Release Material: Technical Deep-Dive (Engineers)

Problem

Production memory systems fail when write-path correctness is coupled to embedding pipelines, or when recall behavior cannot be audited from source data to runtime decisions.

Architecture Principles

1. Audit-first: writes persist as source-of-record nodes/edges/commits with replay and traceability.
2. Derived async: embedding, clustering, and quality features run through async backfill/outbox, isolating write availability.
3. Memory -> Policy: recall and rules feed planner/tool selection, and feedback updates policy behavior over time.

Evidence

1. Contract and docs checks pass in CI/release gates.
2. Consistency and health-gate checks verify invariants before publish.
3. Release artifacts are versioned across GitHub, Docker, npm, and PyPI.
4. Runbook and regression commands are documented and reproducible.

Boundaries

1. Benchmark suites like LoCoMo/LongMemEval are auxiliary regression signals, not sole production-readiness criteria.
2. Cross-tenant/high-concurrency tuning still depends on deployment profile, indexes, and queue policy.
3. Model-provider variance affects recall latency/quality and must be profiled per environment.

Next Step

1. Keep production KPIs as hard gates: p95 latency, write success, recall hit quality, queue lag, and cost per 1k requests.
2. Continue throughput governance hardening: batching, index evolution, pool/quota tuning, and backpressure.
3. Publish periodic evidence packs with exact command outputs and commit references.