Ten things that go wrong when you build with a team of AI agents (part 1)

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.

2. 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.