A technical deep-dive on Juejin offers a concrete case: when a user types "what industries am I most exposed to in my portfolio," six layers of Agent (an AI system that autonomously calls tools and completes tasks step-by-step) are traversed before an answer emerges. What we care about is this: the real difficulty isn't whether the model is smart, but the engineering architecture choices around "letting the model decide the next step."
What this is
The article dissects an ETF Agent financial scenario: when a user asks "how risky is my fund portfolio," the system first determines whether the message is a duplicate, whether research is needed, and whether to call tools to fetch data. It then uses a Projector (a module that uniformly assembles context and personal background) to feed information to the model. The model proposes the next action; the Host (the execution gateway) pulls fund data and runs deterministic calculations; results flow back to the model for further reasoning; finally, a report is generated and delivered to the user.
Throughout, Langfuse (an open-source observability tool that records every step of AI execution) runs in the background: which stage takes how long, what tools the model invoked, how good the output is. Notably, in the "research loop," the model can repeatedly call tools and conduct multi-turn reasoning—but each additional layer adds another potential point of failure. Under network jitter, the same question can even traverse different paths.
Industry view
The developer community broadly endorses this layered approach, viewing it as the necessary path for Agents to reach production—OpenAI's Function Calling and Anthropic's MCP protocol (a standard for connecting models to external tools) are doing similar things. The author also stresses that layering makes "execution records stable and attributable," which is what enables debugging and replay.
But we notice a dissenting voice that has been sidestepped: financial scenarios demand high certainty and explainability, and "the model deciding the next step" is precisely where compliance risk lives. A senior fintech practitioner, when privately evaluating similar architectures, once put it bluntly—the demo looks beautiful, but in production, when a customer asks "where did this conclusion come from," engineers struggle to explain it in under ten minutes. The article itself admits: observability is a post-hoc accountability tool, not a pre-hoc guarantee.
Impact on regular people
For enterprise IT: companies looking to deploy Agents must first clarify who owns which of the six layers. Otherwise what ships is a black box, and when something goes wrong, no one can replay what happened.
For individual professionals: researchers' and analysts' tools will get better, but "final judgment + compliance sign-off" remains a human role in the short term—because the responsible party cannot be delegated to a model.
For consumer markets: investors shouldn't expect AI Agents to reliably deliver fund advice anytime soon. Demo videos and stable products are two very different things.