It seems like you've shared a detailed account of your experience in building and refining SEO agents using AI. Here are some key takeaways and insights from your narrative:
Key Takeaways
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Iterative Development: Your crawler went through multiple iterations, starting with basic HTTP requests to more sophisticated techniques like user-agent string inclusion and following actual links rather than guessing paths.
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Quality Control: Building a reviewer agent first was crucial for maintaining quality. This step ensured that findings were accurate and actionable before they reached clients or stakeholders.
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Validation Standards:
- Google Engineer Test: Ensures the finding would make sense to someone familiar with Google's algorithms.
- Developer Test: Ensures the recommendation is clear enough for a developer to implement without additional questions.
- Agency Reputation Test: Ensures the finding stands up to scrutiny in client meetings.
- Implementation Test: Ensures recommendations are specific and actionable.
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Sandbox Testing: Using sandbox websites with known issues allowed you to train and test your agents effectively, ensuring they could identify and report on real-world SEO problems accurately.
Detailed Insights
Iterative Development of the Crawler
- Initial Challenges:
- Bare
Read the full article at Search Engine Land
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