Domains
GitOps + Operators is the deployment-automation layer above
`domains/kubernetes/` and `domains/infrastructure-as-code/`. It
borrows their declarative-reconciliation model and adds the
git-as-source-of-truth and pull-based credential boundaries. Cross
links are one-directional (per D-026 extended):
- `domains/kubernetes/P1 Declarative Desired State` ← P2
- `domains/kubernetes/P10 Roll Forward, Roll Back` ← P7
- `domains/infrastructure-as-code/P1 Declarative Intent` ← P2
- `domains/infrastructure-as-code/P3 State is Truth` ← P1, P5
- `domains/infrastructure-as-code/P9 Drift is Recoverable` ← P4, P8
- `domains/devops/P4 Rollback First` ← P5, P7
- `domains/devops/P5 Progressive Delivery` ← P7
- `domains/devops/P6 Configuration as Code` ← P1, P2
- `domains/security/secrets.md` ← P3, P10 (reconciliation credentials)
- `domains/security/supply-chain.md` ← P5 (signed, immutable provenance)
- `domains/observability/metrics.md` ← P4, P9 (reconciliation + rollout metrics)
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# Flux — Derived Rules
> Derives from `domains/gitops-operators/first-principles.md`.
> Applies P1P10 to Flux specifically. For the ArgoCD-vs-Flux
> decision, see the decision matrix at the end of this doc and in
> `argocd.md`.
## What Flux Is (P1 Git is the Source of Truth, P3 Pull, Don't Push)
- Flux is a set of composable controllers — the GitOps Toolkit —
that run inside the target cluster, pull desired state from git
or OCI registries, and reconcile the cluster to match. CI never
holds `kubectl` rights against the cluster (P3).
- The composable-controller architecture is a C6 (Composability)
exemplar: each controller does one thing (source, kustomize, helm,
notification) and the controllers compose into a full GitOps
system.
- Flux supports Helm releases, Kustomize overlays, and raw
manifests — see `domains/kubernetes/helm.md` and
`domains/kubernetes/kustomize.md`.
## GitOps Toolkit Controllers (P6 Composability, P4 Continuous Reconciliation)
- **source-controller** — pulls git, Helm, OCI, and bucket sources;
emits artifacts (tarballs) with a digest. The source is the
pinned input to reconciliation (P5 versioning by digest).
- **kustomize-controller** — reconciles Kustomization CRDs against
the artifacts from source-controller. Runs continuously (P4).
- **helm-controller** — reconciles HelmRelease CRDs against Helm
charts from source-controller.
- **notification-controller** — emits events and notifications for
sync, health, and source-readiness events (P9).
- **image-automation-controller** (optional) — updates git with new
image tags when a policy matches, closing the "latest image"
loop declaratively.
## Kustomization CRD (P2 Declarative Over Imperative, P4)
- A Kustomization binds "this source" to "this target namespace"
with a reconciliation interval. The reconciler loops
continuously; drift is corrected automatically (P4).
```yaml
apiVersion: kustomize.toolkit.fluxcd.io/v1
kind: Kustomization
metadata:
name: payments-api
namespace: flux-system
spec:
sourceRef:
kind: GitRepository
name: platform
namespace: flux-system
path: ./manifests/prod
targetNamespace: payments
interval: 1m
prune: true
wait: true
healthChecks:
- apiVersion: apps/v1
kind: Deployment
name: payments-api
namespace: payments
```
- `prune: true` deletes resources removed from git. `wait: true`
waits for health checks before declaring the Kustomization ready.
Disable prune for workloads that need manual removal gates.
## HelmRelease CRD (P6 Composability, cross-link helm.md)
- A HelmRelease binds a Helm chart (from a HelmRepository or OCI
source) to target values and a target namespace. helm-controller
renders and applies it. See `domains/kubernetes/helm.md` for the
chart model.
- Pin the chart version in the HelmRepository or the HelmRelease.
Never float `latest` — unversioned charts drift (P5).
## OCI Sources (P5 State is Immutable and Versioned)
- source-controller can pull from OCI registries (Helm charts as
OCI artifacts, or generic OCI repositories). The digest is the
version — immutable by construction (P5).
- OCI sources close the supply-chain loop: the manifest is signed
and immutable in the registry, and Flux pulls it by digest. Cross-
link `domains/security/supply-chain.md` for signed-provenance
principles.
## Reconciliation and Drift (P4 Continuous Reconciliation, P8)
- Flux reconciles on `interval` (default 1m) and on webhook event.
Drift between git and cluster is detected each interval and
corrected (with `prune` + `selfHeal` semantics).
- Hand-edited drift on a Flux-managed resource is overwritten on the
next loop — the hand edit was never truth (P8). The recovery is
to fix git, not to `kubectl apply`.
## Notifications and Events (P9 Failure is Observable and Surfaced)
- notification-controller emits events for source readiness, sync
success/failure, and health transitions. Wire them to Slack,
PagerDuty, or a webhook. Silent drift is the bug (P9).
- Events flow to `domains/observability/metrics.md` via the
notification controller's provider model — sync and health as
first-class signals.
## RBAC and Multi-Cluster (P10 Least Privilege Reconciliation, P4)
- Flux's controllers run with a ServiceAccount in `flux-system`.
Scope that account to the namespaces Flux reconciles. Do not bind
it to `cluster-admin` (P10). See `domains/kubernetes/rbac.md` and
`domains/security/authorization.md`.
- Flux is per-cluster by design (one Flux install per cluster). For
multi-cluster, use one repo with per-cluster paths, or a fleet
tool that bootstraps Flux per cluster. Per-cluster autonomy is a
feature, not a limitation — it bounds the blast radius of a
compromised controller (P4 locality, P10).
## Secrets (P10, cross-link security/secrets)
- Do not store raw Secrets in the GitOps repo. Use the
SOPS-compatible decryption in kustomize-controller, or External
Secrets Operator, so the git store holds encrypted material only.
See `domains/security/secrets.md`.
## ArgoCD vs Flux — Decision Matrix (IDEATE-21, D-039)
| Axis | ArgoCD | Flux |
|------|--------|------|
| Architecture | Monolithic controller + Application CRD | Composable GitOps Toolkit controllers (source, kustomize, helm, notification) |
| Reconciliation unit | Application (one CRD per app) | Kustomization / HelmRelease (one per deploy unit) |
| UI | Web UI + CLI (full dashboard, tree view, diff viewer) | CLI-first; UI via Weave GitOps or FluxUI (add-on) |
| Sync model | Periodic poll or webhook; sync waves + hooks | Poll + webhook; runs continuously, no explicit sync waves |
| Multi-cluster | One ArgoCD manages many clusters (hub-and-spoke) | One Flux per cluster (per-cluster autonomy) |
| Templating in repo | Helm, Kustomize, ksonnet, raw manifests, Jsonnet | Helm, Kustomize, raw manifests |
| RBAC | Built-in RBAC + SSO + AppProjects | Kubernetes RBAC (no built-in RBAC layer) |
| Progressive delivery | Argo Rollouts (sister project, tight integration) | Flagger (sister project, tight integration) |
| Best for | Teams wanting a UI, multi-cluster from one pane, App-of-Apps bootstrapping | Teams wanting composable controllers, per-cluster autonomy, minimal footprint |
| Watch out for | Monolithic controller scaling, UI as ops crutch, AppProject sprawl | No native UI, steeper learning curve, manual multi-cluster orchestration |
- Use Flux when you want composable controllers, per-cluster
autonomy, and a minimal footprint. Use ArgoCD when you want a UI,
central multi-cluster management, and sync-wave ordering.
- Both are CNCF graduated and both implement the OpenGitOps
principles. The choice is architectural fit, not correctness. See
`argocd.md` for the ArgoCD-side perspective.
## What Violates Flux Discipline
| Violation | Principle |
|-----------|-----------|
| CI pipeline with `kubectl` rights pushing to the cluster | P3 Pull, Don't Push |
| HelmRelease with no pinned chart version | P5 State is Immutable and Versioned |
| Flux ServiceAccount bound to `cluster-admin` | P10 Least Privilege Reconciliation |
| Kustomization with no `healthChecks` on a prod app | P9 Failure is Observable and Surfaced |
| No notification provider wired for sync failures | P9 Failure is Observable and Surfaced |
| Raw Secret in the GitOps repo | P10, `domains/security/secrets.md` |
| Manual `kubectl edit` on a Flux-managed resource | P8 Reconcile, Don't Mutate by Hand |
| `interval: 24h` on a prod Kustomization (drift window too wide) | P4 Continuous Reconciliation |
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# Operators — Derived Rules
> Derives from `domains/gitops-operators/first-principles.md`.
> Applies P6 (Operators Encode Domain Knowledge) primarily, with
> P1, P4, P8, P9, P10. Cross-links `domains/kubernetes/workloads.md`
> and `domains/kubernetes/rbac.md` for the underlying controller
> model, and `domains/infrastructure-as-code/modules.md` for the
> module-vs-operator boundary.
## What an Operator Is (P6 Operators Encode Domain Knowledge)
- An Operator is a Kubernetes controller that encodes human
operational knowledge as CRDs plus a control loop. The operator
reconciles a domain-specific resource (a database, a message
queue, a certificate, a ML model) to a desired state.
- The operator is the deepest expression of
`domains/kubernetes/P1 Declarative Desired State`: the domain
knowledge itself is the desired state. A runbook that lives only
in a wiki is operational knowledge that has not been encoded —
the operator is the encoding (P6).
- An operator runs inside the cluster, observes its CRDs, and acts.
It is a pull-based reconciler by construction — see
`domains/gitops-operators/first-principles.md` P3.
## CRDs and Controllers (P2 Declarative Over Imperative, P4 Continuous Reconciliation)
- A CustomResourceDefinition (CRD) defines the schema of the
domain resource. The controller watches instances of that CRD
and reconciles current → desired (P4).
- The CRD is the public contract of the operator. Version it
(`v1alpha1``v1beta1``v1`) and preserve backward
compatibility — see `domains/api/versioning.md` for the general
API-evolution principles. A CRD is an API surface, not an
internal type.
```yaml
apiVersion: postgres.example.com/v1
kind: PostgresCluster
metadata:
name: payments-db
namespace: payments
spec:
replicas: 3
version: "16"
storage:
size: 100Gi
storageClass: fast-ssd
backup:
schedule: "0 2 * * *"
retention: 7d
```
- The controller reconciles this spec: creates StatefulSets, PVCs,
Services, backup CronJobs. The user declares intent; the operator
makes it so (P2, P6).
## The Control Loop (P4 Continuous Reconciliation, P8)
- The loop watches CRD instances, compares current vs desired, and
acts to converge. Drift (a hand-deleted pod, a failed backup) is
detected and corrected each loop (P4).
- An operator-managed resource should not be hand-edited (P8). The
operator owns the subordinate resources (StatefulSets, PVCs); a
manual `kubectl edit` on a subordinate is drift the operator will
overwrite.
## Operator SDK and OLM (P6 Composability, C6)
- The Operator SDK scaffolds a controller from a CRD (Go, Ansible,
Helm). Use it to avoid re-implementing the controller boilerplate.
- Operator Lifecycle Manager (OLM) installs, updates, and manages
operators as first-class cluster components. OLM is the package
manager for operators — the operator analogue of
`domains/kubernetes/helm.md` for workloads.
- An operator published via OLM is a versioned, catalog-tracked
artifact. Pin the operator version; do not float `latest` (P5
applies to operators as much as to manifests).
## When to Write an Operator vs a Helm Chart (P6, C6 Composability)
| Axis | Helm chart | Operator |
|------|-----------|----------|
| Day-2 operations | None — chart installs, you operate | Encoded — operator reconciles lifecycle (backup, resize, failover, upgrade) |
| State | Static manifests | Live control loop watching CRDs |
| Day-1 install | Strong fit — package and install | Overkill if install is all you need |
| Day-2 reconcile | None — drift is manual | Continuous — drift corrected each loop |
| Domain knowledge | Lives in runbooks + on-call | Lives in the controller code |
| Best for | Off-the-shelf apps, stateless services, one-shot deploys | Stateful apps, complex lifecycles, day-2 automation (backup, scale, failover, version upgrades) |
| Watch out for | Templating complexity, no day-2 reconcile | Controller complexity, multi-team maintenance burden, scope creep |
- Write an operator when the day-2 operations (backup, failover,
resize, version upgrade) are non-trivial and repeated. Write a
Helm chart when install is all you need and day-2 is run by a
human or a separate tool.
- Do not write an operator to wrap a Helm chart and call it day-2
automation — that is a Helm chart with extra steps. See
`domains/infrastructure-as-code/modules.md` for the
module-vs-copy boundary (the operator-vs-chart boundary is its
analogue).
## Scope and Responsibility Boundaries (P10 Least Privilege, C6)
- An operator owns one domain. An operator that manages databases
and message queues and certificates is doing three jobs — split
it. Scope creep is the most common operator failure mode (P6
violation: the encoded knowledge is no longer coherent).
- The operator's ServiceAccount must be scoped to the resources it
manages (P10). A database operator that needs `cluster-admin` to
create a StatefulSet has the wrong RBAC — see
`domains/kubernetes/rbac.md` and `domains/security/authorization.md`.
- One operator per CRD family; one ServiceAccount per operator; one
namespace per operator (or a shared `operators` namespace with
strict RoleBindings). Default namespace is for nothing in
production.
## Failure and Observability (P9 Failure is Observable and Surfaced)
- An operator must surface its reconcile status on the CRD
(`status.conditions`, `status.observedGeneration`). A CRD with no
status is an operator that fails silently (P9).
- Wire operator events to notifications and metrics. A failed
backup, a stuck failover, a version-upgrade stall must emit a
signal — see `domains/observability/metrics.md`.
- An operator that reconciles but does not report health is a
black box. The GitOps controller (ArgoCD/Flux) will read it as
`Progressing` forever — write the health check (see `argocd.md`
"Health and Status").
## What Violates Operator Discipline
| Violation | Principle |
|-----------|-----------|
| Operator that manages databases + queues + certs | P6 Operators Encode Domain Knowledge (scope creep) |
| Operator ServiceAccount bound to `cluster-admin` | P10 Least Privilege Reconciliation |
| CRD with no `status.conditions` | P9 Failure is Observable and Surfaced |
| Operator with no health check wired to GitOps | P9, `argocd.md` Health and Status |
| Unversioned CRD (`v1` shipped without alpha/beta) | P5, `domains/api/versioning.md` |
| Manual `kubectl edit` on an operator-managed subordinate | P8 Reconcile, Don't Mutate by Hand |
| Operator that wraps a Helm chart and adds no day-2 logic | P6 (no knowledge encoded) |
| Operator runbook that exists only in a wiki | P6 Operators Encode Domain Knowledge |
@@ -0,0 +1,177 @@
# Progressive Delivery — Derived Rules
> Derives from `domains/gitops-operators/first-principles.md`.
> Applies P7 (Progressive Delivery is Reversible by Construction)
> primarily, with P4, P9. Cross-links `domains/devops/first-principles.md`
> P4 Rollback First and P5 Progressive Delivery, and
> `domains/observability/metrics.md` for the analysis signals.
## What Progressive Delivery Is (P7 Reversible by Construction)
- Progressive delivery shifts traffic in stages (canary, blue-green)
gated by analysis (metrics, counters, error rates). Each stage is
metric-checked; a failed gate aborts the rollout and reverts to
the prior stable version. Promotion without a rollback path is a
violation (P7).
- Progressive delivery is the GitOps extension of
`domains/devops/P5 Progressive Delivery` and
`domains/kubernetes/P10 Roll Forward, Roll Back`. The k8s rolling
update is the floor; progressive delivery adds metric-gated
promotion and one-command abort.
- Two sister projects dominate: **Argo Rollouts** (Argo ecosystem)
and **Flagger** (Flux ecosystem). Both implement the same pattern
— a Rollout CRD replaces a Deployment, an analysis drives the
gates, an abort reverts traffic.
## The Rollout CRD (P2 Declarative Over Imperative, P7)
- A Rollout (Argo Rollouts) or Canary/Flag (Flagger) is a CRD that
replaces the Deployment as the reconciled resource. It declares
the strategy (canary, blue-green), the traffic split, and the
analysis gates. The controller reconciles traffic and pods to
match.
```yaml
apiVersion: argoproj.io/v1alpha1
kind: Rollout
metadata:
name: payments-api
namespace: payments
spec:
replicas: 10
selector:
matchLabels:
app: payments-api
template:
metadata:
labels:
app: payments-api
spec:
containers:
- name: api
image: registry.example.com/payments-api:1.2.3
strategy:
canary:
trafficRouting:
istio:
virtualService:
name: payments-vs
routes: [primary]
steps:
- setWeight: 5
- pause: { duration: 2m }
- analysis:
templates:
- templateName: success-rate
- setWeight: 25
- pause: { duration: 5m }
- analysis:
templates:
- templateName: success-rate
- setWeight: 50
- pause: { duration: 5m }
- setWeight: 100
```
- Each `setWeight` shifts traffic; each `pause` holds for
observation; each `analysis` runs a metric gate. A failed
analysis aborts the rollout and reverts traffic to the stable
ReplicaSet (P7).
## Canary vs Blue-Green (P7, C3 Simplicity)
| Strategy | Mechanism | Cost | Best for |
|----------|-----------|------|----------|
| Canary | Shift a small % of traffic to the new version; increase on gate success | Low (few new pods) | Most production rollouts; metric-gated, gradual |
| Blue-Green | Run two full environments; switch traffic all-at-once | High (2× capacity) | Schema-breaking changes, instant rollback, low-frequency deploys |
- Canary is the default — it is reversible by construction (P7)
and economical (C8). Blue-green is for changes that cannot be
partial (a breaking schema migration, a full cutover).
- A canary with no analysis gate is a slow blue-green — it is not
progressive delivery. The gate is what makes it progressive (P7).
## Analysis Templates (P9 Failure is Observable and Surfaced, P7)
- An AnalysisTemplate declares the metric query, the success
threshold, and the count of samples. The rollout controller runs
the analysis at each gate; a failed analysis aborts the rollout.
```yaml
apiVersion: argoproj.io/v1alpha1
kind: AnalysisTemplate
metadata:
name: success-rate
namespace: payments
spec:
metrics:
- name: success-rate
interval: 1m
successCondition: result[0] >= 0.99
failureLimit: 2
provider:
prometheus:
address: http://prometheus.observability:9090
query: |
sum(rate(http_requests_total{job="payments-api",code!~"5.."}[2m]))
/
sum(rate(http_requests_total{job="payments-api"}[2m]))
```
- `successCondition` is the gate; `failureLimit` is the tolerance
for transient blips. A single failed sample aborts immediately if
`failureLimit: 0`; tolerate noise with `failureLimit: 2`.
- The metric is the abort signal — see `domains/observability/metrics.md`
for the SLI/SLO discipline that makes the gate meaningful. A gate
on an undefined SLO is a gate on noise.
## Argo Rollouts vs Flagger (P6 Composability, P7)
| Axis | Argo Rollouts | Flagger |
|------|---------------|---------|
| Ecosystem | Argo (ArgoCD sister project) | Flux (Flux sister project) |
| CRD | `Rollout` (replaces `Deployment`) | `Canary` / `Flag` (wraps a `Deployment`) |
| Traffic providers | Istio, NGINX, ALB, SMI, Traefik, Ambassador | Istio, NGINX, Linkerd, SMI, App Mesh, Gloo, Contour |
| Analysis sources | Prometheus, Datadog, Wavefront, NewRelic, CloudWatch, Graphite, Kayenta | Prometheus, Datadog, CloudWatch, Stackdriver, Elasticsearch, Graphite |
| Integration | Tight with ArgoCD (UI shows rollout) | Tight with Flux (events via notification-controller) |
| Learning curve | Rollout CRD replaces Deployment (migration cost) | Wraps existing Deployment (lower migration cost) |
| Best for | ArgoCD shops wanting rollout in the Argo UI | Flux shops wanting progressive delivery with minimal migration |
- Both implement the same pattern. The choice follows your GitOps
controller — Argo Rollouts with ArgoCD, Flagger with Flux. Mixing
is possible but not idiomatic.
## Abort and Rollback (P7 Reversible by Construction, P5)
- An abort reverts traffic to the stable ReplicaSet immediately. A
rollout without a tested abort is a prototype (P7).
- The abort must be one-command (or one-gate-failure). A
progressive delivery that requires manual rollback steps has
lost the "reversible by construction" property — it is a deploy
with extra steps.
- Test the abort path in staging. An abort that has never been
exercised will fail when you need it most — see
`domains/devops/first-principles.md` P4 Rollback First.
## Observability (P9 Failure is Observable and Surfaced)
- Progressive delivery is only as good as its metrics. A rollout
gated on a metric that is not tracked is ungated — the gate is
theater (P9).
- Wire rollout status (phase, weight, analysis result) to
notifications and dashboards. A stalled rollout with no signal is
silent drift (P9). See `domains/observability/metrics.md`.
- Cross-link `domains/kubernetes/workloads.md` for the underlying
Deployment/ReplicaSet model that progressive delivery replaces.
## What Violates Progressive Delivery Discipline
| Violation | Principle |
|-----------|-----------|
| Canary with no analysis gate | P7 Progressive Delivery is Reversible by Construction |
| Rollout with no tested abort path | P7, `domains/devops/P4 Rollback First` |
| Analysis gate on an undefined SLO | P9 Failure is Observable and Surfaced |
| Blue-green with no 2× capacity budget | C8 Economy (blue-green is a cost decision) |
| Rollout stalled with no notification | P9 Failure is Observable and Surfaced |
| Manual `kubectl` traffic shift on a Rollout-managed service | P8 Reconcile, Don't Mutate by Hand |
| `failureLimit: 0` on a noisy metric (constant false aborts) | P4 Continuous Reconciliation (gate noise tolerance) |