A 30-day series on what changes when an engineering team — not just an individual engineer — adopts AI as a first-class part of how they work. From culture and workflows to governance and the human side.

A structured 30-day blog roadmap covering Claude Code, GitHub Copilot, Microsoft Copilot Studio, agentic AI, coding agents, and AI in SDLC — from a Lead AI Engineer with 11 years of experience.

Claude Code's context window is finite — large codebases require deliberate strategies for scoping context, routing Claude to the right area, and composing agents across package boundaries.

Claude Code runs headlessly in CI pipelines — useful for PR descriptions, changelog generation, automated review comments, and code quality checks, but with real cost and control tradeoffs to manage.

MCP servers let you extend Claude Code with tools specific to your internal stack — your build system, your internal APIs, your deployment pipelines — without forking the CLI or writing plugins.

Custom slash commands let you encode team workflows as repeatable prompts; hooks run shell scripts before and after tool calls — together they turn Claude Code from a smart editor into a consistent team process.

CLAUDE.md is the most leveraged file in an AI-native codebase — a poorly written one wastes every Claude Code session; a well-engineered one makes the model a reliable collaborator from the first turn.

A production-grade evaluation strategy for enterprise AI agents isn't about picking one tool — it's about defining evaluation layers, metric selection by agent type, and embedding quality gates into CI/CD.

Each agent framework has a natural evaluation partner — here's how to instrument LangGraph, Google ADK, AWS Strands, Microsoft Semantic Kernel, and Copilot Studio agents for production-quality evaluation.

Azure AI Foundry, AWS AgentCore, and Google's Gemini Enterprise Agent Platform each offer native agent evaluation that integrates directly with their deployment surfaces — here's what they actually provide.