#multi-agent

AI systems that work once under ideal conditions are interesting. AI systems that work reliably in production are valuable. In this final post of the series, I’ll share techniques for building robust AI workflows - connecting multiple reasoning steps, validating outputs along the way, and creating systems that improve through iteration.

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Language models are great at many things, but complex reasoning isn’t always their strong suit. Ask a straightforward question and you’ll get a decent answer. Ask something that requires multiple logical steps, and things get shaky. In this post, I’ll share two powerful techniques that transform how AI approaches problem-solving: step-by-step reasoning and action-oriented thinking.

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Have you ever wondered why some AI assistants feel genuinely helpful while others just regurgitate generic responses? The difference often comes down to how we guide and structure their behavior. In this post, I’ll share what I’ve learned about building AI systems that go beyond simple question-answering to become true problem-solving partners.

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