Top 4 Agentic AI Design Patterns

The autonomous learning ability of AI models: learning methods beyond programming languages

Learning is a continuous process, both for humans and AI models. However, a common question is whether these AI models can learn independently like humans? According to the latest developments – they can . To better understand this, let's go back to college, when C, Java, and Python were the main programming languages ​​we needed to master in computer science. Learning these languages ​​requires understanding of grammar, semantics, practical applications, and problem solving. Therefore, in order to master these languages, we conducted continuous practice (or could be said to have trained). In addition, we have learned a lot from classmates and professors, right? Likewise, just as humans can learn from their own thinking, expertise, and other media, large language models (LLMs) may do so.

However, it is a difficult journey for both humans and LLM to acquire expertise or become an expert in a certain field. We understand human learning and reasoning abilities, as well as their abilities in decision-making and completing tasks, but what is the training process of LLM like?

I can say this:

1. First is pre-training for LLM: In this step, you help the model learn patterns such as grammar, sentence structure, and even the relationship between words and concepts.
2. Instruction fine-tuning (or fine-tuning): To fine-tune the model, use a selected dataset containing the instruction examples and the desired response.
3. Reinforcement learning based on human feedback (RLHF): Human evaluators rank the responses of models, which are further used to improve the consistency of models with user expectations.

This makes sense, right? But what if we build an autonomous workflow to let the model learn and give the output while doing all the checks independently? It's like having a personal assistant who can do all the work without any manual intervention. In addition, in this article, we will discuss four autonomous AI design patterns used to build AI systems.

• What is the independent AI reflection model?
• What is the usage mode of autonomous AI tool?
• What is the autonomous AI planning model?
• What is the autonomous AI multi-agent model?

Overview

• This article discusses how AI models, especially large language models (LLMs) like GPT , can come from main learning by adopting autonomous workflows that simulate human iterative problems.
• Autonomous workflows enhance AI performance by gradually refining tasks, similar to how humans repeatedly examine and improve their work for better results.
• Four key autonomous design patterns—reflection, tool use, planning, and multi-agent collaboration—are introduced as strategies to make AI systems more autonomous and competent.

Table of contents

• Overview
• What is an independent design model?
• Independent design mode: evaluation
• 4 independent design modes you must know
  • Reflective mode
  • Tool usage mode
  • Planning mode
  • Multi-Agile Mode
• in conclusion
• Frequently Asked Questions

What is an independent design model?

The autonomous design model is introduced as a solution to make LLM more autonomous. Rather than just providing a hint to the model and expecting the final answer (such as writing a paper at once), it is better to use a proxy-like approach, which is to prompt LLM multiple times step by step. Each step improves the task and the model iteratively improves its output.

To better understand this, let's look at it like this:

When we prompt LLM in zero sample mode, it is like asking someone to write a story in one go without making modifications. LLMs do a great job in this, but they can do better. By using a proxy-like workflow, we can prompt LLM step by step multiple times. Each step is built on previous steps, thereby improving response. Think of it as requiring LLM to check the article multiple times and improve it in each check.

Each step means:

Let's take the example of writing code using an autonomous workflow:

1. Outline of planning code: break down tasks into smaller modules or functions.
2. Collect information and content: Research libraries, algorithms, or existing solutions. If necessary, please search the Internet or view the document.
3. Write a first draft of code: implement basic functions, focusing on structure rather than perfection.
4. Check for inefficiency or errors in the code: Check for unnecessary code, errors, or logical flaws.
5. Modify the code: Refactor, optimize, or add comments to improve clarity.

Repeat this process until the code is efficient and concise.

By allowing the model to complete these steps independently, autonomous design patterns enhance human-like reasoning and efficiency. This is similar to how humans break down complex tasks, collect information, make improvements, and iterate until the final result is satisfactory. Now, let's learn more about the independent design model.

Independent design mode: evaluation

In a letter shared by Andrew Ng, an analysis pointed out that AI-driven code generation has made progress, with particular attention to the performance of models such as GPT-3.5 and GPT-4. The focus of the evaluation is on the capabilities of these models in the well-known HumanEval coding benchmark, a common standard for evaluating the ability of algorithms to write code.

The data provided show the evolution of AI encoding capabilities using AI agents. GPT-3.5 was tested in a zero-sample setting (i.e. without any prior examples), with a 48.1% accuracy. GPT-4, which was also evaluated in the zero-sample mode, showed significant improvements with a success rate of 67.0%. However, what is highlighted in the analysis is how these models are integrated into iterative agent workflows (autonomous workflows) dramatically improve their performance. When GPT-3.5 is included in such a proxy loop, its accuracy soars to an impressive 95.1%, far exceeding its baseline and even approaching human-level coding capabilities.

This finding highlights the transformative potential of iterative workflows (autonomous workflows) in enhancing the performance of AI models , suggesting that the future of AI-assisted coding may rely more on these more advanced and adaptable frameworks than on improvements in model size or architecture.

But which autonomous design models have completed the delegation of autonomy over AI systems, allowing them to act more independently and effectively? These patterns build AI agents to perform tasks, make decisions, and communicate with other systems in a more human-like and autonomous way, ultimately creating applications that are both savvy and reliable.

4 independent design modes you must know

In autonomous AI and key design patterns, it is crucial to understand how each pattern enables large language models (LLMs) such as GPT to operate more autonomously and efficiently. These design patterns break through the limitations of AI by encouraging self-evaluation, tool integration, strategic thinking and collaboration. Let us explore four important autonomous design patterns that shape how these models run and perform complex tasks.

The following are the types of independent design patterns:

1. Reflective mode

The reflection model focuses on improving the ability of AI to evaluate and improve its own output. Imagine an LLM reviews the content or code it generates like a human reviewer, identifying errors, gaps, or areas that need improvement, and then making suggestions for improvement.

This cycle of self-criticism is not limited to a single iteration. AI can repeat the process multiple times as needed to obtain improved and perfect results. For example, if the task is to write software, LLM can generate initial versions, criticize its own logic and structure, and modify the code. The iterative nature of reflection produces stronger and more reliable output over time.

This pattern is especially useful in tasks that require precision, such as content creation, problem solving, or code generation. Using this approach can improve the accuracy and reliability of the model through self-guided corrections.

An interesting example is self-reflective RAG. SELF-RAG is a framework designed to improve the quality and factual accuracy of language models by integrating retrieval and self-reflection into the text generation process. Traditional search-enhanced generation (RAG) models enhance responses by combining relevant retrieved paragraphs, but usually search a fixed number of documents regardless of their relevance, which may introduce noise or irrelevant content. SELF-RAG addresses these limitations through an adaptive approach that dynamically retrieves information based on the generated content and uses reflection markers to evaluate the quality of the generation.

How to use SELF-RAG reflection?

SELF-RAG combines a self-reflective mechanism through “reflective markers” that are used to evaluate various aspects of text generation, such as relevance, support, and overall utility. During the generation process, the model evaluates whether the search is required and evaluates the quality of the generated content by criticizing itself at different stages.

Here are easy-to-understand charts:

• Traditional RAG first retrieves a fixed number of documents, while Self-RAG performs a dynamic search based on the content being generated.
• Self-RAG evaluates multiple generated fragments, criticizes their quality, and selectively combines the most accurate information.
• The iterative process of Self-RAG can gradually improve generation and improve the accuracy and correlation of output.

In short, Self-RAG adds an additional layer of self-reflection and improvement, resulting in more reliable and precise answers.

2. Tool usage mode

Tool usage patterns significantly expand their capabilities by allowing LLM to interact with external tools and resources, thereby enhancing their problem-solving capabilities. AI that follows this pattern does not rely solely on internal computing or knowledge, it can access databases, search the network, and even execute complex functions through programming languages ​​such as Python.

For example, the LLM may be prompted to retrieve data from the network, analyze data, and integrate it into its output for a specific query. Alternatively, it may be assigned tasks to calculate statistics, generate images, or operate spreadsheets—these operations beyond simple text generation. By combining the use of tools, LLM has evolved from a static knowledge base to a dynamic proxy that can interact with external systems to achieve its goals.

This model is powerful because it allows AI systems to handle more complex and multifaceted tasks, and internal knowledge alone is not enough to extend its utility to real-world applications.

3. Planning mode

The planning model enables LLM to break down large and complex tasks into smaller, more manageable components. Planning enables agents to respond to requests and strategically build the steps required to achieve their goals.

LLM uses planning mode to deal with problems linearly and temporarily, but instead creates a subtask roadmap to determine the most efficient path to complete the task. For example, when encoding, LLM will first outline the overall structure and then implement the various functions. This avoids confusing or twists and turns logic and makes AI focus on its main goals.

ReAct (Inference and Action) and ReWOO (Inference with Open Ontology) further extend this approach by integrating decision-making and contextual reasoning into the planning process. ReAct enables LLM to dynamically switch between reasoning (thinking problems) and action (executing specific tasks), enabling more adaptable and flexible planning. By combining these two steps, LLM can iteratively improve its approach and solve unexpected challenges that arise.

ReWOO, on the other hand, enhances planning patterns by using open-world ontology to guide reasoning. This means that LLM can combine a wider range of situational information and knowledge from various fields to make smarter decisions. With ReWOO, AI can adjust plans in real time to meet newly acquired information or changing needs, ensuring a stronger and comprehensive problem-solving approach.

Overall, planning mode, ReAct, and ReWOO enable LLM to handle complex tasks in a structured but highly adaptable way, enabling efficient and goal-oriented execution.

Additionally, generating a structured plan (or "user request summary") ensures that the AI ​​tracks all steps and does not ignore a wider range of tasks. This approach ensures higher quality and consistency of results, especially in complex problem solving or multi-stage projects.

4. Multi-Agility Mode

The multi-agent model is built on the concept of delegation, similar to project management in human teams. This pattern involves assigning different agents (LLM instances with specific roles or functions) to different subtasks. These agents can independently handle their assigned tasks, while also communicating and collaborating to achieve unified results.

There are several types of multi-agent mode:

1. Collaborative Agent : Multiple agents work together to handle different parts of a task, share progress and work towards a unified result. Each agent may specialize in a different field.
2. Supervisory Agent : A central supervisory agent manages other agents, coordinates their activities and validates results to ensure quality.
3. Hierarchical team : A structured system in which high-level agents supervise low-level agents and decisions are issued at all levels to complete complex tasks.

For more details on this content, please visit: Multi-Agent Collaboration.

For example, in a scenario where text analysis and numerical calculations are required, two independent agents can handle each task, sharing its results to form a comprehensive solution. One agent may focus on understanding the context, while another handles the data, and together they provide a comprehensive response. This pattern is especially effective for dealing with large-scale or complex problems that require multiple skills.

In short, the multiagent model reflects how humans collaborate across various specialized areas, ensuring each agent focuses on its strengths while contributing to greater coordinated efforts.

By mastering these four independent design models, developers and users can unlock the full potential of AI systems. The reflection model improves accuracy and quality through self-evaluation, the tool uses the model to achieve dynamic real-world interaction, the planning model provides a roadmap for solving complex tasks, and multi-agent collaboration ensures that multiple agents work effectively and collaborate. Overall, these models lay the foundation for building smarter, more autonomous AI systems that can meet real-world challenges.

in conclusion

The autonomous design model emphasizes the transformational potential of autonomous workflows in making AI models (particularly large language models (LLMs) ) more autonomous and efficient. It explains that while models like GPT-3.5 and GPT-4 perform well in zero-sample tasks, their accuracy and effectiveness are significantly improved when it comes to an iterative autonomous workflow. This approach allows the model to decompose tasks, self-evaluate, leverage external tools, conduct strategic planning, and collaborate with other agents to enhance its problem-solving capabilities.

This article introduces four key design patterns—reflection, tool use, planning, and multiagent—which form the basis of these autonomous workflows. These patterns break through the limitations of AI and enable AI systems to run more independently and intelligently, just like humans deal with complex tasks. This suggests that future AI advances will depend on increasing the size of the model and developing more adaptable and strategic workflows.

In this series of articles on autonomous design patterns, we will further explore each design pattern in detail: reflection, tool use, planning, and multiagents, revealing how they make AI systems more autonomous and competent.

Stay tuned!!!

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Frequently Asked Questions

Q1. What is the independent design model in AI? ****A: The autonomous design model is a strategy used to make AI systems (especially large language models (LLMs)) more autonomous and effective. These patterns allow AI to perform tasks, make decisions, and interact with other systems more independently by simulating human-like problem-solving and reasoning processes. Key models include reflection, tool use, planning, and multi-agent collaboration.

Q2. How does reflection mode improve AI performance? ****A: The reflection model enhances the ability of AI to self-evaluate and improve its output. By repeatedly reviewing your own work, AI will identify errors, gaps, or areas that need improvement and correct them in an iterative loop. This pattern has proven particularly useful in tasks that require precision, such as code generation or content creation, as it helps to produce more accurate and reliable results.

Q3. What are the benefits of using tool usage patterns in AI workflows? ****A: Tool usage patterns extend AI's capabilities by allowing AI to interact with external tools and resources. AI does not rely solely on internal knowledge, it can access databases, perform web searches, or execute functions using programming languages ​​such as Python. This makes AI more versatile and able to handle complex tasks that require information or calculations beyond its existing data.

Q4. How does planning mode help LLM handle complex tasks? ****A: Planning mode enables AI models to break down complex tasks into smaller, more manageable steps, creating a roadmap for problem solving. This approach helps maintain focus on the main goals and ensures efficient execution of tasks. Variants like ReAct (Inference and Action) and ReWOO (Inference with Open Ontology) combine decision-making and adaptive strategies, allowing AI to dynamically improve its approach based on the emergence of new information.

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