Is landing an Agent too difficult? Don't just focus on the model; the problem lies in "orchestration"

Many people think building an Agent is just tuning an API, but once you get hands-on, you'll find: the biggest pitfall is often not the model, but your workflow orchestration.
Even if the tool invocation is designed beautifully, it's useless if the process can't be connected smoothly. Here are a few seriously underestimated hardcore points:
🧠 State machines and Memory: Context management isn't just about storing conversations; whether the state machine is well-designed directly determines if the Agent can handle complex, long tasks.
🛡️ Fault tolerance is "breathing": Retry and fallback mechanisms must be embedded in the code. If it crashes at the slightest jitter, it's just a demo, not a product.
🤝 Human-in-the-loop: AI isn't meant to completely replace humans but to let people handle parts that machines can't manage at critical points. This kind of collaboration mode is the future's killer app.
Agent failures are nine times out of ten due to process deviations, not model inadequacies.
It's like a top racing driver with a poor chassis; no matter how powerful the engine, you can't achieve top speed.
The upper limit of an Agent depends on the model, but the lower limit is definitely about the art of engineering orchestration.