Provider choice stays open
Claude Code, Codex, Antigravity CLI, Mistral Vibe, OpenCode, subscription-backed access, and direct APIs all route through one lifecycle.
Adopt AI coding inside a disciplined engineering process.
The hard part of AI coding adoption is governing what the assistant ships. Generated code needs a plan, a pull request, a review, a way to fix it from feedback, and enough history for the next person to trust it. ProPR makes that the default path, for every agent and every developer.
Leadership concerns
- Cost visibilityPer-call model, tokens, cost, and duration in the usage log.
- Repository access controlA user whitelist decides who can trigger tasks and comment commands.
- Traceable logsPrompts, plans, usage, commits, and follow-up history on every task.
- Provider choiceFive agents and direct APIs route through one lifecycle.
- Repeatable pathsEvery agent and developer ships through the same plan, PR, and review path.
Overview
Coding-agent use often starts in private terminal sessions. Without a shared path, that produces one-off branches, inconsistent model choices, unclear spend, and pull requests that other people have to reverse-engineer.
ProPR gives the organization a common path while developers keep their preferred tools for exploration and live debugging. Planned changes move through ProPR when they need shared context, review, follow-up commits, and an audit trail.
One visible path makes AI changes governable.
Every task walks the same five steps, whichever agent runs it and whoever starts it. Review comments become follow-up commits on the same branch through PR fine-tuning.
01
Plan
ControlAn allowed user starts the task and approves the scope before code changes begin.
RecordPrompt, context, task split, chosen agent and model.
02
Isolated run
ControlThe agent edits inside its own Docker workspace, Git worktree, and branch.
RecordCommits, logs, model, tokens, cost, duration.
03
Pull request
ControlEvery result arrives in GitHub as a branch and a pull request.
RecordDiff, implementation summary, execution record.
04
Review
ControlReviewers comment, CI reports, and
/review posts a scored assessment on request.
RecordComment history, review findings, follow-up commits.
05
Merge
ControlA person merges the pull request unless you enable auto-merge for green PRs.
RecordNormal GitHub merge history on the base branch.
Access control spans the whole pathA user whitelist decides who can trigger tasks from the dashboard, CLI, and issue labels. Bot accounts are filtered out, and comment commands run only for allowed authors.
Cost shows up per task
The usage log attaches per-call model, cost, and duration to the change that consumed them. Optional Agent Tank adds subscription capacity context, and the cost comparison weighs subscriptions, API usage, seats, credits, and infrastructure together.
Security is explicit
Self-hosting keeps the delivery layer, credentials, task history, logs, Docker workspaces, and Git worktrees in your deployment. The outbound allowlist is optional, off by default, and needs elevated container privileges. Trust and security lays out the data boundary.
Recovery is part of the path
Retry, discard, revert, or correct a wrong attempt from the PR conversation, so generated code never has to be treated as a one-shot result.
The adoption conversation becomes concrete.
Which tasks deserve agents?
Keep direct agents for live exploration. Move planned features, fixes, refactors, and PR follow-ups into ProPR when they need review history.
Who can move a PR forward?
The user whitelist sets the entry gate. Comment controls, labels, and settings then decide which GitHub comments trigger fixes, reviews, branch updates, or recovery actions.
Which models are allowed?
Different providers have different cost, quality, context, and data-exposure profiles. Agent routing puts each phase on the access path you approve.
How do we avoid tool sprawl?
Use ProPR as the shared delivery layer around multiple agents. Every developer follows the same branch, PR, and audit path.
What to measure.
Track how many planned tasks become useful PRs, how often PR comments request follow-up commits, which agents run each phase, how much model usage each task consumes, and where humans still need to intervene. Task details and dashboards give that conversation a factual base.
Good first use cases.
Start with tasks where the boundaries are visible: small features, contained bug fixes, refactors with tests, repository cleanup, documentation updates tied to code, first implementations in empty repos, or PR review follow-ups. These show the value of planning, worktree isolation, GitHub review, and follow-up commits without asking the organization to change every development habit at once.
