Generative AI tools are widely used for code generation, with 91% of developers employing them. Experts recommend concrete best practices such as validating AI output, maintaining human oversight, integrating testing, and using version control to reduce errors. Over-reliance on a single tool creates blind spots and reduces robustness. Multi-agent workflows allow different AI assistants to complement each other's strengths and handle complex coding tasks more effectively. Many AI agents fail to understand business context, requiring domain-specific training and human-in-the-loop correction. Providing an 'undo' option for AI mistakes can improve recoverability, trust, and practical adoption.
AI agents are undeniably powerful, but wielding that power responsibly is another story.
According to a recent survey, 91% of developers are using AI for code generation.
One emerging best practice of the genAI era is: Don't treat any single tool as the solution to every problem.
Just as humans have specific strengths, so do AI coding assistants.
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