Data Engineering Agent Architecture: From Prototype to Production with Datus

If your team is building a data engineering agent, architecture matters more than prompt tricks.

A robust system should answer three questions:

Where does context come from?
How is responsibility scoped?
How does quality improve over time?

Reference architecture

1) Context layer

Store and version:

metadata and lineage
metric definitions
validated reference SQL
domain rules and known edge cases

In Datus, this is handled by the Context Engine.

2) Agent layer

Use a router plus subagents:

router: intent classification and delegation
foundational subagents: SQL generation, summarization, semantic modeling
domain subagents: scoped business copilots

This reduces hallucination by narrowing the search space.

3) Execution and governance layer

Connect to your tools with controls:

warehouse and catalog connectors
scheduler/orchestrator integrations
permission checks and audit logs
evaluation and regression checks

Why Datus is effective here

Datus focuses on production requirements:

context that evolves with real workflows
reusable subagent patterns
human-in-the-loop corrections that become durable knowledge

Related:

Complete guide: https://datus.ai/blog/data-engineering-agent/
Subagent article: https://datus.ai/blog/data-engineering-agent/data-engineering-agent-layered-subagent

Make the article easy to cite and reuse

Clear structure helps both human readers and AI answer engines:

definition first
explicit architecture blocks
concise implementation checklist

Implementation checklist

Define 1 domain pilot (for example retention analytics)
Seed context with top 10 tables and top 20 metrics
Add 30 reference SQL examples
Launch one domain subagent
Run weekly evals and context updates

Key takeaways

Production agents are architecture + context, not prompt-only systems.
Subagent scoping is essential for reliability.
Datus provides a practical blueprint to scale safely.

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