A coding agent is an AI agent that can inspect a software repository, edit files, run commands, execute tests, and prepare a change for review. Unlike code completion, it works across multiple steps and uses feedback from tools to revise its plan before returning a result.
Google’s overview of agentic coding distinguishes autonomous planning and modification from simple suggestions. Current tools include Codex, Claude Code, and Cursor, but the durable concept is the loop around the model.
What makes a coding agent effective?
It needs repository instructions, targeted file search, a safe shell, and tests that can reject a bad edit. The test result matters more than the model’s confidence, because generated code can look plausible while breaking an adjacent path. Agent skills can package project procedures and deep agents can split long work into bounded subtasks.
What controls should surround a coding agent?
Run its commands through sandboxed code execution, restrict network and secret access, and require review before merging. A coding agent should produce evidence that another person can inspect, including the diff and fresh test output. Our analysis of why trust replaced typing as the bottleneck covers that operating model.
Frequently asked questions
How is a coding agent different from code completion?
Code completion predicts text at the cursor. A coding agent can explore a repository, change several files, run tools, interpret failures, and continue until a defined task is complete.
Should a coding agent be allowed to merge its own changes?
No. The agent can prepare and verify a change, but a separate reviewer or human should control merge authority for production repositories.
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