Demystifying AI Agents for Software Engineers

A practical comparison between AI agent architectures and traditional software patterns to make AI more approachable for software engineers.

In this post, I decided to simplify AI Agent concepts for software engineers by comparing AI Agents with Microservice Architecture to make it easier for fellow software engineers to start working on AI projects. Keep in mind that a multi AI Agents architecture could be designed differently depends on various use cases.

So, here’s a simplified comparison of AI Agent Architecture’s components for software engineers:

• Modular Architecture ↔ Microservice Architecture: Both architectures emphasize modular, independent components that work together efficiently.

• AI Agent ↔ Service: An AI agent operates like a service that handles specific tasks autonomously, similar to microservices but AI agents can decide on their own with added intelligence and adaptability.

• Task ↔ Function: Each task in an AI agent is similar to a function/method in a microservice.

• Tool ↔ APIs or Microservices: Tools in AI agents are comparable to APIs or even other microservices that enable interaction with external systems or services.

• Short Memory ↔ Caching: Short-term memory is a temporary storage for quick retrieval, similar to caching.

• Long Memory ↔ Database Storage: Long-term memory provides persistent storage, much like databases (SQL and No SQL).

• Fine-Tuning and RAG ↔ Domain-Specific Services: Fine-tuning and RAG make AI agents domain-specific, similar to domain-specific microservices.

• Multi-Agent System ↔ Microservice Architecture: A network of agents mirrors a distributed microservice architecture.

• Infrastructure & Cloud: AI Agents and Microservices rely on scalable cloud solutions for deployment and maintenance.

• Queue & Notifications: Task coordination in AI systems uses queues and notifications, just like in event-driven and microservice architectures.

• Vector Database (New Database Paradigm): Vector databases store embeddings for similarity search, offering a new approach to data retrieval.

Additional Concepts for AI Agents and Microservices

• Orchestration (e.g., LangChain or LangGraph): LangChain and LangGraph orchestrate AI workflows at the application level. They coordinate interactions between agents and tools. For infrastructure orchestration, Kubernetes can still be used to manage containerized AI agents.

• Observation & Feedback Loops: Agents can learn or adapt dynamically via feedback loops, while traditional microservices lack.

• Autonomy vs. Predefined Logic: AI agents have decision-making autonomy, leveraging machine learning models, whereas microservices execute predefined logic.

• Security & Governance: AI agents may require additional considerations for ethical AI, prompt injection attacks, and hallucinations. These are not typical concerns in microservice architectures.

• Cost Management: Cloud costs for AI agents can be higher due to LLM inference, embedding queries, and GPU/TPU requirements.

If you found this post helpful, feel free to share it with your team and help everyone learn and adapt to AI.