Baidu's 'Partner' at WAIC: Three Signals for Agent Implementation That Interviewers Might Ask About

The most valuable information in this article is: Baidu Dazi became the only general-purpose agent selected as a "Treasure of the Hall" at WAIC 2026, while the global number of DAAs (Digital Active Assistants) is expected to reach 79.4 million. Combining these two numbers implies that the turning point for AI moving from "answering questions" to "being companions" has arrived.

As a fresh master's graduate preparing for AI algorithm job interviews, I've solved 300 LeetCode problems, but honestly, facing topics like the implementation of "agents," I feel somewhat insecure inside. Because interviewers will likely ask: Do you understand the difference between agents and traditional AI? Have you done anything similar in your projects? And Baidu Dazi's display at WAIC happens to give me a concrete case to think about.

Why was BA Dazi chosen as the "Treasure of the Hall" instead of other LLMs?

The selection dimensions for WAIC's "Treasure of the Hall" include technological content, market prospects, generality, and social/economic benefits. Baidu Dazi being the only selected general-purpose agent indicates it's not just "an AI that can chat," but possesses some scalable capability.

I carefully read the reports. Baidu Dazi is described as a "general-purpose agent," meaning it doesn't perform well only in specific domains like previous vertical-scenario AI assistants. It's more like "a person's AI companion," capable of chatting with you, planning schedules, and providing decision-making references. This "generality" is precisely the hardest bottleneck in current LLM implementation—many models score high on benchmarks, but in actual use, users often feel "it doesn't understand me."

Here's a detail: The number of DAAs (Digital Active Assistants) is expected to reach 79.4 million. If this number is true, it shows that agents are no longer lab products but have been accepted by a large number of users. For interviews, this data can be used to answer questions like "What do you think about the commercial prospects of agents?"—user scale has reached tens of millions, requiring the tech stack to support high concurrency, low latency, and personalization.

The original report mentioned: "Baidu Dazi appeared as a 'Treasure of the Hall,' being the only selected product of its kind among the ten Treasures of the Hall." This sentence reminds me that "technological content" and "market prospects" in selection criteria often contradict each other, but Baidu Dazi satisfying both suggests its technical solution may balance inference efficiency and emotional interaction.

Global DAA count of 79.4 million: Interview points hidden behind this number

DAA (Digital Active Assistant) refers to digital human assistants with active interactions. What does 79.4 million mean? I roughly estimated: if each DAA averages 10 interactions per day, that's nearly 800 million interactions daily. Behind this necessarily involve large-scale distributed inference, model serving, resource scheduling, and other engineering problems.

If an interviewer asks "How do you view the engineering challenges in LLM implementation?", I might start with this number: First, online service for hundred-billion-parameter models relies on latency and cost control. Second, multi-turn dialogue requires memory state management; traditional solutions use dialogue trees, but agents need more flexible context awareness. Baidu Dazi choosing the "general-purpose agent" route implies it might use architectures similar to "memory enhancement" or "tool calling," rather than simple prompt filling.

Additionally, I noticed this number is "Global DAA," suggesting Baidu might be doing cross-language, cross-cultural agent adaptation. This leads to another frequent interview question: How do multilingual models handle domain alignment? Is it direct pre-training on mixed corpora, or adding specific language data during fine-tuning? Currently, the mainstream industry practice is the latter, but effectiveness depends on data quality.

Looking at AI Algorithm Job Interview Trends from WAIC: Stop Just Grinding LeetCode

I recently interviewed with several companies and noticed a trend: Interviewers are increasingly focusing on "implementation capability" rather than "model capability." For example, they ask "How would you design a data loop for an agent?" instead of "Do you know Transformer's attention mechanism?"

Baidu Dazi being a "Treasure of the Hall" made me realize that the agent direction might be a hiring hotspot for the next two or three years. Why? Because general-purpose agents need to solve multiple difficult problems simultaneously: intent understanding, task planning, memory management, tool calling, and safety alignment. Each of these sub-problems can constitute a standalone project. Interviewers know no one can master them all, but if you can demonstrate thinking on a specific sub-problem, e.g., "How I used reinforcement learning to optimize dialogue strategy," it appears very pragmatic.

My own study plan is adjusting too: Besides grinding problems, I've started reading papers related to agents, such as architectural analyses of ReAct, Toolformer, and AutoGPT. Although interviews might not test this directly, at least I can discuss some industry insights.

[!tip] Interview Tip: If asked "Have you worked on any agent-related projects?", tell the truth, but be sure to supplement with your own thoughts. For example, "I didn't work on it directly, but I read Baidu Dazi's technical report and found it used xxx method. I believe the bottleneck of this method is xxx. If I were to improve it, I would try xxx." This is more persuasive than empty theorizing.

Summary

Baidu Dazi's selection and the 79.4 million DAA number jointly point to a core viewpoint: Agents are no longer concepts but are becoming the next explosive point in the AI industry. Preparation for algorithm jobs must shift from "grinding models" to "building products."

Original link: https://www.qbitai.com/2026/07/452956.html