"No magic, no sentience - just prompts, tools, context, and memory." That's how developer Edy Silva frames AI coding agents in a new post on the Codeminer42 engineering blog, and it's a useful corrective to the mystique that still surrounds tools like Cursor, Claude Code, and GitHub Copilot.
The core argument is that most developers using AI coding agents don't understand what's happening under the hood, and that ignorance makes them worse at using these tools. An AI coding agent isn't a junior developer trapped in your terminal. It's a loop: take a prompt, gather context from your codebase, pick from available tools (file reads, terminal commands, web searches), execute, check the result, and repeat.
This matters practically. When you understand that an agent's quality depends heavily on the context it can access, you start structuring your projects differently. Clear file names, good README files, and consistent code patterns aren't just good practice anymore. They're how you make your AI agent more effective, because the agent is literally reading those files to decide what to do next.
The memory component is worth understanding too. Most coding agents maintain context only within a single session (the conversation or task window). Some newer implementations persist memory across sessions through files like CLAUDE.md or .cursorrules, but this is still primitive compared to how a human developer builds project knowledge over weeks. Knowing this helps set realistic expectations: your agent is smart but amnesiac unless you explicitly give it memory scaffolding.
The "tools" concept is the one that separates agents from plain chatbots. A chatbot generates text. An agent can read files, run commands, search codebases, and make edits. The quality difference between coding agents often comes down to which tools they expose and how well they chain them together, not just which language model sits underneath.
For anyone spending significant time with AI coding tools, understanding these mechanics turns vague frustration ("why did it do that?") into specific, fixable problems ("it didn't have enough context" or "it picked the wrong tool for this step"). That shift from treating the agent as a black box to understanding its decision loop is the single biggest thing most users can do to get better results.