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Sprint 30 Engineering Log

Program Context

Phase: Phase 8
Sprint: Sprint 30
Status: Implemented (Awaiting Signed Commit Promotion)
Primary Architecture Layers: Orchestrator Messaging Layer, Telemetry Ingestion Pipeline

Architectural Intent

Implement broker abstraction for RabbitMQ/Kafka compatibility, enforce ordered event streaming, and normalize telemetry into an ML-ready schema for high-throughput ingestion.

Implementation Detail

Added Kafka-compatible routing and publishing contracts:
- KafkaRoutingModel
- InMemoryKafkaBroker
- KafkaTelemetryPublisher
Preserved RabbitMQ path and added strict ordering enforcement using:
- ordering_key
- stream_position
Added telemetry schema normalization envelope for ML ingestion via ml_record payload with:
- fixed telemetry.ml.v1 schema marker
- status normalization (status_code)
- stable feature keys and stream metadata
Extended unit/integration coverage for:
- broker abstraction behavior (RabbitMQ and Kafka)
- ordering violation dead-letter behavior
- normalized ML payload presence in emitted broker envelopes
Added Sprint 30 high-volume QA test with 2,000-event throughput validation.

Security and Control Posture

Broker transport remains decoupled from execution authorization controls.
Ordered stream enforcement reduces replay/reordering ambiguity in downstream analytics.
Normalized telemetry schema improves deterministic ingestion for ML pipelines.

QA and Validation Evidence

Validated with unit tests and QA artifact tests, including high-volume stream publishing.

Risk Register

Primary risk is functional drift between in-memory Kafka compatibility and production Kafka transport. Mitigation is to add a live Kafka adapter smoke suite in a follow-up sprint.

Forward Linkage

Sprint 31 extends this foundation with VectorVue cognitive feedback ingestion and policy-bound adaptation.

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