Agentic Development

A practical AI-Agent SDLC boilerplate for setting up governance, scope, data boundaries, tool access, approval gates, evidence rules, eval cases, and release checks before agents start…

Atlas is an Ambient Life Intelligence prototype that explores how AI agents can connect approved signals, evidence, memory, and user control.

A technical, plain-language guide to agentic development, AI agent teams, governance, framework choices, real workflows, and why companies are moving from assistants to governed agents.

A plain-language guide to MCP servers, Model Context Protocol, and how AI agents connect to tools like Figma, GitHub, DevTools, docs, databases, Jira, Drive, and local…

A practical GH-600 Agentic AI Study Platform with lessons, quizzes, labs, flashcards, scenarios, and an exam simulator for GitHub Copilot, MCP, guardrails, evaluation, and responsible AI.

AI agents should not be treated as magic workers that can freely read files, call tools, change code, query systems, create pull requests, send messages, or…

What I learned while debugging an accessibility scanner with AI agents, and why evidence matters more than pass/fail theatre.

What it takes to use agents without letting the work slip into vague claims, fake certainty, or sloppy release habits.

The later AI work feels much less like “let the model help everywhere” and much more like “if AI is here, it needs to fit inside…

I can see the enthusiasm in this stage, but I can also see that I had not settled the trust story yet. The first AI phase…