My AI-Assisted Development Workflow in 2026

How I actually use coding agents day to day—where they shine, where they waste your time, and how to stay in control.
Coding agents went from novelty to core tooling fast, but using them well is a skill. I share the workflow that has stuck: tight feedback loops, treating the agent like a fast junior who needs clear specs, writing tests first so the agent has a target, and keeping a human in the loop for architecture. I cover the failure modes too—confidently wrong refactors, context rot on large codebases, and the temptation to accept code you don't understand. The goal isn't to write less code; it's to spend your attention where it matters.
More Articles
Shipping Production-Grade AI Agents That Don't Break
Lessons from taking an LLM agent from a flashy demo to a reliable system that real users depend on every day.
RAG That Actually Works: Beyond the Naive Vector Search
Why most retrieval-augmented generation pipelines disappoint, and the hybrid retrieval architecture I reach for instead.
Streaming Generative UI in Next.js with the AI SDK
Streaming React components straight from the server lets you build AI interfaces with zero loading-spinner waterfalls.