How to Design a Modern AI Agent-Based Architecture with Object-Oriented Software Patterns

For the past weeks I explained the core patterns when designing a GenAI product.

I changed the topic for today's article and switched to AI-Agents before explaining RAG in my next article.

We had a lot of rain in Germany over the past few months, I don't know who noticed, thank God it's over..., but with a lot of rain, comes often times a lot of time indoors. So, watching my apple trees struggle, my kids bringing in puddles of mud, while our cat slept through the whole ordeal, I took on a small personal project that I think is perfect explaining and bridging 2 essential topics. AI-Agents and Software-Design.

Unlike many who believe they can land a plane without training (I still don't know who they are, but statistics say they exist), I know my limitations with the stock market so I wanted a little buddy that does what I cannot. My goal was to create a fully automated AI agent-based swing trader to generate passive income.

In today's article I will take my AI-Agent based swing trader to explain the basics when it comes to an agent-based architecture. But before diving into the details, let's start with the basics of software design. Understanding what an object-oriented pattern is on a high level is essential, and I’ll reference an article on AI agents I wrote not too long ago.

So, let's dive in.

AI agents will transform industries from finance to healthcare. In our previous article, "AI Agents: The Frontier in Bridging Pharmaceutical and Autonomous Systems", we explored their potential in pharmaceuticals. Combining them with classic design patterns is the key to building powerful AI agent systems. I used Microsoft AutoGen to simplify this process.

The Role of Design Patterns in Software Architecture

Introduction to Design Patterns

Design patterns are standardized solutions to common problems in software design. They provide a proven template for addressing recurring design challenges in software engineering. Using those patterns as a software/systems architect ensures to establish a well-designed, readable, maintainable software. You avoid one of the most common issues: DRY. Don't repeat yourself. As an example: You want one steering wheel in your car and not 2 more in the back for your kids to steer you off the road without you having any control over it. The concept of design patterns was popularized by the Gang of Four (GoF)—Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides—in their book "Design Patterns: Elements of Reusable Object-Oriented Software."

There are 23 GoF design patterns, which are categorized into three main groups:

• Creational Patterns: Deal with object creation mechanisms, ensuring that objects are created in a manner suitable for the situation. Examples include Singleton, Factory Method, and Builder patterns.
• Structural Patterns: Concerned with object composition, these patterns help form large object structures by organizing classes and objects. Examples include Adapter, Composite, and Facade patterns.
• Behavioral Patterns: Focus on communication between objects, defining how objects interact and how responsibilities are distributed. Examples include Observer, Strategy, and Command patterns.

Importance of Design Patterns

1. Modularity: Design patterns help break down complex systems into smaller, manageable pieces. This modular approach simplifies the development process and makes the system easier to understand and maintain.

2. Reusability: Patterns provide reusable solutions that can be applied to different projects. This reusability saves time and reduces the likelihood of errors, as these solutions have been tested and proven in various contexts.

3. Maintainability: Systems designed with patterns are generally easier to update and maintain. Patterns promote a clear separation of concerns, so changes to one part of the system often require minimal changes to others.

4. Scalability: Patterns facilitate scalable system architecture. For instance, the Singleton pattern ensures a class has only one instance, which can be critical in managing resources efficiently in large-scale applications.

5. Communication: Patterns provide a common vocabulary for developers, making it easier to convey complex ideas succinctly. This shared language improves collaboration and understanding among team members.

Overview of the Gang of Four Patterns

While there are many design patterns, today we will focus on those particularly relevant to our AI-driven swing trading system:

1. Facade Pattern: Simplifies interactions by providing a unified interface to a complex subsystem, making it easier to use and understand.
2. Singleton Pattern: Ensures a class has only one instance and provides a global point of access to it.
3. Strategy Pattern: Defines a family of algorithms, encapsulates each one, and makes them interchangeable. This pattern allows the algorithm to vary independently from clients that use it.
4. Observer Pattern: Defines a one-to-many dependency between objects so that when one object changes state, all its dependents are notified and updated automatically.

Importance of These Patterns in our AI Agent Architecture

1. Facade Pattern: In an AI system, the Facade pattern helps manage complex interactions between different AI agents by providing a unified interface. This simplification is crucial in ensuring the system remains modular and interactions are streamlined.

2. Singleton Pattern: Used to manage instances of agents that should have a single point of control. For example, a central coordination agent that oversees the entire system needs to be a singleton to maintain consistency and avoid conflicting actions.

3. Strategy Pattern: This pattern allows different algorithms to be selected at runtime, which is particularly useful in AI where various models and approaches might be applied based on the context. In our swing trading system, different strategies for stock analysis and trading can be switched dynamically.

4. Observer Pattern: Useful for monitoring changes in data or the environment. For instance, a monitoring agent can observe market conditions and trigger actions in other agents when specific conditions are met.

Practical Benefits

Implementing these design patterns in AI agent architecture brings several practical benefits:

• Efficiency: Using proven patterns reduces development time and enhances system efficiency.
• Robustness: Patterns are well-tested solutions, resulting in a more robust and less error-prone system.
• Flexibility: Patterns like Strategy and Observer provide the flexibility to change parts of the system without significant rewrites, facilitating easy updates and improvements.
• Scalability: As the system grows, patterns like Singleton and Facade help manage complexity, making the system scalable.

Applying Design Patterns to AI Agent Development

Concept and UML Diagram

To understand how we applied design patterns to our AI agent development, let's start by discussing the overall concept and the UML diagram that illustrates the architecture of our AI-driven swing trading system.

Our system consists of several specialized AI agents, each responsible for a distinct task within the trading workflow. These agents work together seamlessly, thanks to well-defined communication protocols and interaction patterns. Here’s a simplified UML diagram to visualize these interactions:

Explanation of Agents, Their Roles, and Communication Logic

Coordination Agent (Facade and Singleton Patterns)

• Role: The central agent that manages and coordinates all other agents, ensuring that the trading strategy aligns with the overall financial goals.
• Patterns Used: Facade (provides a unified interface) and Singleton (ensures only one instance exists).
• Communication: Oversees the entire process and interacts directly with all other agents to maintain consistency and centralized control.

Stock Advisor Agent (Strategy Pattern)

• Role: Analyzes market trends and suggests potential stocks for trading.
• Pattern Used: Strategy (allows the stock suggestion algorithm to be easily changed or updated).
• Communication: Provides initial stock suggestions, which are then validated by the Fact-Checking Agent.

Fact-Checking Agent (Strategy Pattern)

• Role: Validates stock suggestions to ensure they meet predefined criteria.
• Pattern Used: Strategy (allows different validation strategies to be applied).
• Communication: Works with the Stock Advisor Agent to confirm the viability of suggested stocks.

Technical Analysis Agent (Strategy Pattern)

• Role: Conducts technical analysis on the validated stocks to predict future price movements.
• Pattern Used: Strategy (enables use of various technical analysis methods).
• Communication: Analyzes stocks validated by the Fact-Checking Agent to provide deeper insights.

Fundamental Analysis Agent (Strategy Pattern)

• Role: Assesses the fundamental health of the stocks, such as financial statements and company performance.
• Pattern Used: Strategy (permits different fundamental analysis approaches).
• Communication: Evaluates the fundamental aspects of stocks that have passed technical analysis.

Risk Management Agent (Strategy Pattern)

• Role: Evaluates the risks associated with trades and sets parameters like stop-loss and take-profit levels.
• Pattern Used: Strategy (facilitates various risk assessment techniques).
• Communication: Assesses the risks of trades suggested by analysis agents and sets appropriate trading parameters.

Trade Execution Agent (Strategy Pattern)

• Role: Executes buy and sell orders based on validated stock suggestions and risk parameters.
• Pattern Used: Strategy (allows flexibility in execution strategies).
• Communication: Carries out trades based on the parameters set by the Risk Management Agent.

Reporting Agent (Strategy Pattern)

• Role: Generates detailed performance reports and tracks trading metrics.
• Pattern Used: Strategy (supports different reporting methods).
• Communication: Collects data from all agents to create comprehensive performance reports.

Monitoring Agent (Observer Pattern)

• Role: Continuously monitors market conditions and triggers actions based on predefined criteria.
• Pattern Used: Observer (notifies the system of market changes).
• Communication: Observes market data and notifies the relevant agents to take action when specific conditions are met.

Integrating AI Agents with Microsoft AutoGen

Microsoft AutoGen is a powerful tool that simplifies the creation and management of AI agents. It allows developers to define agents with specific roles and responsibilities, making it easier to integrate them into complex systems like our AI-driven swing trading platform.

How AutoGen Simplifies Agent Creation and Management

AutoGen streamlines the process of creating AI agents by providing a framework that handles much of the boilerplate code. This allows developers to focus on defining the unique behavior and interactions of each agent. With AutoGen, agents can be created, configured, and deployed with minimal effort, ensuring they work seamlessly together.

AutoGen provides several benefits for AI agent development:

1. Ease of Use: AutoGen abstracts much of the complexity involved in setting up and managing AI agents, making it accessible even to those with less experience in AI development.
2. Consistency: By using a standardized framework, AutoGen ensures that all agents are created and managed in a consistent manner, reducing the likelihood of errors.
3. Scalability: AutoGen supports the creation of multiple agents that can work together, allowing the system to scale as needed.

Example: Defining a Reporting Agent with AutoGen

Here’s an example of how a Reporting Agent is defined using Microsoft AutoGen:

In this example, the ReportingAgent is set up to monitor trading performance and generate reports. The ConversableAgent base class from AutoGen provides the necessary infrastructure to handle interactions and manage the agent's lifecycle.

Detailed Steps of Integration

1. Initialization: The ReportingAgent class is initialized with specific configurations, including the API key and settings for the language model it will use.
2. Role Definition: The system message defines the role of the agent, ensuring it knows its responsibilities within the trading system.
3. LLM Configuration: The llm_config parameter specifies the language model and its settings, allowing the agent to generate accurate and contextually appropriate responses.
4. Human Input Mode: By setting human_input_mode to "NEVER," the agent operates autonomously without requiring human intervention.

Benefits of Using AutoGen in Our System

By integrating Microsoft AutoGen into our AI-driven swing trading system, we gain several advantages:

• Reduced Development Time: AutoGen's framework minimizes the time required to set up and deploy new agents.
• Improved Reliability: Standardized agent creation ensures that all agents function reliably and interact seamlessly.
• Enhanced Flexibility: The ability to easily modify agent configurations and roles allows the system to adapt to changing requirements and market condition.

Conclusion

Looking back on what we've covered in this article, it's clear how important design patterns are for developing modern AI agent-based architectures. We've gone through the basics of design patterns, explored their practical benefits, and shown how they can be applied to create a robust AI-driven swing trading system.

Using tools like Microsoft AutoGen has made it even easier to build and manage AI agents. AutoGen takes care of a lot of the groundwork, letting us focus on making sure our agents work well together and do their jobs effectively.

As we move forward, it's becoming less about just being an AI expert and more about being able to design and build modern software systems that use AI. The real value now lies in integrating AI in a way that's scalable, maintainable, and fits seamlessly into larger systems. This ability to blend AI with strong software architecture is what's driving innovation.

The future of AI isn't just in the tech itself but in how we use it to solve real-world problems. By combining classic design patterns with modern tools like AutoGen, we can create AI systems that are not only powerful but also reliable and easy to manage.

Thanks for joining me on this journey. Let's keep pushing the boundaries of what AI can do and how it can be integrated into our software systems. The best is yet to come.