The article argues that the most effective agent products are those that are opinionated rather than flexible. The author emphasizes that flexibility often leads to a "flexibility trap," where users are overwhelmed with options and settings instead of focusing on outcomes. By taking a strong stance on tool design and prompts, developers can create agents that perform specific tasks reliably. The author advises prioritizing user experience by minimizing configuration options and handling complex tasks on behalf of users. This means doing extensive testing and refining prompts and tools to ensure the agent works effectively right out of the box. Key principles of opinionated agent design include reducing the number of settings, obsessively refining prompts, and using dogfooding—where teams use their products—to understand their effectiveness. The article critiques the myth of general-purpose agents, which often compromise task performance for broader applicability. It suggests that many developers default to general-purpose designs due to a lack of clear opinions on their products, leading to inefficiency. Instead, the author advocates for deep, narrow agents that excel at specific tasks, which can yield significant value while minimizing bugs and confusion. When it comes to harnessing models, the article highlights that models and their harnesses are interdependent. You can't evaluate a model without considering the design of its harness. The author warns that upgrading models can disrupt existing setups due to differences in their capabilities. Therefore, teams should focus on developing specialized agents tailored to particular tasks, as this approach leads to better task performance. The article also notes that organizations like Anthropic are creating dedicated teams to develop agent harnesses optimized for specific domains, recognizing that a focused approach enables more intelligent and effective tools.