Quick summary
A coding assistant suggests code in the editor. A coding agent can pursue a goal through several actions: read repository instructions, search files, modify code, run tests, inspect failures and prepare a pull request. The important change is not merely longer code generation, but a feedback loop between the model and development tools.
The agent loop
- Interpret: turn an issue or request into a working objective.
- Inspect: read relevant code, tests, configuration and local instructions.
- Plan: choose files and verification steps.
- Act: edit code and invoke approved tools.
- Observe: read compiler, test or linter output.
- Revise: correct failures and present a reviewable change.
The loop can reduce routine effort because the agent receives evidence from the repository rather than relying only on its pretrained memory.
What becomes easier
Agents are useful for bounded work such as adding tests, updating repetitive interfaces, tracing a bug, migrating an API or drafting documentation. They can explore unfamiliar code faster and keep mechanical changes consistent across files. Parallel agents may investigate alternatives, although coordinating their edits adds complexity.
What humans now specify
A vague request produces more room for a plausible but wrong implementation. Effective tasks describe intended behaviour, constraints, excluded scope and acceptance tests. Repository instructions can establish commands, architecture boundaries and style. Developers increasingly review plans, evidence and diffs rather than typing every line.
Why tests are necessary but insufficient
Passing tests show conformity to the cases that exist. They may not reveal a misunderstood requirement, insecure design, weak error handling or a missing edge case. Generated tests can share the same mistaken assumption as generated code. Independent review, static analysis, dependency scanning and realistic integration tests remain important.
Permissions define the risk
An agent that can only edit a temporary branch has a different risk profile from one that can deploy, read secrets or modify production data. Use least privilege, isolated execution, allowlisted tools and auditable logs. Treat repository text and external content as potentially hostile instructions, not trusted commands.
Reality check
A successful demo on a clean task does not establish reliable autonomy across a large codebase. Performance depends on repository quality, test coverage, tool access and task clarity. Faster code production can increase review pressure and technical debt if teams merge changes without understanding them.
A strong operating model
Give the agent a scoped issue, disposable workspace and explicit checks. Require a small diff, a description of assumptions and evidence from tests. A named human remains accountable for architecture, security and merge approval. The most useful metric is not lines generated, but accepted changes that remain correct in production.
