Narrow AI Agents Outperform All-in-One Bots, Developer's Production Tests Show

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A software developer shared lessons from building a general-purpose AI agent that could handle web search, file editing, Slack messaging, database queries, and more, only to find it consistently produced errors in production. The agent's failures — including wrong calendar bookings, a repo-reformatting pull request, and outdated pricing answers — all stemmed from having too many tools and no clear, singular purpose. The developer replaced the bloated agent with tightly scoped specialist agents, each given a one-sentence job description and only the tools strictly necessary for that task. In one documented case, a billing specialist agent with just three tools dramatically reduced wrong answers by restricting the model to a small, verified dataset and blocking it from browsing unreliable public sources. The core finding is that AI agent reliability improves not by upgrading the underlying model, but by narrowing the agent's scope, tools, and success criteria.

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