vivo’s connected R&D team has mapped Agent evolution along a single technical line: from prompt engineering to RAG, Harness engineering, and ReAct loops, and then to multi-agent collaboration. Our judgment is that the industry’s focus is shifting from “whether the model can answer” to “whether the system can deliver reliably,” and the latter remains far from mature.
What This Is
An AI Agent—an intelligent agent capable of autonomously planning, calling tools, and completing tasks—is not a standalone model but an engineering system. Prompt engineering clarifies goals and constraints; RAG, which retrieves enterprise information before the model answers, addresses knowledge freshness and sourcing; Harness engineering handles execution, logging, and correction; ReAct loops let models alternate between reasoning and action; and multi-agent collaboration assigns complex tasks to different roles.
The original article frames this evolution as a chain of responses to earlier limitations. A stage that cannot run reliably gives rise to the next. A foundation model, for example, may generate a sequence of steps but fail to execute them consistently, retain context, or know when to stop. The real difficulty is not “generating a plausible plan,” but closing that loop reliably under real permissions, abnormal data conditions, and external systems.
Industry View
We agree with the article’s technical framework: as enterprises adopt Agents, the priority should move from model parameters to task design, knowledge integration, access control, and outcome acceptance testing. A customer-service assistant connected only to internal documents and an Agent capable of checking orders, modifying inventory, and processing refunds operate at entirely different levels of risk and engineering complexity.
However, the article’s account of technological progress is somewhat too smooth. Multi-agent systems are not inherently superior to single-agent systems: the more roles involved, the higher the communication overhead, latency, calling costs, and error propagation typically become. ReAct can also amplify model misjudgments—a single incorrect tool choice may turn an error into a real-world operation. Greater autonomy therefore does not necessarily mean greater business value. In sensitive domains such as finance, healthcare, and customer data, auditability, rollback capability, and human confirmation may matter more than the level of autonomy.
Impact on Regular People
For enterprise IT: Agents will drive the reintegration of knowledge bases, workflows, and system permissions. In the near term, the quickest wins will generally come not from fully unattended systems, but from process-oriented applications that can retrieve information, draft content, issue reminders, and pause for human confirmation at critical points.
For individual professionals: Repetitive information organization, first drafts, and cross-system lookups will gradually be delegated to tools, but users will still be responsible for reviewing the results. What creates real value is an Agent that can connect to enterprise data, understand permission boundaries, and leave a record of its actions—not a chatbot that occasionally gives the wrong answer.
For the consumer market: Customer service, shopping assistants, and software companions will increasingly take more steps proactively on users’ behalf, but incorrect execution, privacy authorization, and platform responsibility will rise with them. If companies only demonstrate what their Agents can do without explaining permissions, human confirmation, or data-use boundaries, they will ultimately bear the cost of eroded trust.