Deep Understanding of Agent Workflow Core: Fundamental Differences Between Agent vs Traditional Programming vs Workflow

About This Series

As the author of Lynxe (formerly JManus), I've spent a lot of my spare time perfecting this Func-Agent framework, which has given me a deeper understanding of what a ReAct-based Agent is.

I want to summarize this content because the core purpose of this project is to explore cutting-edge best practices for Agents. We've made some progress—Lynxe can solve over 80% of the problems I face—so I think it's worth documenting the effective practices I've discovered to help others get started quickly.

You can visit Lynxe (/lɪŋks/) to read the detailed source code and learn about some agent best practices. This is a very complete, production-grade Func-Agent framework.

Series Plan

• What is a ReAct Agent?
• Deep Understanding of Agent Workflow Core: Fundamental Differences Between Agent vs Traditional Programming vs Workflow
• Deep Analysis of Function Calling, MCP, and Skills: Essential Differences and Best Practices
• Some practices for context management
• Best practices for parallel execution and the pitfalls I've encountered
• Others (to be written as ideas come up or feedback is received)

Main Content

In the previous article, we introduced what a ReAct Agent is. Now let's discuss a more practical question: What are the fundamental differences between Agents and traditional programming/workflow approaches? Why do we need Agents?

One-sentence summary: Traditional programming and Workflow both involve humans making decisions and designing all logic in advance, while Agents have AI making decisions and can solve problems that traditional programming cannot, making it easier to create differentiated experiences. Therefore, Agents are more suitable as a new paradigm for next-generation user interaction. Just like the coding agents everyone is using now, there will be more agents emerging in the future targeting different domains.

Comparison of Three Approaches

Let's start with an intuitive comparison table:

Dimension	Traditional Programming	Workflow	Agent
Required Skills	Need to master programming languages, algorithms, system design, and other professional knowledge	Understand programming principles, ability to use graphical drag-and-drop products, and write extension functions	Natural language is sufficient to complete all business logic
Task Completion Method	Completely relies on hardcoded rules, difficult to handle uncertain or complex scenarios	Fixed path flow, limited conditional judgment, unable to dynamically adjust strategies	Dynamically adjusts strategies to complete tasks under the guidance of natural language
Modification and Maintenance Costs	Multi-role waterfall collaboration: Operations discovers problem -> Product breaks down and schedules -> Development -> Deployment -> Testing -> Launch	Basically only saves the deployment step: Operations discovers problem -> Product breaks down and schedules -> Development -> Testing -> Launch	Business self-closed loop: (Discover -> Test -> Solve)

This table might seem a bit abstract. Let me explain the fundamental differences between these three approaches in a more concrete way.

Traditional Programming: Everything Must Be Planned in Advance

Traditional programming is like building a house—you need to draw all the blueprints first, prepare all the materials, and then strictly follow the blueprints during construction. Once you encounter a situation not covered in the blueprints, you have to redesign.

Practical Example

Suppose you want to create a "weather-based clothing recommendation" feature:

Traditional Programming Approach:

def get_weather_recommendation(city):
    # 1. Query weather
    weather = query_weather_api(city)
    temperature = weather['temperature']
    condition = weather['condition']
    
    # 2. Judge based on temperature
    if temperature < 10:
        return "Recommend wearing a thick coat"
    elif temperature < 20:
        return "Recommend wearing a thin coat"
    elif temperature < 25:
        return "Recommend wearing long sleeves"
    else:
        return "Recommend wearing short sleeves"
    
    # 3. What if the API returns an error? Need to write exception handling
    # 4. What if the data format is wrong? Need to write data validation
    # 5. What if the user wants more detailed advice? Need to modify the code

The problems with this approach are obvious:

• Hardcoded rules: All logic is predetermined, and you need to modify code when encountering new situations
• Complex exception handling: All edge cases must be considered in advance, making the code very complex
• High modification costs: Changing a small piece of logic requires development, testing, and deployment—going through the entire process

Workflow: Fixed Process, but More Flexible

Workflow is like building with blocks—you can connect different "blocks" (nodes) graphically to form a fixed process. It's more flexible than traditional programming, but essentially still a fixed path.

Practical Example

The same "weather-based clothing recommendation" feature using Workflow:

[Start] -> [Query Weather API] -> [Judge Temperature] -> [Return Recommendation] -> [End]
         ↓ (if fails)
      [Error Handling Node]

This approach is better than traditional programming:

• Visual: No need to write code, just drag and drop
• Modular: Each node is independent and reusable
• But problems still exist:
  • The process is fixed. If the user wants "first check weather, then check clothing advice, finally save to file," you need to redesign the entire process
  • Limited conditional judgment; complex logic still requires writing code
  • Complex processes become harder to maintain
  • Modifying the process still requires developer participation

Agent: Walk and Observe, Dynamic Adjustment

An Agent is like an experienced guide—you tell it the goal, and it dynamically adjusts the route based on actual conditions. You don't need to plan everything in advance; solve problems as they arise, and change paths when one doesn't work.

Practical Example

The same "weather-based clothing recommendation" feature using Agent:

You only need to tell the Agent: "Help me check what the weather is like in Beijing today, what I should wear, and then save it to a file."

The Agent will decide on its own:

1. First call the weather query tool
2. Based on the weather results, decide to call the clothing advice tool
3. After getting the advice, decide to call the file writing tool
4. If a tool fails, it will automatically try other methods

The entire process is dynamic and doesn't require designing all steps in advance.

Fundamental Difference: Who Makes the Decisions?

The most fundamental difference between these three approaches is: Who makes the decisions?

• Traditional Programming: Programmers make decisions, thinking through all possible situations in advance and writing them into code
• Workflow: Product/Development makes decisions, designing fixed process paths
• Agent: AI makes decisions, dynamically adjusting strategies based on actual conditions

Because the decision-makers are different, the skill requirements are also different: Traditional programming requires mastering programming languages, algorithms, system design, and other professional knowledge with a high threshold; Workflow requires understanding programming principles and graphical tools with a medium threshold; while Agents only need to be able to describe requirements in natural language—obviously, Agents greatly lower the threshold. At the same time, this also brings huge differences in modification and maintenance costs: Traditional programming requires multi-role waterfall collaboration (days to weeks), Workflow can only save the deployment step, while Agents can achieve business self-closed loops, taking only minutes from discovering a problem to solving it.

Real-World Scenario Comparison

Let's use a more complex scenario for comparison: "Help me analyze the sales data from the past week, find abnormal orders, generate a report and send it to the team"

Traditional Programming Approach

Requires:

1. Write code to connect to database
2. Write SQL to query sales data
3. Write algorithm to analyze abnormalities (define what is abnormal)
4. Write code to generate report
5. Write code to send email
6. Handle various exception cases (database connection failure, email sending failure, etc.)
7. Testing and deployment

May require: 1-2 weeks of development time, involving backend development, data analysis, operations, and other roles.

Workflow Approach

Requires:

1. Design process: Query data -> Analyze data -> Generate report -> Send email
2. Configure each node
3. Write some extension functions to handle complex logic
4. Testing and launch

May require: 1~3 days, needs technical personnel participation.

Agent Approach

Only requires:

1. Tell the Agent: "Help me analyze the sales data from the past week, find abnormal orders, generate a report and send it to the team"
2. Agent automatically completes all steps
3. If the result is incorrect, continue adjusting with natural language: "Change the definition of abnormal orders—only orders with amounts exceeding 3 times the average should be considered abnormal"

May require: A few minutes to a few hours, operations personnel can complete it themselves.

When to Use Which Approach?

Although Agents seem very powerful, not all scenarios are suitable for Agents. Each of the three approaches has its applicable scenarios:

Traditional Programming is Suitable For:

• Scenarios with extremely high performance requirements (high-frequency trading, real-time games, etc.)
• Scenarios requiring precise control (security systems, financial systems, etc.)
• Scenarios with very fixed logic that won't change

Workflow is Suitable For:

• Scenarios with relatively fixed business processes but requiring visual management
• Scenarios requiring non-technical personnel to participate in process design
• Scenarios requiring process auditing and version management

Agent is Suitable For:

• Scenarios with frequently changing requirements
• Scenarios requiring handling of uncertainty
• Scenarios requiring rapid iteration and trial-and-error
• Scenarios where non-technical personnel need to complete tasks independently

Summary

Overall, we believe that Agents are a new application paradigm that is more future-oriented and worth exploring and trying.

The main reason is very simple: Agents can deliver a more obvious "new experience," making them easier for end users to perceive.

Workflow and traditional programming models—the core change in both is merely adding AI capabilities appropriately within fixed processes. Beyond this difference, other aspects are similar. Therefore, both are essentially program-controlled process flows, and they are actually mutually substitutable relationships. This competitive substitution relationship has already been fully competed in the era without AI. The result is that the code-writing approach became the mainstream choice because of its excellent reusability and extensibility.

Agent's approach is completely different. Its decision-making power is completely delegated to the Agent and Prompt, enabling it to solve problems that traditional programming cannot—such as handling uncertainty, dynamically adjusting strategies, understanding natural language intentions, etc. Therefore, Agents are not a simple replacement for traditional programming, but a new paradigm with more opportunities.

From an application scenario perspective:

• If you want to enhance AI capabilities in existing systems, you can use code + toolcall, which provides the best results and guaranteed accuracy. This approach is suitable for scenarios requiring precise control and high performance.

• If you want users to clearly feel this is an AI-driven innovative product, then using AI Agents is a better choice. This approach is suitable for scenarios requiring handling uncertainty, rapid iteration, and enabling non-technical personnel to complete complex tasks.

The key is to understand the essence of each approach and choose the most appropriate one based on actual scenarios. The core value of Agents lies in opening up new possibilities, making AI a true decision-maker, not just an executor.