Architecture

Introduction Designing systems around large language models (LLMs) is not just about calling an API. Once you go beyond toy demos, you face questions like: How do I keep latency under control as usage grows? How do I manage costs when token usage explodes? How do I make results reliable and safe enough for production? How do I deal with context limits, memory, and personalization? How do I choose between hosted APIs and self-hosting? This post is a zero-to-hero guide to system design for LLM-powered applications. It assumes you’re comfortable with web backends / APIs, but not necessarily a deep learning expert. ...

Introduction Shared file storage is a foundational piece of many infrastructure architectures—from legacy on‑premises applications to modern containerized microservices. Two terms you’ll encounter often are: NFS (Network File System) – the long‑standing, POSIX‑style file sharing protocol. EFS (Amazon Elastic File System) – AWS’s managed network file system service. They’re related but not interchangeable: EFS uses NFS, but NFS is not EFS. This article explains: What NFS and EFS actually are How they’re similar and how they differ Performance, availability, security, and cost considerations Common architectures and when to choose each Practical examples (mount commands, Terraform snippets, migration patterns) The goal is to help you decide: “Should I just use standard NFS, or is EFS the right choice for this workload?” ...

As LLM-powered systems have grown more capable, they have also grown more complex. By 2025, most production-grade AI systems no longer rely on a single monolithic agent. Instead, they are composed of multiple specialized sub-agents, each responsible for a narrow slice of reasoning, execution, or validation. Sub-agents enable scalability, reliability, and controllability. They allow systems to decompose complex goals into manageable units, reduce context pollution, and introduce clear execution boundaries. This document provides a deep technical explanation of how sub-agents work, how they are orchestrated, and the dominant architectural patterns used in real-world systems, with links to primary research and tooling. ...

Introduction Retrieval-Augmented Generation (RAG) transformed how LLMs access external knowledge. But traditional RAG has a fundamental limitation: it’s passive. You retrieve once, hope it’s relevant, and generate an answer. If the retrieval fails, the entire system fails. Agentic RAG changes this paradigm. Instead of a single retrieve-then-generate pass, an AI agent actively plans retrieval strategies, evaluates results, reformulates queries, and iterates until it finds sufficient information—or determines that it cannot. ...

Table of Contents Introduction 1. Understanding A2A and Defining the Problem 1.1 What is A2A? 1.2 Typical A2A Requirements 1.3 Example Scenario We’ll Use 2. High-Level Architecture 2.1 Core Components 2.2 Synchronous vs Asynchronous 2.3 Choosing Protocols and Formats 3. Local Development Setup 3.1 Tech Stack Choices 3.2 Project Skeleton (Node.js Example) 4. Designing the A2A API Contract 4.1 Resource Modeling 4.2 Versioning Strategy 4.3 Idempotency and Request Correlation 4.4 Error Handling Conventions 5. Implementing AuthN & AuthZ for A2A 5.1 OAuth 2.0 Client Credentials 5.2 mTLS (Mutual TLS) 5.3 Role- and Scope-Based Authorization 6. Robustness: Validation, Resilience, and Retries 6.1 Input Validation 6.2 Timeouts, Retries, and Circuit Breakers 7. Observability: Logging, Metrics, and Tracing 7.1 Structured Logging 7.2 Metrics 7.3 Distributed Tracing 8. Testing Strategy from Day One 8.1 Unit Tests 8.2 Integration and Contract Tests 8.3 Performance and Load Testing 9. From Dev to Production: CI/CD 9.1 Containerization with Docker 9.2 CI Example with GitHub Actions 9.3 Deployment Strategies 10. Production-Grade Infrastructure 10.1 Kubernetes Example 10.2 Configuration and Secrets Management 11. Security and Compliance Hardening 12. Operating A2A in Production Conclusion Further Resources Introduction Application-to-application (A2A) communication is the backbone of modern software systems. Whether you’re integrating internal microservices, connecting with third‑party providers, or exposing core capabilities to trusted partners, A2A APIs are often: ...