An April 2026 post on Nowcoder mentioned a candidate freezing when the interviewer pressed them on, "How do you handle the next Thought when a tool returns empty results?" AI Agent interviews have upgraded from testing definitions to testing engineering capability—this signal deserves our attention.
What this is
ReAct (Reasoning + Acting, alternating cycles of inference and action) is the most mainstream design pattern for AI Agents: Thought reasons about current state → Action calls tool → Observation receives result → loop until outputting the answer. Each step's Observation feeds back into the next Thought, and the model re-decides based on new information. It has been widely implemented for three reasons: Thought acts as a translation layer for "result → judgment"; execution traces are traceable and easy to debug; different tools can be bound to each step. But the core risk is that Thought reasoning can drift, and the model will continue along the wrong conclusion. The solution is to add structured constraints in the prompt—such as "check whether the previous observation meets expectations before generating the next step."
Industry view
Proponents argue that ReAct translates the model's implicit reasoning into a readable sequence, and combined with LangSmith (an AI application observability tool), problems can be located step-by-step, making it the most pragmatic Agent paradigm currently. However, the opposition is equally clear: loops can become infinite loops, so dual safeguards of an iteration limit and termination words must be set; the decision between parallel and serial tool calls depends on data dependencies rather than speed, leading to high engineering complexity; a more fundamental critique is that most developers cannot articulate which Agent capabilities change and which remain constant when switching to a stronger or weaker model, indicating that understanding of architectural boundaries remains at the assembly stage. We note that the shift in interview questioning is itself a bellwether of industry maturity: definition questions weed out those who haven't entered the field; crash recovery questions weed out those who haven't gone live.
Impact on regular people
For enterprise IT: An Agent isn't just plugging in a model; Thought quality control and failure recovery are mandatory engineering costs—don't be misled by demos. For individual careers: AI job interviews have upgraded from "What is ReAct?" to "How to handle empty tool results?"; memorizing definitions isn't enough, you need live troubleshooting experience. For the consumer market: Highly explainable Agents are more likely to see initial adoption—users can see what the AI is thinking, which lowers the trust threshold.