Evolution of Agentic AI Design Patterns in LLM-Based Applications

Introduction

“AI agents will become the primary way we interact with computers in the future. They will be able to understand our needs and preferences, and proactively help us with tasks and decision-making”

Satya Nadella, CEO of Microsoft

This vision is rapidly becoming a reality, and the driving force behind it is the evolution of AI agents into more intelligent, flexible, and proactive systems. But what enables these agents, specifically Large Language Models (LLMs), to seamlessly adapt and respond to our needs? The answer lies in agentic design patterns—the underlying frameworks that allow LLMs to autonomously determine the best sequence of actions to accomplish a task. In this article, we’ll explore how these patterns are transforming LLMs into powerful, autonomous agents capable of driving the future of human-computer interaction. Let’s dive in and see how it all works.

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Overview

• Agentic AI design patterns enable autonomous decision-making in AI agents, improving LLMs’ flexibility and task execution.
• GenAI agents use machine learning to interpret data and perform tasks like customer service, coding, and content creation.
• Tool use allows LLMs to interact with external resources, enhancing their functionality and enabling more complex problem-solving.
• Multi-agent collaboration splits tasks between agents, optimizing complex workflows like supply chain management and autonomous systems.
• Autonomous coding agents streamline software development by generating and refining code, crucial in industries like fintech and autonomous vehicles.

Agentic AI Design Patterns 

Agentic AI Design Patterns are a set of structured approaches that guide the development and execution of autonomous AI agents. 

These patterns ensure robust, scalable, and efficient AI systems. Thus an AI developer finds them as a guide to improve the abilities of language models by impacting resolute aspirational behaviors. 

Ultimately, this boils down to the role of AI agents and how these agents will help developers create not only savvy but also dependable and user-friendly applications.

Let’s have a look at some of the prominent roles of AI agents.

Role of GenAI Agents

GenAI agents utilize advanced algorithms and machine learning models to interpret data and execute tasks. They are employed in various real-life applications, such as customer services, chatbots, automated coding systems, and content creation tools.  

To learn more about AI agents and their roles, read a detailed article on AI Agents.

Evolving Role of LLMs in App Development

LLMs have come a long way since its inception. Their capabilities have opened new horizons, from customer service bots to intricate data analysis tools. Incorporating agentic design patterns into LLMs is a new milestone in this development. 

The future of agentic framework in integration with LLMs is promising, with the evolution of more.

• Advanced reflective agents
• Enhanced multi-agents collaboration
• Planning
• And improved tool use

These expected advancements in agentic workflow will strengthen the abilities of LLMs that make these workflows possible right now.

But before we look too far into the future, let’s examine the design patterns that make these workflows possible today.

Tool Use: Empowering LLMs with Functions 

Tool use allows LLMs to interact with external tools during conversation generation. This pattern is useful as it allows agents to extend their functionalities. Tools can be functions written by developers, APIs for external services, or any resources LLM can interact with.

Let’s understand the pattern of tool use with the help of the diagram below.

• Task Defining: In the first step, the agent defines a task that it needs to solve. It can be a query, or any work related problem posed by a user.
• LLM Processing: The LLM evaluates the query and decides if it requires an external tool to solve the problem. It decides which tool to call based on its reasoning qualities.
• Interaction between tool and environment: Once the tool is finalized, it interacts with the environment. These actions generate feedback that is fed to the system.
• Feedback to the LLM: The feedback received from the interaction between the tool and environment is sent to the LLM. The LLM uses this feedback to refine its understanding of the task further.
• Task resolution: The LLM incorporates feedback and continues reasoning, possibly connecting more tools until the task is done.

Practical Applications

In real-world scenarios, application of implementing tool use patterns can be seen in automated data analysis, leveraging statistical tools to derive insights, or in customer support where they access the database to provide information swiftly.

Multi-Agent Collaboration

The Multi-Agent Collaboration pattern is a combination of multiple autonomous agents working together to achieve a common goal. The main objective of their collaboration is to break down complex tasks into subtasks executed by different agents. 

This pattern is significant because it enables division of labor enhancing problem solving capabilities. 

Practical Applications

Multi-agent collaboration includes multiple automated robots in manufacturing industries, where different agents various aspects of the production process, optimizing supply chain operations, or in supply chain management to coordinate robots in warehouses to manage stock, pick and pack items.

Autonomous Coding Agent

Autonomous coding agents, sometimes referred as AI coding assistants are Gen AI agents designed to optimize code autonomously. In this process agents are designed to generate, modify, or improve code based on the given tasks. 

These agents follow different patterns to optimize their effectiveness. Let’s have a look and understand one of them.

• User Input: In the first step, users put a query or task through API or user interface, which is then refined and interpreted by the agents.
• Agent Activation: The central agent initiates the process and starts working on it. It accesses historical data, vector databases to tailor code generation followed by breaking the tasks into sub-task. 
• Code Generation: Once done, agents generate and test code simultaneously based on the feedback.

Practical Applications

In the present day, the main applications of autonomous code generation include their use in developing software for autonomous vehicles, where they generate codes for decision making algorithms. Nowadays, the Fin-tech sector has also started automating their system to ensure secure transactions. 

Reflection: Self-Criticism

Reflection is one of the most promising agentic design patterns as it allows one to critique its own output and improve it iteratively. By prompting an agent to reflect on its own output, we can harness its ability to suggest improvements. This design pattern can be extensively used in various interactive processes such as code generation, text writing and question answering.

Here’s how it typically works:

• Self-awareness: To answer a query, an agent first monitors its own internal state, including knowledge base, goals, plan and actions.
• Reasoning: Once it evaluates whether its current behavior is aligned with goals, it then starts reasoning to find if it should continue with its current strategy.
• Adaption: In this design pattern, agents have the capabilities to modify its behavior, meaning change in decision-making, updating its knowledge base, or altering the way it interacts with the environment.

Planning: Autonomous Decision-making

Planning is the most important design pattern on which agents rely since it allows LLM to autonomously decide on the steps which it needs to execute to accomplish a bigger task. This design pattern allows agents to break down complex problems into smaller subtasks. 

When LLMs are asked to produce a result based on React prompting, without structured planning, the result can be lower quality. Generating a user query request along with the reasoning step can help LLMs plan their output.

Additional Design Patterns

Apart from the above-mentioned design patterns, there are three additional notable AI design patterns for autonomous agents, which includes the following:

• Reactive Agents: These agents respond in real time, making them appropriate for dynamic conditions.
• Proactive Agents: Unlike the first agent, these agents take initiative based on predefined goals, making them ideal for long-term projects.
• Hybrid Agents: These agents have the characteristics of both reactive and proactive, making it hybrid by adapting to the context.

Conclusion

In conclusion, agentic design patterns lay the foundation for the evolution of LLM based applications. From tool usage to multi-agent collaboration, these patterns offer scalable solutions to various industries applications. As we look ahead, integration of agentic design patterns within LLM based applications holds immense potential for robust AI systems.

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