A simple fact: the key to determining whether AI has become an Agent—a model application that can call external tools and continue acting based on the results—is not whether it talks more like a human, but whether it has a “tool call—read result—adjust action” loop. This framework explains why ChatGPT’s web access, Claude Code’s coding capabilities, and Coze plugins share the same underlying product logic. It also puts the capability boundaries enterprises actually need to address front and center.
What this is
The article offers a concise formula: Agent = LLM (large language model) + Planning (breaking complex tasks into steps) + Memory (retaining history, preferences, and intermediate state) + Tools (external capabilities such as retrieving data, executing code, and sending emails), all running continuously through a loop.
For example, a system could check Beijing’s weather for the next day and remind someone to wear warmer clothes if the temperature falls below 10°C. A conventional chatbot might simply provide a plausible-looking temperature. An Agent, by contrast, first requests access to a weather API, obtains real data, and then determines whether to trigger the reminder. The model proposes actions and explains the results; external programs perform the actual execution and enforce permissions.
The ReAct (reasoning and acting) loop can be divided into five broad steps: receive the task, think through the next step, call a tool, read the result, and decide whether to finish or continue. The article emphasizes that without a loop and tool calls, a system is usually just a chatbot with memory and should not be called an Agent.
Industry view
We believe the framework’s value lies in shifting product discussions from “How intelligent is the model?” to “Is the system reliable?” When enterprises deploy Agents, the hard part is often not generating a plan, but connecting internal data, restricting operational permissions, handling API failures, and making every action auditable. The more capable the model, the more clearly organizations must define which actions require approval.
The risks are equally clear: the framework itself does not automatically solve hallucinations—the model generates plausible-looking but incorrect information—incorrect tool selection, or tasks caught in infinite loops. Once capabilities such as code execution, sending emails, and modifying files are misused, the impact expands from an erroneous response to real business actions. If memory becomes contaminated with outdated or sensitive information, subsequent decisions may continue to drift off course.
That is why what the article describes as “the model requests tools, and code executes” is not only a technical design choice but also a security boundary. The maturity of an Agent cannot be judged merely by whether a demo works. We also need to know whether it stops when a step fails, blocks unauthorized actions, and makes operations traceable after execution.
Impact on regular people
For enterprise IT: The realistic near-term value is not replacing entire systems. It is first connecting Agents to low-risk, reversible workflows such as reporting queries, document organization, and internal knowledge retrieval. Existing systems should remain in control of permissions, logs, and approvals.
For individual professionals: Assistants that can call calendar, document, and code tools may reduce repetitive work, but key conclusions still require human verification. The genuinely valuable capability is completing an inspectable workflow, not merely generating longer text.
For the consumer market: Chat assistants will gradually shift from “answering questions” to “acting on a user’s behalf,” such as researching information, booking tickets, and processing documents. Consumers will also pay closer attention to when an assistant can call external services, how its data is used, and who is responsible when something goes wrong.