Self-Learning scaffold

A complete program, not a fragment: it compiles as written. Copy it, rename the placeholder identifiers to your domain, and run axon check — the compiler will tell you what your renaming broke. An MCP client can also generate it through the axon.compose tool by naming the self_learning domain.

// AXON Self-Learning scaffold — continual learning loop with
// feedback-driven model improvement, mandate-gated deploys.
//
// This pattern targets ML-ops adjacent workflows: collect feedback,
// score, retrain (or fine-tune adapter), evaluate, deploy under a
// mandate. The mandate gates promotion to production — adopters
// who want fully-automated promotion can relax this.

// ── Types ─────────────────────────────────────────────────────────

type FeedbackSignal {
    interaction_id: Text
    outcome:        Text
    user_label:     Text
    confidence:     Number
    captured_at:    Text
}

type LearningBatch {
    batch_id:       Text
    signal_count:   Int
    summary:        Text
}

type DeploymentDecision {
    model_version:  Text
    promoted:       Text
    evaluation_id:  Text
    rationale:      Text
}

type SubmitFeedbackRequest { signal: FeedbackSignal }

// ── Memory + store ────────────────────────────────────────────────

memory FeedbackStore {
    store:     persistent
    backend:   postgresql
    retrieval: exact
}

// ── Mandate — gates production promotion ──────────────────────────

mandate PromotionApproval {
    constraint:   "Production promotion requires eval-set lift ≥ 2% + manual ML lead review"
    tolerance:    0.005
    max_steps:    1
    on_violation: halt
}

// ── Identity + grounding ──────────────────────────────────────────

persona MLLearningAgent {
    domain: ["mlops", "continual-learning", "model-evaluation"]
    tone: analytical
    confidence_threshold: 0.85
    cite_sources: true
}

context LearningRun {
    memory: persistent
    language: "en"
    depth: deep
    max_tokens: 4096
    temperature: 0.15
}

anchor EvidenceBackedPromotion {
    require: evidence_backed
    confidence_floor: 0.9
    unknown_response: "Insufficient evidence for promotion — keeping current model live."
    on_violation: raise InsufficientEvidenceError
}

// ── Shield ────────────────────────────────────────────────────────

shield LearningShield {
    scan:       [prompt_injection, training_poisoning, model_theft]
    on_breach:  quarantine
    severity:   critical
    compliance: [SOC2]
}

// ── Flow ──────────────────────────────────────────────────────────

flow IngestFeedback(signal: FeedbackSignal) -> FlowEnvelope<DeploymentDecision> {
    step Store {
        given: signal
        ask: "Persist the signal under the FeedbackStore namespace."
        output: FlowEnvelope<LearningBatch>
    }
    step EvaluateAndDecide {
        given: Store.output
        ask: "Aggregate the batch; run eval; emit promotion decision + rationale."
        output: FlowEnvelope<DeploymentDecision>
    }
    return EvaluateAndDecide.output
}

// ── HTTP boundary ─────────────────────────────────────────────────

axonendpoint FeedbackAPI {
    method:      post
    path:        "/v1/learning/feedback"
    body:        SubmitFeedbackRequest
    execute:     IngestFeedback
    output:      FlowEnvelope<DeploymentDecision>
    shield:      LearningShield
    backend:     auto
    compliance:  [SOC2]
    retries:     1
    timeout:     20s
    requires:    ["mlops.feedback.submit"]
}