Scaling Teams with Structured AI Agents
Organized Cursor into specialized agents for data engineering — improved team velocity by 20-30%.
The Problem
Without structure, AI coding assistants drift. A single generic context across pipeline design, testing, and reporting leads to inconsistency and wasted tokens.
The Solution
Five specialized agents, each owning a domain:
| Agent | Role |
|---|---|
| Data Engineer | Builds pipelines, models, scripts |
| Engineering Lead | Reviews design, enforces best practices |
| Quality Engineer | Validates data, runs tests |
| Project Manager | Tracks sprints, writes stakeholder updates |
| Presentation Builder | Creates summaries and decks |
How It’s Structured
Rules enforce workspace discipline — agents write to their own context, never overwrite each other’s work, and follow a review → update → commit cycle.
Commands make common workflows repeatable:
/review-pipeline— Lead or QE reviews new logic/summarize-analyst-findings— PM creates business-ready output/update-user-skills— refresh agent context over time
Skills (user-level knowledge) are shared across projects — dbt conventions, naming standards, validation templates — so learnings from one sprint carry into the next.
Workspace Convention
Two levels of context, each with a clear owner:
- User level — shared across all projects. Learnings, playbooks, snippets, reference docs. Agents infer from this automatically when starting new work.
- Workspace level — per repo. Each agent maintains their own context: notes, action items, validation reports, sprint state.
Generating all these files may make the model bloat — but if you keep context summarized and up to date, the agent knows exactly where to look, what to work on, and what to reuse. No hand-holding needed.
Result
- 20-30% faster sprint throughput
- Consistent reviews and quality checks built into every workflow
- Knowledge reuse across projects without re-explaining context
Published March 2025