Breaking Down the AI Agent Execution Flow: How Tools and Skills Work Together

When we give an AI Agent a simple instruction—say, "help me collect recent industry hot topics and organize them into a report"—what kind of thinking and execution process does it actually go through behind the scenes? Many people still perceive Agents as merely "smarter chatbots," but to truly understand their value, we need to break down the complete chain from receiving a task to delivering results.

Based on a Bilibili creator's demonstration and explanation of Agent operations in practice, this article systematically reviews the two core concepts of Agents—Tools and Skills—and reconstructs the eight-step execution flow of a task from assignment to completion.

Tool and Skill: The Two Foundational Concepts of Agent Operation

To understand how an Agent "gets work done," we first need to distinguish between two easily confused concepts: Tool and Skill.

A Tool represents a specific action—it's the "hands and feet" through which the Agent interacts with the outside world. Just as tightening a screw requires a wrench, when an Agent performs an online search, it calls the WebSearch tool. The data returned by a search tool might be HTML web pages, PDF documents, or other file types, and the Agent needs to read from and write to the file system, then understand and process it.

A Skill, on the other hand, is a set of working methods—it defines how complex tasks should be broken down, how tools should be called, and what conditions and standards the results need to meet. The decisions a Skill makes include: which tool to use and when, what steps the process should follow, how to retry on failure, and what output standards must be met before it stops running. In short, a Skill is the solidified accumulation of experience and methodology.

Understanding the Tool-Skill Relationship Through a Kitchen Analogy

If we compare an Agent to a chef, this relationship becomes very intuitive:

• Tool = knives and pots: the implements that handle the actual operations in the kitchen
• Skill = the complete recipe and cooking method: coordinating the entire process of a dish from prepping ingredients to serving

Tools handle the concrete actions of "cutting" and "stir-frying," while the Skill governs "how this dish should be cooked overall."

In the demonstration, the creator installed an "online search skill" for the Agent. Using the kitchen metaphor, this is equivalent to adding a matching set of knives and pots to the kitchen—in essence, it's about giving the Agent a reusable search methodology. This is key: the value of a Skill lies not in one-time use, but in the fact that it solidifies the experience of handling a category of tasks, allowing the Agent to automatically invoke it when facing similar needs, without having to "figure it out" all over again.

The Complete Eight-Step Flow of Agent Task Execution

With the concepts clarified, let's look at how things actually run under the hood after an Agent receives a task. Taking the instruction "collect recent hot topics and organize a report" as an example, the Agent first needs to clarify the scope of "recent hot topics," who the target audience is, and how many are needed, then formulate a plan, arrange searches, and perform filtering and analysis.

The entire execution process can be broken down into the following eight steps:

1. Understand the goal: parse the true intent of the user's instruction
2. Formulate a plan: break down the complex task into executable sub-steps
3. Call tools: select and execute the appropriate tool (e.g., WebSearch)
4. Evaluate execution: assess whether the current results meet the requirements
5. Organize results: structure the raw data into organized content
6. Verify sources: validate the reliability of the information
7. Continue deepening: if information is insufficient, repeat the search to gather more data
8. Deliver the task: output the final results to the user

Reconstructing the Agent's Thought Process From Actual Execution Logs

This flow isn't just theoretical. By examining the Agent's execution history, we can clearly see its actual operating trajectory.

During the thinking phase, the Agent first analyzes: "The user is asking me to collect certain content. This is a task that requires internet access. Which skill should I call?" At this point, the just-installed online search skill comes into play. The Agent will clarify "let me first search these two hot topics" and provide the user with an analytical explanation—this is the outward manifestation of a complete chain of thought.

Upon entering the execution phase, the first step is calling the tool. Once the plan is formulated, the Agent issues a search command and obtains the first batch of results. But it won't stop there—if it judges the amount of information to be insufficient, it will continue calling tools for deeper searches, even iterating over multiple rounds, until enough content has been accumulated. This is exactly what the "evaluate execution" and "continue deepening" steps look like in practice.

Once the Agent confirms that "enough information has been collected," it begins organizing a complete analysis report and finally delivers the results to the user. The entire chain—from understanding the goal, formulating a plan, calling tools, evaluating execution, organizing results, verifying sources, continuing to deepen, to final delivery—is logically complete and forms a closed loop.

The Practical Significance of Understanding the Agent Execution Flow

A complete Agent task must include at least the core steps described above. This flow explains why an Agent can handle open-ended, multi-step complex tasks rather than giving just a one-time answer.

For those who want to truly make good use of Agents, understanding this mechanism has two practical benefits:

First, Skills are reusable capability units. Configuring the right skills for an Agent is equivalent to accumulating standard methods for handling a category of tasks. The more reasonably skills are configured, the stronger and more stable the Agent's ability to complete tasks autonomously.

Second, Agents possess self-judgment and iteration capabilities. An Agent doesn't just mechanically run a single search and call it done—it proactively evaluates whether the results are sufficient and decides whether to deepen further. This "execute—evaluate—execute again" loop is precisely what distinguishes an Agent from traditional script-based automation.

You may not have noticed, but during the actual execution in the demonstration, the "verify sources" step was handled by the Agent on its own and wasn't clearly displayed in the interface. This also reminds us that different Agent implementations vary in the transparency and rigor with which they execute each step of the flow. When relying on their output, users should still maintain the necessary awareness of manual verification.