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.
Thirty posts across five arcs. Here's the honest synthesis: the biggest surprises, the things I was wrong about, what actually changed in my thinking, and where I think all of this is heading.
When a single agent misbehaves you have one thing to debug. When a multi-agent system misbehaves, the problem could be anywhere across orchestrator, specialists, connectors, and flows. How to build observability that makes the invisible visible.
Connectors, custom APIs, authentication patterns, and the integration decisions that determine whether your Copilot Studio agents actually hold up under enterprise load. What works, what breaks, and what to build carefully.
A single Copilot Studio agent handles one domain well. A multi-agent system handles complex enterprise workflows — routing, handoff, shared context, and orchestration. Here's the architecture and the decisions that matter.
Copilot Studio is not a chatbot builder. It's a platform for deploying AI agents across enterprise systems. Here's the developer's honest guide — what it's genuinely good at, where it has rough edges, and how to think about it alongside code-first approaches.
An agent that runs without crashing is not the same as an agent that works. Evaluating coding agent quality properly — what to measure, how to build an eval suite, and what the numbers actually tell you.
Single-step agents are relatively predictable. Multi-step agents are where things get interesting — and where they fall apart in ways that are hard to diagnose. The orchestration patterns that hold up and the failure modes that will find you eventually.
Memory is where agentic systems get subtle and hard to debug. Context windows fill up. Conversation history grows. The agent starts forgetting, confusing, or contradicting itself. Here's how to design memory properly from the start.