AI Coding Agents
Overview
AI coding agents study how language models can work with repositories and development tools to carry out tasks such as fixing bugs, adding features, and building software. Common approaches combine planning and tool use with repository search, structured code editing, reusable skills or memory, and feedback from tests or execution. Benchmarks such as SWE-bench and Terminal-Bench measure issue resolution and command-line work, while newer evaluations test long-running, multi-file changes and behavior in real applications. Agents can solve many coding tasks, but success varies with the model, harness, and task; passing functional tests still does not guarantee secure, maintainable, or specification-faithful software, so reliable verification and effective human oversight remain open challenges.
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