# AI Agents workflow

AI Agents workflow:

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🧠 The Neural Core

Perception Engine 🔄: Converts raw inputs into formatted data and embeddings (📊 75% faster decision-making through vectorization).

LLM Orchestration 🤖: Four dynamic subtasks plus a reasoning layer (⚡ 1.5x performance boost with parallel processing).

The “Brain” 🧩: Integrates subtask 3/4 with strategic planning (🔗 90% accuracy in complex scenario modeling).

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<figure><img src="https://media.licdn.com/dms/image/v2/D5622AQGhgAr8-bkLQw/feedshare-shrink_800/B56ZW0gOwNGQBw-/0/1742490103195?e=1745452800&#x26;v=beta&#x26;t=NJm-qRbcUo6JfDT7kUEs81ST3X6NTYT9oO2IHUG9cuY" alt=""><figcaption></figcaption></figure>

⚡ Action & Interaction

LLM-Driven Execution 🎯: Combines subtask 2 with adaptive planning (📈 60% fewer errors compared to traditional models).

Memory-Environment Loop 🌐: Real-time feedback storage (💾 10TB+/sec data handling) fuels iterative learning.

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🔑 Key Insights

Symbols Matter: Input embeddings (🔢) connect perception and reasoning.

Data Flow: Environment interactions enable 360° adaptability (🌍 40% faster responses in dynamic settings).

📌 Why It Matters: This framework is reshaping autonomy—powering self-optimizing systems across healthcare, finance, and robotics!


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://phantanloc.gitbook.io/locpt_wiki/homepage/2.it-cntt/ai-tri-tue-nhan-tao/ai-agents-workflow.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.