Why Single-Purpose AI Agents Outperform All-in-One Setups

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A software developer found that running one AI agent across all projects caused it to misapply rules and produce inconsistent output, such as writing landing page copy in the tone of a commit message. Drawing on software engineering principles, he refactored his setup so each agent handles only one clearly defined job within its own isolated workspace. The key insight is that an agent's memory becomes unreliable when it stores rules from multiple contexts, since a rule valid for one project can be actively harmful in another. Isolated agents coordinate indirectly through a publish-subscribe event system overseen by a supervisor agent, rather than communicating directly with each other. The author reports that narrower workspaces produce more accurate, compounding memory and make it far easier to audit what an agent has learned.

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