Anthropic's recent Claude Code coverage highlights a broader change in enterprise AI adoption. Coding agents are no longer being evaluated only by how much code they can write. Teams are testing whether agents can understand a repository, preserve context, explain tradeoffs, and survive review.
That is a different standard from autocomplete. A coding agent touches build systems, tests, documentation, issue triage, and sometimes deployment workflows. The central product problem becomes control: how much autonomy should the agent have, and where should a human approval boundary sit?
The most credible agent workflows share a few traits. They make plans visible. They cite changed files. They run checks. They fail clearly when context is missing. They do not hide errors behind fallback behavior. That is why agent design is now partly a software-engineering governance problem, not just a model-quality problem.
For buyers, the practical question is not whether an agent can produce a convincing patch in a demo. It is whether the organization can review, reproduce, and audit that patch after the model moves on.