Data engineering for agent systems: metadata, governance, lineage, quality checks, and retrieval-ready content pipelines.

Data engineering

Overview

Agents depend on high-quality data and metadata. Data engineering ensures knowledge sources are structured, permissioned, fresh, and retrievable with high precision.

Key topics

  • Metadata strategies (owners, timestamps, tags, access scope).
  • Governance: permissions, retention, and redaction at ingestion time.
  • Data quality checks: duplicates, outdated pages, broken links.
  • Lineage and provenance for citations and audit requirements.
  • Content normalization for better retrieval and chunking.

Common pitfalls

  • Indexing everything without curation (noise overwhelms recall).
  • Missing metadata leading to poor filtering and low precision.
  • Stale content and broken sync pipelines.
  • No provenance mapping from outputs to source sections.

Recommended practices

  • Design ingestion as a pipeline with monitoring and alerts.
  • Enforce permission inheritance from source systems.
  • Use structure-aware chunking and maintain stable identifiers.
  • Continuously evaluate retrieval quality and improve content hygiene.

This page is intended to be actionable for engineering teams. For platform-specific details, cross-reference /platform/agents, /platform/orchestration, and /platform/knowledge.