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Request review
The developer requests review of one scoped URL, artifact, API surface, or provider-native document thread with a clear goal and lifetime.
Human judgment at AI speed
The right human.In the loop.
ReviewLoop gives an AI agent a safe, structured way to send selected work to the right reviewer, collect fast human judgment, and bring actionable feedback directly back into the work.
Scoped by the ownerOnly the selected URL, artifact, document, or bounded context is shared.
Routed by fitRoles, capabilities, experience, tags, and availability find the right reviewer.
Accelerated by AIPrivate pre-checks turn human review into fast confirm, reject, edit, or annotate decisions.
Closed by verificationSelected findings return to the agent, then go back to a human for retest.
One continuous loop
From work to judgment and back again.
ReviewLoop is not a raw tunnel into a coding agent. It is a controlled handoff. The owner chooses what leaves the machine, AI does the mechanical pre-evaluation, and a matched human supplies the judgment that matters.
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Pre-check and route
AI looks for basic quality signals while ReviewLoop matches the request to qualified, available reviewer profiles.
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Human validates
The reviewer interacts with the work in place, confirms or rejects AI suggestions, and adds precise annotations of their own.
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Agent receives findings
Structured feedback returns to the originating workflow. The owner chooses what to accept, fix, decline, or discuss.
After the work changes, ReviewLoop sends the selected fixes back for human verification—preserving a real release decision instead of assuming the AI got it right.
The actual product
One loop. Three focused views.
Each person sees only what they need: matched work for the reviewer, the artifact and feedback controls in the workspace, and live status for the developer.
Real ReviewLoop screens captured from the local product. The request and identities shown are synthetic demonstration data.
View 01 · Reviewer
Only relevant work reaches the queue.
Hard requirements filter eligibility. Preferred traits rank the qualified matches. Reviewers see exactly why the work fits them.
- Any-of role requirements
- All-of capability requirements
- Experience and availability gates
- Explainable soft-match score
View 02 · Reviewer
The work and the feedback stay together.
The live target is embedded next to structured feedback controls. AI suggestions are candidates—not conclusions—so the human can add, skip, edit, or comment in one place.
- Live URL or rendered artifact
- AI-assisted candidate findings
- Custom bugs, comments, and evidence
- Mediated questions about approved context
View 03 · Developer
The originating agent stays informed.
The developer can see claims, activity, feedback, context questions, and retest state without losing the coding workflow.
- Live lifecycle and occupancy
- Structured findings returned automatically
- Owner-controlled accept or decline decisions
- Selected fixes sent back for verification
Why it is valuable
Human review without a human-speed handoff.
AI can generate and revise work faster than a traditional review process can absorb it. ReviewLoop removes the mechanical waiting and packaging while preserving the human decisions that reduce risk.
Shorter feedback latency
Requests are already scoped, pre-evaluated, and routed. The reviewer spends time making decisions rather than reconstructing context.
Value: less coordination and waitingBetter use of human judgment
AI handles repeatable checks. People validate ambiguity, usability, risk, domain nuance, and whether the result is actually good enough.
Value: attention goes to consequential questionsMeasurable review outcomes
ReviewLoop records response time, findings, accepted issues, fixes, retests, and verification—without pretending estimated savings are observed facts.
Value: evidence for risk and efficiency decisionsControlled by design
Share the work. Not the machine.
Capability links are treated as credentials. Targeted work can require verified identities and profile matches. Reviewer input is always untrusted until the owner chooses what to do with it.
What can be shared
One selected URL, screenshot, build, artifact, API surface, or provider-native document
A clear review goal, expiration, authorized audience, and bounded context summary
Structured findings, evidence, comments, approved questions, and retest results
What is never implied
No raw Codex or Claude conversation, hidden prompt, browser profile, or agent login
No shell, unrestricted repository access, unrelated files, cookies, credentials, or secrets
No automatic permission for reviewer comments to change the work or authorize a release
Keep human judgment in the loop—without slowing everything down.
Let AI prepare the review. Let the right person make the call. Give the agent structured feedback it can act on, then close the loop with verification.