Blog & Resources
Insights, tutorials, and updates from the AvXion team
Context Engineering for Long-Running AI Agents
Most AI agents fail not because models are weak, but because context is treated as text instead of as a managed system resource. This article breaks down how long-running agents should engineer memory, context, and tool selection to remain stable over time.
Why Your AI Fails: You’re Not Engineering Context
Most AI systems don’t fail because large language models are weak or prompts are poorly written. They fail because context is treated as text instead of infrastructure. This article explores why hallucinations, memory loss, and tool misuse are usually context failures, not model failures—and why context engineering is emerging as a core systems discipline for building reliable, scalable AI applications.
Why Pub/Sub Becomes Inevitable in Scalable Architectures
As systems grow, tightly coupled services start to fail under load. This article explains why Pub/Sub becomes inevitable in scalable architectures, drawing from real production problems, engineering lessons, and how experienced teams use event-driven design to build resilient, high-growth systems.
How Netflix Engineered One of the World’s Most Resilient Distributed Systems
Netflix operates one of the most complex and resilient distributed systems in the world. This article explores how Netflix scaled its architecture to serve hundreds of millions of users globally—covering cloud migration, microservices, data distribution, content delivery, chaos engineering, and the real-world challenges Netflix faced while building a fault-tolerant streaming platform.
Navigating AI in the Workplace: Best Practices and Ethical Considerations
Discover practical best practices for integrating AI ethically into your workplace. Learn how to boost productivity while addressing bias, privacy, and fairness in 2025's AI landscape.
Why Use MCP (Model Context Protocol): A Practical, Human-Friendly Guide for Developers
Many developers wonder why Model Context Protocol (MCP) is needed when AI tools already work. This article explains what MCP solves, when to use it, and how it enables scalable, reliable, and secure AI agent systems in real-world applications.
The Rise of AI Coding Agents: What Meta’s Manus Acquisition Means for Developers
AI coding agents are reshaping how software is built. Meta’s acquisition of Manus signals a shift toward agent-driven development, where AI plans, codes, tests, and iterates alongside developers. This article explores what it means for modern software engineers and how to adopt AI agents responsibly.
gRPC in Depth: Architecture, Best Practices, and Production-Ready Patterns
Modern systems demand **speed, scalability, and strong contracts** between services. REST APIs have served us well, but as systems evolve into **microservices**, REST begins to show limitations in performance, type safety, and real-time communication.