Blurring Lines: Data, AI, and the New Playbook for Team Velocity
Data Engineering Podcast
00:00
Max's background and AI adoption
Max Beauchemin summarizes his Apache Airflow and Superset history and his rapid AI adoption across tasks.
Play episode from 02:44
Transcript
Transcript
Episode notes
Summary
In this crossover episode, Max Beauchemin explores how multiplayer, multi‑agent engineering is transforming the way individuals and teams build data and AI systems. He digs into the shifting boundary between data and AI engineering, the rise of “context as code,” and how just‑in‑time retrieval via MCP and CLIs lets agents gather what they need without bloating context windows. Max shares hard‑won practices from going “AI‑first” for most tasks, where humans focus on orchestration and taste, and the new bottlenecks that appear — code review, QA, async coordination — when execution accelerates 2–10x. He also dives deep into Agor, his open‑source agent orchestration platform: a spatial, multiplayer workspace that manages Git worktrees and live dev environments, templatizes prompts by workflow zones, supports session forking and sub‑sessions, and exposes an internal MCP so agents can schedule, monitor, and even coordinate other agents.
Announcements
Links
In this crossover episode, Max Beauchemin explores how multiplayer, multi‑agent engineering is transforming the way individuals and teams build data and AI systems. He digs into the shifting boundary between data and AI engineering, the rise of “context as code,” and how just‑in‑time retrieval via MCP and CLIs lets agents gather what they need without bloating context windows. Max shares hard‑won practices from going “AI‑first” for most tasks, where humans focus on orchestration and taste, and the new bottlenecks that appear — code review, QA, async coordination — when execution accelerates 2–10x. He also dives deep into Agor, his open‑source agent orchestration platform: a spatial, multiplayer workspace that manages Git worktrees and live dev environments, templatizes prompts by workflow zones, supports session forking and sub‑sessions, and exposes an internal MCP so agents can schedule, monitor, and even coordinate other agents.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I'm interviewing Maxime Beauchemin about the impact of multi-player multi-agent engineering on individual and team velocity for building better data systems
- Introduction
- How did you get involved in the area of data management?
- Can you start by giving an overview of the types of work that you are relying on AI development agents for?
- As you bring agents into the mix for software engineering, what are the bottlenecks that start to show up?
- In my own experience there are a finite number of agents that I can manage in parallel. How does Agor help to increase that limit?
- How does making multi-agent management a multi-player experience change the dynamics of how you apply agentic engineering workflows?
Links
- Agor
- Apache Airflow
- Apache Superset
- Preset
- Claude Code
- Codex
- Playwright MCP
- Tmux
- Git Worktrees
- Opencode.ai
- GitHub Codespaces
- Ona
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