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2025.01.13
The Base of Future AI Agents: The Irreplaceability of Workflow
18
2025-03-18
At present, the heat of AI Agent (intelligent agent) continues to rise. From intelligent customer service to automated task scheduling, AI Agent has attracted the attention of countless enterprises and developers with its powerful autonomous decision-making ability and flexible task processing mode.
In contrast, the current Workflow seems to lack some "flexibility" and "intelligence". So, in the wave of future AI, will the value of Workflow be underestimated?
What are Workflow and Agent?
If AI is compared to a person,
Workflow is like the "skeleton" of our body.
It is a linear execution framework based on predefined rules and steps (such as ERP systems and CRM processes), emphasizing standardization, predictability, and strong compliance. It ensures the stability of high-frequency tasks with clear rules in our work and reduces human errors. For example, in the daily work of a bank, Workflow can automatically process credit inquiries in loan approval.
And Agent is like our "brain".
It can complete a whole set of task processes for us from perception-decision-delivery independently. Its value lies in that it can specifically solve some non-standard and customized tasks. For example, in bank work, Agent can be responsible for dynamically evaluating the risk level of customers.
Key differences
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Workflow
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Agent
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Task trigger: manual instruction
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Task trigger: environmental perception, autonomous start
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Decision mechanism: rule-driven
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Decision mechanism: goal-driven
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Interaction mode: single request-response
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Interaction mode: continuous dialogue and collaboration with long-term memory support
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Capability boundary: limited scenarios, closed system
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Capability boundary: cross-domain experience migration
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Application scenarios: standardized, predictable
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Application scenarios: complex, unstructured
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Relationship reconstruction: symbiosis and collaboration between Workflow and Agent
Workflow and Agent are not in a clear opposite relationship, but more like a symbiotic and collaborative relationship.
(1) Hierarchical collaboration: Workflow as the "skeleton", Agent as the "brain"
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Bottom layer: Workflow solidifies the core business process to ensure basic efficiency and compliance. (such as ERP order processing)
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Middle layer: Agent dynamically optimizes the process to solve non-standard problems. (such as adjusting supply chain parameters according to real-time inventory)
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Top layer: Agent independently decides the strategic goal, and Workflow executes the implementation details. (such as market expansion path planning)
(2) Dynamic switching: a rigid-flexible intelligent system
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Rule-first scenarios: Workflow takes the lead, and Agent only provides auxiliary suggestions. (such as financial audit)
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Innovation-first scenarios: Agent takes the lead, and Workflow assists in resource scheduling. (such as product design)
Application collaboration scenarios of Workflow and Agent
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Financial industry: the "double engine" of risk control and service
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Workflow: automatically processes standardized processes such as credit investigation and anti-money laundering detection;
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Agent: dynamically evaluates customer credit risks and generates personalized financial management plans.
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Medical industry: the balance between compliance and precision
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Workflow: strictly follows the diagnosis and treatment specifications;
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Agent: multi-modal analysis of patient data to assist in formulating personalized treatment plans.
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Manufacturing industry: the integration of efficiency and flexibility
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Workflow: solidifies the operation standards of the production line;
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Agent: predicts equipment failures and dynamically adjusts maintenance plans.
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Customer service: the unity of standardization and humanization
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Workflow: processes common consultation work orders;
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Agent: emotion recognition + knowledge graph calling to solve complex complaints.
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There are many more application collaboration scenarios between Workflow and Agent, and more application scenarios will be gradually explored and popularized in the future.
The base of future AI Agent, Workflow is irreplaceable
From "solidified process" to "elastic framework": Workflow will embed the feedback mechanism of Agent to support dynamic adjustment of steps. For example, the manufacturing process can automatically skip the failure link according to the equipment failure prediction.
Low-code and AI-native: more efficient Workflow platforms (such as Dify, Azure AI Foundry) integrate AI interfaces, allowing enterprises to quickly generate and modify processes through natural language instructions, reducing the development threshold.
The "knowledge base" of AI Agent: the business rules and data accumulated by Workflow will be transformed into the training materials of Agent to accelerate its industry adaptability. For example, the financial risk control Agent can master the compliance logic faster by learning the historical approval Workflow.
Finally, let's imagine that with the continuous upgrading of AI technology and the continuous deepening of business integration, the organizational structure of enterprises will迎来 new changes in the future.
