Layer researchAgent data engineering
Agent data engineering & memory pipelines
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-engineeringMachine-readable version of this page: /library/layers/r14-agent-data-engineering.md