Top 4 Agentic AI Design Patterns for Architecting AI Systems

Introduction

Learning is a continuous journey, whether you’re human or an AI model. However, one question that often comes up is, can these AI models learn themselves just like humans do? As per the recent developments – They can. To understand this in a better way, let’s go back to our college days when C++, Java, and Python were the primary languages we needed to master to excel in computer science. Learning these languages requires understanding syntax, semantics, practical application, and problem-solving. So, to get a strong hold on these languages, we practised continuously (or you can say get trained). Also, we learned a lot from our classmates and professors. Right? Similarly, just like humans can learn from their own thinking, expertise and other mediums, perhaps LLMs can, too.

However, gaining expertise or becoming a subject matter expert is quite a rigorous journey for both humans and LLMs. We know about the human learning process and reasoning capabilities for making decisions and completing tasks, but what does LLM training look like?

Can I say? 

1. Firstly, pre-training of LLM: In this step, you help the model learn patterns, such as grammar, sentence structure, and even relationships between words and concepts.
2. Instruction-tuning (or Fine-Tuning): To fine-tune the model, a curated dataset containing examples of instructions and desired responses is used.
3. Reinforcement Learning with Human Feedback (RLHF): Human evaluators rank model responses, which is used further to improve the model’s alignment with user expectations.

That makes sense, right? But what if we build an agentic workflow to make the model learn and give the output while doing all the checks independently? It would be like having your own assistant who can do all the work without any human intervention. Further, in this article we will talk about the 4 Agentic AI Design Patterns for Architecting AI Systems.

Overview

• The article discusses how AI models, particularly large language models (LLMs) like GPT, can learn autonomously by adopting agentic workflows, which mimic human-like iterative problem-solving.
• Agentic workflows enhance AI performance by refining tasks step-by-step, similar to how humans review and improve their work repeatedly for better results.
• Four key Agentic Design Patterns—Reflection, Tool Use, Planning, and Multi-Agent Collaboration—are introduced as strategies that make AI systems more autonomous and capable.

What is Agentic Design Patterns?

The agentic design pattern is introduced as a solution for making LLMs more autonomous. Instead of just giving the model one prompt and expecting a final answer (like writing an essay in one go), an agent-like approach involves prompting the LLM multiple times, step by step. Each step refines the task, with the model improving its output iteratively.

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

When we prompt an LLM in zero-shot mode, it’s like asking someone to write a story in one go without revising. LLMs do well at this, but they can do even better. By using an agent-like workflow, we can prompt the LLM multiple times in steps. Each step builds on the previous one, refining the response. Think of it like asking the LLM to go over the essay multiple times, improving it with each pass.

By each step, I meant: 

Let’s take the example of writing a code using Agentic workflow: 

1. Plan an outline for the code: Break down the task into smaller modules or functions.
2. Gather information and content: Research libraries, algorithms, or existing solutions. Do web searches or check the documentation if needed.
3. Write the first draft of the code: Implement the basic functionality, focusing on structure over perfection.
4. Review the code for inefficiencies or errors: Check for unnecessary code, bugs, or logic flaws.
5. Revise the code: Refactor, optimise, or add comments for clarity.

Rinse and repeat until the code is efficient and clean.

By allowing the model to work through these steps independently, the agentic design pattern enhances both human-like reasoning and efficiency. This is similar to how humans break down complex tasks, gather information, make improvements, and iterate until the final result is satisfactory. Now, let us understand the Agentic design pattern in detail.

Agentic Design Patterns: Evaluations

Andrew Ng’s analysis, shared in a letter on Deeplearning.ai, noted advancements in AI-driven code generation, particularly focusing on the performance of models like GPT-3.5 and GPT-4. The evaluation was centred on these models’ capabilities to perform on the widely recognized HumanEval coding benchmark, a common standard for assessing an algorithm’s proficiency in writing code.

The data presented shows the evolution in AI coding abilities using AI agents. GPT-3.5, when tested in a zero-shot setting (i.e., without any prior examples), achieved a correctness rate of 48.1%. GPT-4, also evaluated in a zero-shot manner, demonstrated a significant improvement, with a 67.0% success rate. However, what stood out in the analysis was how integrating these models into an iterative agent workflow (Agentic workflow) drastically boosted their performance. When GPT-3.5 was wrapped in such an agent loop, its accuracy soared to an impressive 95.1%, far surpassing its baseline and even approaching human-level coding proficiency.

This finding underscores the transformative potential of iterative workflows (Agentic workflow) in enhancing AI model performance, suggesting that the future of AI-assisted coding may heavily rely on these more advanced, adaptive frameworks rather than on model size or architecture improvements alone. 

But what are Agentic design patterns that complete the delegation of autonomy to AI systems, enabling them to act more independently and effectively? These patterns structure AI agents to perform tasks, make decisions, and communicate with other systems in a more human-like and autonomous manner, ultimately creating both savvy and dependable applications.

4 Types Agentic Design Patterns that You Must Know

In Agentic AI and the key design patterns, it’s essential to understand how each pattern empowers large language models (LLMs) like GPT to behave more autonomously and effectively. These design patterns push the boundaries of what AI can do by encouraging self-evaluation, tool integration, strategic thinking, and collaboration. Let’s explore four vital agentic design patterns that shape how these models operate and perform complex tasks.

Here are the types of agentic design patterns:

1. Reflection Pattern

The Reflection Pattern focuses on improving AI’s ability to evaluate and refine its own outputs. Imagine an LLM reviewing its generated content or code as if it were a human reviewer, identifying errors, gaps, or areas that need improvement and then offering suggestions for how to improve.

This self-critique loop is not limited to a single iteration. The AI can repeat the reflection process as many times as necessary to achieve a refined, polished result. For example, if tasked with writing software, the LLM can generate an initial version, critique its own logic and structure, and revise the code. The iterative nature of reflection leads to stronger, more reliable outputs over time.

This pattern is particularly useful in tasks that require precision, such as content creation, problem-solving, or code generation. Employing this approach can enhance the model’s accuracy and reliability through self-guided corrections. 

One interesting example is Self-Reflective RAG. SELF-RAG is a framework designed to improve language models’ quality and factual accuracy by integrating retrieval and self-reflection into the text generation process. Traditional Retrieval-Augmented Generation (RAG) models enhance responses by incorporating relevant retrieved passages but often retrieve a fixed number of documents regardless of their relevance, which can introduce noise or irrelevant content. SELF-RAG addresses these limitations through an adaptive approach that retrieves information on demand and uses reflection tokens to assess the generation’s quality.

How SELF-RAG Uses Reflection?

SELF-RAG incorporates self-reflection mechanisms via “reflection tokens,” which serve to evaluate various aspects of the text generation, such as relevance, support, and overall utility. During the generation process, the model evaluates whether retrieval is necessary and assesses the quality of the generated content by critiquing itself at different stages.

Here’s the diagram for better understanding:

• Traditional RAG retrieves a fixed number of documents first, while Self-RAG performs retrieval dynamically based on the content being generated.
• Self-RAG evaluates multiple generated segments, critiques their quality, and selectively combines the most accurate information.
• Self-RAG’s iterative process enables refining the generation step by step, improving the accuracy and relevance of the output.

In a nutshell, Self-RAG adds an extra layer of self-reflection and refinement, leading to more reliable and precise answers.

2. Tool Use Pattern

The Tool Use Pattern significantly broadens an LLM’s capability by allowing it to interact with external tools and resources to enhance its problem-solving abilities. Instead of relying solely on internal computations or knowledge, an AI following this pattern can access databases, search the web, or even execute complex functions via programming languages like Python.

For instance, an LLM could be prompted to retrieve data from the web for a specific query, analyze it, and integrate it into its output. Alternatively, it might be tasked with calculating statistical results, generating images, or manipulating spreadsheets—actions that go beyond simple text generation. By incorporating the use of tools, LLMs evolve from static knowledge banks into dynamic agents capable of interacting with external systems to achieve goals.

This pattern is powerful because it allows AI systems to tackle more complex, multifaceted tasks where internal knowledge alone isn’t sufficient, expanding their utility into real-world applications.

3. Planning Pattern

The Planning Pattern enables an LLM to break down large, complicated tasks into smaller, more manageable components. Planning equips an agent with the ability to react to requests and strategically structure the steps needed to achieve a goal.

Instead of tackling a problem linearly, ad hocly, an LLM using the Planning Pattern will create a roadmap of subtasks, determining the most efficient path to completion. For example, when coding, the LLM would first outline the overall structure before implementing individual functions. This avoids confusion or meandering logic and keeps the AI focused on the main objective.

ReAct (Reasoning and Acting) and ReWOO (Reasoning With Open Ontology) further extend this approach by integrating decision-making and contextual reasoning into the planning process. ReAct enables the LLM to dynamically alternate between reasoning (thinking through the problem) and acting (performing specific tasks), allowing for more adaptive and flexible planning. By combining these two steps, the LLM can refine its approach iteratively, addressing unexpected challenges as they arise.

ReWOO, on the other hand, enhances the planning pattern by using an open-world ontology to guide reasoning. This means the LLM can incorporate broader contextual information and knowledge from various domains, leading to more informed decision-making. With ReWOO, the AI can adjust the plan in real-time based on newly acquired information or changing requirements, ensuring a more robust and comprehensive problem-solving approach.

Together, the Planning Pattern, ReAct, and ReWOO enable an LLM to handle complex tasks in a structured yet adaptive manner, resulting in efficient and goal-oriented execution.

Moreover, generating a structured plan (or a “user_request_summary”) ensures that the AI keeps track of all steps and doesn’t lose sight of the broader task. This method ensures higher quality and consistency in the results, especially in complex problem-solving or multi-phase projects.

4. Multi-Agent Pattern

The Multi-Agent Pattern builds upon the concept of delegation, akin to project management in human teams. This pattern involves assigning different agents (which are instances of an LLM with specific roles or functions) to handle various subtasks. These agents can work independently on their assignments while also communicating and collaborating to achieve a unified outcome.

There are several types of multi-agent patterns:

1. Collaborative Agents: Multiple agents work together on different parts of a task, sharing progress and building toward a unified result. Each agent may specialize in a different domain.
2. Supervised Agents: A central supervisor agent manages other agents, coordinating their activities and verifying results to ensure quality.
3. Hierarchical Teams: A structured system where higher-level agents oversee lower-level agents, with decision-making cascaded through levels to accomplish complex tasks.

For more details on this, explore: Multi-agent Collaboration.

For instance, in a scenario requiring both text analysis and numerical computation, two separate agents can handle each task, sharing their results to form a comprehensive solution. One agent might focus on understanding the context, while another processes data, and together they deliver a holistic response. This pattern is particularly powerful for tackling large-scale or complex problems that require diverse skill sets.

In short, the Multiagent Pattern mirrors how humans collaborate across specialities, ensuring that each agent focuses on its strengths while contributing to a greater, coordinated effort.

By mastering these four agentic design patterns, developers and users alike can unlock the full potential of AI systems. The Reflection Pattern improves accuracy and quality through self-evaluation, Tool Use enables dynamic, real-world interactions, Planning provides a roadmap for solving complex tasks, and Multiagent Collaboration ensures that multiple agents work together effectively. Together, these patterns create a foundation for building more intelligent, autonomous AI systems capable of addressing real-world challenges.

Conclusion

Agentic Design Patterns emphasize the transformative potential of agentic 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-shot tasks, their accuracy and effectiveness significantly improve when adopting an iterative, agentic workflow. This method allows the model to break down tasks, self-evaluate, leverage external tools, plan strategically, and collaborate with other agents, enhancing their problem-solving capabilities.

The article introduces four key design patterns—Reflection, Tool Use, Planning, and Multiagent—that form the foundation of these agentic workflows. These patterns push the boundaries of what AI can do and enable AI systems to behave more independently and intelligently, much like humans handling complex tasks. This signals that future AI advancements will depend on increasing model size and developing more adaptive and strategic workflows.

In this series on Agentic Design Patterns, we will further explore each design pattern in detail: Reflection, Tool Use, Planning, and Multiagent, uncovering how they empower AI systems to become even more autonomous and capable.

Stay tuned!!!!

Also, to understand the Agent AI better, explore: The Agentic AI Pioneer Program

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