Introduction: Why an AI agent for customer support has become essential today
Companies seeking to offer 24/7 assistance without sacrificing quality face a complex challenge. A dedicatedAI agent for customer supportcombines speed, accuracy and personalization, meeting the expectations of modern users. In this article you will discover when and why it is worth implementing it, which models to choose and how to do it in 2026.
Why a specialized AI agent is the best choice in 2026
Current trends driving this choice
- Inference speed:Zyphra's new FP8 kernels (ZYFA) on AMD MI355X deliver up to 1.76x faster performance, reducing response times to milliseconds. Together AI's Together Compiler can boost inference speed up to 2.6x by continuously re-optimizing the forward pass.
- MoE models:Inkling 2 Small (594B with 17B active) and ERNIE 5.5 Flash Base (280B with 14B active) provide an optimal balance between capability and computational resources.
- Scalability:Frameworks such as LangChain and MoE-optimized inference APIs enable handling request spikes without slowdowns.
- Customization:Few-shot learning combined with well-structured prompts allows adapting the agent to specific domains, such as multilingual support.
These factors make the AI agent not just an interesting option, but a strategic solution for those who want to stay competitive.
How to build an AI agent for customer support: a step-by-step guide
1. Choose the base model
In 2026, MoE models dominate the landscape. Inkling 2 Small offers an excellent performance-to-parameter ratio, while ERNIE 5.ilina 5.5 Flash Base excels at processing large volumes of multilingual text. Evaluate your computational budget and language requirements before deciding.