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Layer researchAgent data engineering

Agent data engineering & memory pipelines

By Murali SidLinkedInPublished

Status: complete (published 2026-08-19). Scope finalized at Batch A kickoff with maintainer POV; see findings.md and sources.md.

Scope inventory (v0)

  • Ingestion and preparation: connectors, parsing and OCR, chunking strategy, metadata enrichment (entities, ownership, freshness), PII handling and consent at ingestion
  • Purpose-driven curation: use-case-scoped corpora, multi-view/multi-card embedding design, LLM-guided taxonomy with classical topic modelling on embeddings, SME-in-the-loop curation workflows, knowledge hub editorial process
  • Memory feeding: promotion policies into entity and cross-domain memory tiers, TTL and refresh, ACL propagation into indexes, index lifecycle and re-embedding operations
  • Economics and quality: retrieval quality evaluation per view (recall and precision), embedding-vs-LLM cost engineering, data contracts with source owners

Challenged-default candidates

Full-corpus LLM processing vs LLM-guided curation with deterministic machinery (the founding technique of this track); one universal index vs use-case-scoped multi-view indexes. Each track proposes its final list at kickoff.

Files

findings.md and sources.md carry this track's research. The scoping brief and the vendor map were archived on 2026-08-23 (see archive/, decision D038).

Cite this page

Written by Murali Sid, Founder and AI Systems Architect, last updated 18 September 2026. Licensed CC BY-SA 4.0, so quote it freely with attribution.

Murali Sid (2026). Agent data engineering & memory pipelines. Agentic Architecture Skills. https://www.agenticarchitectureskills.com/library/layers/r14-agent-data-engineering

Machine-readable version of this page: /library/layers/r14-agent-data-engineering.md

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