AI & ML
AI Coding Agents Explained (With a Real Example)
Dev Learning Tools Dev.to (EN Zone)
19 views
AI coding agents have moved past autocomplete: they can read an entire codebase, plan a multi-file change, write it, verify it, and ship it, mostly without step-by-step approval.
I wrote up how that actually looks day to day, since this exact site (devlearningtools.com) is built entirely through a terminal agent (Claude Code): request comes in as plain language, agent reads the existing code first, plans the change, writes it, verifies with a real build, then commits and confirms it's live.
Also compares Claude Code, Codex CLI, Gemini CLI, Cursor, Copilot, and Windsurf, plus a Common Mistakes section from real experience.
Full writeup: https://www.devlearningtools.com/blog/ai-coding-agents-explained
Curious what everyone else's actual workflow looks like.
Read original: https://dev.to/devlearningtools/ai-coding-agents-explained-with-a-real-example-12k2
← Previous
n8n + RAG + MCP: Designing an AI Workflow That Knows Where Its Knowledge Comes From
Next →
How I Would Design an n8n AI System That Can Recover From Its Own Failures
Related
OpenClaw Plugin: Connect WAIaaS to LangChain, CrewAI, and Any AI Framework
AI & ML
60
Dev.to (EN Zone)
Why Giving AI Agents More Context Can Make Them Worse
AI & ML
49
Dev.to (EN Zone)
How to Optimize Development Cycles with AI: Numbers, Tools & Tactics [2026]
AI & ML
51
Dev.to (EN Zone)
How to Stop Leaking API Keys and PII to LLMs: Zero-Trust In-Memory Sanitization for Cursor & Claude
AI & ML
49
DEV Community
Comments0
No comments yet — be the first