Learn how technology leaders align modernization approaches with business opportunities. Strategic frameworks for ROI-focused legacy system updates.

We all know that AI's potential is enormous, yet the way to win with AI is to embrace customization and deep integration. It is for this reason that I believe (and I’ve been running tech services companies for 25 years) that the entire software services industry is about to change for the foreseeable future.

In the rapidly evolving landscape of AI, protocols like Google's Agent-to-Agent (A2A) and Anthropic's Model Context Protocol (MCP) are emerging as key enablers for building sophisticated AI systems. Understanding the differences between these protocols is crucial for making informed decisions about which one best suits your project's needs. This post will break down the core distinctions between A2A and MCP to help you choose the right tool for the job.

Agent workflows have quickly emerged as one of the most compelling frontiers in AI application development—but building them for real-world production use cases is still a nuanced and evolving challenge. At Stride, we recently partnered with a healthcare client to redesign a complex patient-facing workflow using LangGraph, LangChain’s graph-based orchestration framework.

Claude Code represents a fascinating approach to AI-assisted development, leveraging powerful language models through command-line tools and file manipulation. However, this architecture creates an interesting trade-off compared to editor-native AI assistants, particularly when it comes to tasks that modern IDEs handle through built-in tooling.

When choosing between AI coding assistants, understanding their underlying search capabilities can significantly impact your development experience. While both Claude Code and GitHub Copilot excel at code generation and assistance, they differ fundamentally in how they find and understand relevant code context—and this difference can lead to dramatically different results.