A Coze team member said on November 2 that the product may stop operations next year. But the more important issue is this: even as autonomous Agents capable of planning for themselves take center stage, fixed workflows will not disappear, because the two serve different needs—consistency on every run and the ability to respond dynamically.
What this is
Coze is ByteDance's low-code AI development platform. Users drag and connect nodes to configure models, plugins, and conditional branches, building bots or automated tasks. The article notes that its early core value was filling the planning gaps of less capable models: specifying the order of steps, execution conditions, and failure retries.
Today, more powerful models such as Claude Opus 5.5 can break tasks down, call tools, and decide the next step on their own. The scheduling layer of a traditional workflow can therefore shift from an advantage to a constraint. Coze's potential exit does not mean “the workflow approach has failed”; it means the independent value of the low-code canvas is shrinking.
Industry view
We believe the technical direction is moving from “external workflows doing the thinking for models” toward “models plan autonomously while tools execute.” Products such as DeepSeek's super-agent point in the same direction: as model capability improves, hard-coded steps, conditions, and skill-scheduling layers gradually give way.
Workflows still have clear advantages. Tasks such as video production and financial processing require accurate results, reliable execution, and reproducibility. If every run produces a different outcome, delivery risk increases instead. The more reasonable division is to keep standard processes on workflows, while coding, troubleshooting, and open-ended research are better suited to autonomous Agents.
Cost and migration create risks as well. The number of calls, inference consumption, and error rate of autonomous Agents are generally harder to predict. Companies that commit all existing processes to a new model may gain flexibility while losing budget control. Tool compatibility can also change as models are upgraded: the same set of skills may amplify performance on an older model but become a constraint on a newer one.
Impact on regular people
For enterprise IT: Companies need not rush to rebuild entire systems because Coze may be shutting down. A more practical approach is to retain existing workflows while using small-scale business tests to evaluate autonomous Agents for accuracy, cost, and reproducibility.
For individual professionals: Process decomposition remains an important skill, but the key change is that the division of labor between people and AI is shifting from “designing fixed paths” toward “defining objectives, acceptance criteria, and exception-handling rules.”
For the consumer market: After Coze exits, low-barrier AI tools may be further integrated into office software and general-purpose models. Consumers will have more choices, but they should also watch package fees, call limits, and data-processing terms.