Real-world reasoning: How Amazon Nova Lite 2.0 handles complex customer support scenarios

Artificial intelligence (AI) reasoning capabilities determine whether models can handle complex, real-world tasks beyond simple pattern matching. With strong reasoning, models can identify problems from ambiguous descriptions, apply policies under competing constraints, adapt tone to sensitive situations, and provide complete solutions that address root causes. Without robust reasoning, AI systems fail when faced with nuanced scenarios requiring judgment, context awareness, and multi-step problem-solving.

This post evaluates the reasoning capabilities of our latest offering in the Nova family, Amazon Nova Lite 2.0, using practical scenarios that test these critical dimensions. We compare its performance against other models in the Nova family—Lite 1.0, Micro, Pro 1.0, and Premier—to elucidate how the latest version advances reasoning quality and consistency.

Solution overview

We evaluate five Amazon Nova models across five customer support scenarios, measuring performance on eight dimensions:

• Problem identification
• Solution completeness
• Policy adherence
• Factual accuracy
• Empathy and tone ...