Why Making Agents Move Is the Next Critical Step: Explainability, Tool Creation, and 100% Accuracy

The industry’s current focus is on building truly useful AI agents, and the key question has shifted to the agents' cognitive boundaries: how much autonomy they need, how strong contextual understanding must be, and how they can stay stable in real tasks.

Wang Wenfeng defines an agent as a product that leverages model knowledge, combines real‑time information, and uses tools to solve problems. He points out a gap: although foundational models have reached doctoral‑level performance, many agents still behave like interns.

Sheet0.com embodies his vision by providing a product that gives large models scalable real‑time data acquisition capabilities. Launched in August, it targets data analysts, product managers, engineers, and marketers who spend over five hours a week on data collection. A typical use case is a salesperson who previously needed weeks of engineering work to gather company information from LinkedIn and X; Sheet0 delivers a complete data table in 30 minutes.

Wang stresses that the core value is not merely time savings but delivering data that is 100% accurate and delivered quickly. He likens price elasticity in data work to economics: a ten‑fold cost reduction can generate dozens or hundreds of times more demand.

Explainability is highlighted as the next development milestone. By breaking every system action into traceable, reusable "semantic steps," agents can understand what they are doing and self‑repair when errors occur. He cites Claude Code as an inspiration, noting that each internal reminder carries the reason behind an action, allowing the agent to act with understanding rather than blind execution.

To build a reliable feedback loop, Sheet0 records failed tasks, compares them with successful ones, extracts the differences, and stores the successful paths in an internal knowledge base. This semi‑automatic process lets agents learn from failures and gradually improve without full automation.

Wang also discusses the broader market landscape: while some view Sheet0 as a traditional spreadsheet or crawler tool, he argues that the product’s purpose is to provide an extensible real‑time data layer for the AI era, enabling agents to take actions based on up‑to‑date information. He believes that focusing on trust—both users trusting the agent and engineers trusting the model—is essential, even if it slows short‑term progress, because "slow is fast" in the long run.

Looking ahead, Wang sees huge commercial potential in domains with high price elasticity, where reducing data acquisition costs dramatically boosts demand. He envisions agents that can autonomously create new tools, turning each new tool into a new skill for the model.

Finally, Wang shares personal entrepreneurial insights, emphasizing long‑term perseverance, the importance of belief, and the contrast between the bold, risk‑taking culture of U.S. AI startups and the more cautious, metric‑driven approach in China.