{
  "id": 13604163,
  "title": "Ten things that go wrong when you build with a team of AI agents (part 1)",
  "url": "https://urgent.news/2026/10/11/ten-things-that-go-wrong-when-you-build-with-a-team-of-ai-agents-part",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-11T01:30:16.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/theducttapeio/ten-things-that-go-wrong-when-you-build-with-a-team-of-ai-agents-part-1-dho"
  },
  "original_language": "en",
  "account": "1. Agents fix the symptom, not the cause: When multiple AI agents are working on the same project, they often address the immediate issue rather than the underlying problem. For instance, they might implement a retry mechanism for a timeout error, but this might not resolve the root cause if the issue stems from the environment, such as insufficient memory on a machine. To overcome this, the article suggests pausing after a couple of fixes to identify the broader contextual factors affecting the problem. This might involve examining elements like memory allocation, network conditions, data integrity, or version compatibility. By doing this, the team can address the root cause instead of merely treating the symptoms.\n\n2. Scaling up exposes what was shared all along: Adding more resources, like extra workers, agents, or a faster machine, can sometimes expose hidden dependencies within the system. For example, two tasks that were previously running independently might start to conflict when carried out in parallel due to shared resources such as accounts, files, ports, or databases. To mitigate this, the article advises listing all shared resources before scaling. Then, it suggests either assigning dedicated resources to each parallel task or making shared resources explicit. By treating these failures as valuable feedback rather than setbacks, the team can identify and address hidden dependencies, leading to more robust and reliable systems.",
  "summary": "Recently I wrote about how I build a product alone with a team of seven Claude Code agents . One piece of feedback on it stuck with me: the parts where the agents went wrong are usually the most useful for other builders. So this post, and part 2 , are only about those parts. Not our specific bugs, but the patterns behind them. If you run more than one agent on the same project, you will meet…",
  "key_points": [
    "Agents address symptoms, not root causes",
    "Scaling reveals hidden dependencies",
    "Shared resources become apparent at scale"
  ],
  "editors_take": "Building with a team of AI agents reveals systemic flaws as it exposes hidden dependencies and treats symptoms rather than root causes, requiring a more nuanced approach to problem-solving.",
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}