Context as Code, DevX as Leverage: Accelerating Software with Multi‑Agent Workflows

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Nov 24, 2025

Max Beauchemin, a data engineering veteran and creator of Apache Airflow and Superset, discusses his shift to multi-agent development with Agor. He explores the concept of an 'AI-first reflex,' where humans orchestrate tasks while agents accelerate workflows. Max highlights how shifting bottlenecks like code review can be addressed through improved developer experiences and 'context as code.' He introduces Agor’s innovative platform, designed for managing git worktrees and collaborative environments, enabling richer visibility and parallelization in software engineering.

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INSIGHT

AI-First Reflex And Human Orchestrator

Max Beauchemin developed an "AI-first reflex" and uses agents for most tasks including legal research and writing.
He views his role increasingly as orchestrating agents while humans handle people and leadership.

INSIGHT

New Bottlenecks Emerge With Agent Speed

As teams accelerate with agents, bottlenecks shift to code review, QA, and async coordination.
Faster coding increases PR volume and raises mean time to first review as a new choke point.

ADVICE

Treat Context As Code

Keep the agent's context retrievable and organized by creating a repo-level context folder with small "context nuggets."
Treat context as code to make just-in-time retrieval easier and avoid dumping everything into one file.

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Summary
In this episode Max Beauchemin explores how multiplayer, multi‑agent engineering is reshaping individual and team velocity for building data and AI systems. Max shares his journey from Airflow and Superset to going all‑in on AI coding agents, describing a pragmatic “AI‑first reflex” for nearly every task and the emerging role of humans as orchestrators of agents. He digs into shifting bottlenecks — code review, QA, async coordination — and how better DevX/AIX, just‑in‑time context via tools, and structured "context as code" can keep pace with agent‑accelerated execution. He then dives deep into Agor, a new open‑source agent‑orchestration platform: a spatial, multiplayer canvas that manages git worktrees and shared dev environments, enables templated prompts and zone‑based workflows, and exposes an internal MCP so agents can operate the system — and each other. Max discusses session forking, sub‑session trees, scheduling, and safety considerations, and how these capabilities enable parallelization, handoffs across roles, and richer visibility into prompting and cost/usage—pointing to a near future where software engineering centers on orchestrating teams of agents and collaborators. Resources: agor.live (docs, one‑click Codespaces, npm install), Apache Superset, and related MCP/CLI tooling referenced for agent workflows.

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Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
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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

Interview

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?

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The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0