Coding Agents Explained: What They Are And How They Differ From AI Assistants

A coding agent is an AI system that combines a language model with context, tools, an execution environment, and feedback mechanisms to independently plan, edit, test, and verify software changes, unlike autocomplete tools that predict code near the cursor or chat assistants that provide explanations without executing work. Its core workflow is an iterative loop of understanding a task, planning changes, using tools such as filesystems, terminals, Git, test runners, and documentation lookups, observing real outputs, verifying results through tests or builds, and revising its approach when failures occur. The surrounding harness, rather than the model itself, executes requested commands and enforces permissions, making tool access and sandboxing central to safety. Effective agents depend heavily on selective context management, strong tests, clear task definitions, and appropriately scoped autonomy, while long-term memory can preserve useful repository knowledge across sessions. They can handle tasks including debugging, feature development, refactoring, testing, and pull-request preparation, but may misinterpret ambiguous requests, pass inadequate tests, make overly broad changes, or struggle with production-only, visual, and performance issues. Coding agents can operate through terminals, IDEs, or cloud-based environments, and should be evaluated by task completion, correctness, diff quality, recovery from failure, human intervention, cost, and review burden rather than benchmark performance alone.

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