This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Product upvotes vs the next 3
Waiting for data. Loading
Product comments vs the next 3
Waiting for data. Loading
Product upvote speed vs the next 3
Waiting for data. Loading
Product upvotes and comments
Waiting for data. Loading
Product vs the next 3
Loading
Quarterback
Automate your AI manager pipeline for agentic workflows
Coding agents say "done" when the job isn't finished, a requirement skipped, onboarding bypassed, an unrelated file touched. Quarterback compiles your request into a task contract, then independently verifies the result against it. Deterministic checks first, AI judgment second.
Hey guys, I'm Arshad. I build Quarterback at Velora.
The thing that started this: I kept noticing that my coding agent wasn't wrong so much as incomplete. The code compiled. Tests passed. The diff looked fine. And the backend field existed while the frontend never sent it. I'd catch it three commits later.
That's not a model problem. It's a missing layer. The agent interprets the task, implements its interpretation, then reviews its own interpretation and reports success. Nobody independently checks whether the original job got done.
Quarterback is that check. Your request becomes an explicit task contract goal, constraints, acceptance criteria, verification plan. Your existing agent does the work (Claude Code, Cursor, Codex, whatever you already run). Then a separate layer verifies the result against the contract: deterministic evidence first tests, types, diff scope, schema checks and model judgment only where the question is genuinely semantic.
The metric I care about is human interventions per accepted task. Every message where you correct, clarify, redirect or verify the agent. Accepted matters a system that stops asking questions while shipping bad results hasn't improved.
I'm not going to post a number today. I've seen enough vendor benchmarks in this category to know how they're made, and I'd rather publish real cohort data with the methodology attached than a chart I made up. That's coming.
What I need right now is 10–20 developers who use a coding agent daily on a real repo. We measure your baseline, then run the same work through Quarterback, and you see the delta on your own tasks. Anonymized metrics only.
Two questions I'd genuinely like answered in the comments:
What's the last thing your agent said it finished that it hadn't?
What do you always check yourself before trusting "done"?
I'll be here all day.
About Quarterback on Product Hunt
“Automate your AI manager pipeline for agentic workflows”
Quarterback was submitted on Product Hunt and earned 7 upvotes and 1 comments, placing #49 on the daily leaderboard. Coding agents say "done" when the job isn't finished, a requirement skipped, onboarding bypassed, an unrelated file touched. Quarterback compiles your request into a task contract, then independently verifies the result against it. Deterministic checks first, AI judgment second.
On the analytics side, Quarterback competes within Productivity, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.7M followers on Product Hunt. The dashboard above tracks how Quarterback performed against the three products that launched closest to it on the same day.
Who hunted Quarterback ?
Quarterback was hunted by Arshad Shaik. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Quarterback including community comment highlights and product details, visit the product overview.