How AI coding agents work—and what to remember if you use them

AI coding agents are basically supercharged LLMs that help automate coding tasks, but they’re handcuffed by context limits that cap how much code they can juggle at once—think of it as your AI buddy getting overwhelmed by too many tabs open, which can torch your token budget if you’re not careful. To dodge these constraints, clever devs fine-tune these agents to offload heavy lifting to other tools, like scripting Python to crunch data from files instead of force-feeding everything into the LLM, as seen in Anthropic’s Claude Code that smartly uses database queries and Bash commands to handle massive datasets without the full bloat. Another key trick is dynamic context management, where agents compress their memory by summarizing the essentials—like architectural choices and bugs—while ditching fluff, so they don’t completely lose their train of thought but might need a quick refresher from your code notes. If you’re an SMB or MSP dipping into this, remember to keep an eye on token usage to avoid surprise costs, ensure agents leave clear trails in your codebase for easy reorientation, and treat them as helpful sidekicks rather than flawless wizards to sidestep potential errors from oversimplification.

Source: https://arstechnica.com/information-technology/2025/12/how-do-ai-coding-agents-work-we-look-under-the-hood/