Physix FrontierLine up AI agents for sequential tasks: A beginner's copy-paste guide
Line up AI agents to work as a team; I ran it for an afternoon, and there were more pitfalls than surprises.
I compared solo operation vs. team-based approaches for running AI coding agents, actually testing AI DevKit. Conclusion: Worth it, but with a premise—you need to master individual agents first before talking about teams. This tool is prepared for people who "already have one agent doing work for them," not for complete coding novices.
First, the concept. An agent is an AI that can write code, run commands, and modify files on its own, equivalent to a programmer paid hourly (actually per token). Usually, we use Cursor or Claude Code, chatting "you ask, it answers." What AI DevKit does is enable a bunch of agents to message each other, share memory, and divide tasks. Plainly put: Previously, you contacted one outsourcer directly; now you're a contractor with several outsourcers under you, and the tool handles communication and scheduling.
It took me about an afternoon to get it running. The path: First, pull the project from its GitHub repo, install dependencies locally, then type the startup command in the terminal. It asks which agents to use in the project; I selected Cursor and Claude Code to see if they could cooperate well.
After startup, a console interface pops up. It looks like a large panel: status lights for all agents on the left, task queue on the right, and message stream in the middle. Everything each agent is doing, which step it's at, and what results it submitted scrolls in real-time.
My first assigned task was simple: Have Agent A write a Python script to process CSV, and Agent B perform code review. Steps:
1. Create two new tasks in the console, assigning them to the two agents respectively.
2. Write clear description for Agent A's task: "Read data.csv in the directory, clean empty rows and duplicates, output clean.csv."
3. Write for Agent B: "Review Agent A's output, check for unhandled edge cases."
4. Mark Agent A's task as a dependency for B, telling B to wait until A finishes before starting.
Then you can just watch. After A finishes, B automatically continues on A's output. In between, they shout at each other in the message stream. For example, B asks A, "Why didn't you remove the units from this field?" A replies, "Training data didn't include units." This mutual confirmation process is the biggest difference from other AI coding tools.
I won't hide it: more pitfalls than surprises. The worst pitfall was letting two agents modify the same file simultaneously. The one that finished writing last completely overwrote the previous changes. Not a git conflict, but total overwrite, unrecoverable. The reason isn't hard to understand: each agent's context has limited visibility; it doesn't know what colleagues did. Although the console has a cross-agent messaging mechanism, agents don't proactively check. You need to clearly define boundaries when assigning tasks.
So, one piece of experience: Task decomposition is more important than the tool itself. At any given moment, let only one agent write files; the other must switch to "wait for output then review" mode. Ensure serial arrangement. Parallelism feels great for a moment, but merging code is a funeral pyre. If you don't want to decompose tasks, don't let them touch the same file.
The second pitfall relates to memory. Previously, I thought agents couldn't even understand settings pages; I need to correct that halfway now. AI DevKit has a locally searchable memory bank. What A does can be stored, and B can browse it. But this memory is archival, not auto-instilled. B doesn't automatically understand just because A did it. If you don't explain the background clearly, it will still make mistakes on the same issue. The tool solves communication channels, not instruction quality.
This tool solved a real problem: After AI coding evolves from "one person working" to "a group working," who acts as the "person"? The answer is this control plane. But it's essentially still new, documentation isn't comprehensive. I got stuck two or three times, figuring things out by reading source code. Novices shouldn't deploy five agents immediately; chaos ensues. I recommend max two; get one run successful before scaling up.
Next, try its skills feature. Solidify repetitive processes into reusable workflows, e.g., "Automatically run tests and commit after writing code," letting agents follow the process themselves. Run a real small project for half a day, and you'll know where the upper limit of this thing is.
📌 This article is compiled from Hacker News. Original: https://codeaholicguy.com/2026/08/19/how-i-run-ai-coding-agents-as-a-team-with-ai-devkit/
All rights reserved by the original authors. This is a compilation and independent analysis based on public reports.
