MartinLoop is the control plane for autonomous AI coding agents. Today, it wraps Claude, Codex, OpenCode, and other agents with spend limits, proof checks, safety rules, rollback, and run receipts. The bigger build turns that into a full agent control plane: dashboards, HeadlessOS-style execution, team oversight, cost visibility, and a trusted record of what every agent did, why it kept going, and why it stopped. Unbounded AI agents are expensive. Fix that. Run it. Prove it. Stop it
Hey Product Hunt, I built MartinLoop after watching useful coding agents fail in the most expensive way: they keep trying. A loop can look productive while it burns tokens, repeats the same failure class, edits outside scope, or stops with no durable evidence.
MartinLoop is the open-source control layer around that loop. It adds hard budget caps, verifier-gated next-attempt admission, safety policy for scope/secrets/verifier commands, rollback evidence, and JSONL run records you can inspect or resume later.
The goal is simple: keep the speed of autonomous agents, but make every run accountable. I would love feedback from anyone running Claude Code, Codex, OpenCode, or similar coding-agent workflows in real projects: what receipt would you need before trusting a long-running agent overnight?
Good work! Are JSONL records capturing rejected paths or only the committed one?
One thing I would genuinely love feedback on from launch-day testers: what proof would you want before trusting a coding agent overnight? Budget receipt, verifier result, rollback path, or a file-level diff trail? MartinLoop is built around making those runs inspectable instead of just fast.
Small update for launch-day testers: the fastest way to try MartinLoop is now:
npx martin-loop demo
The feedback I want most is simple: after an AI coding run fails, what proof would make you trust the next attempt?
Beginner-friendly agent UX is mostly about predictable stop states. People forgive a small failure if the tool says what happened, what it checked, and why it stopped. They lose trust when the run keeps going with no new signal.
A small rule that catches a lot of fake progress: if the agent can't explain what changed since the last attempt in one sentence, it probably should not get another retry yet. That sounds strict, but it saves a lot of budget from "busy" loops that only reshuffle the same failure.
One lesson from testing coding agents: cost usually spikes after the first failure, not before it. If the agent can't show a receipt for done, the next retry should get harder, not easier: cap the spend, require a verifier check, and stop when the same mistake repeats. If you've seen a failure mode we should test before launch, I'd love that feedback.
About MartinLoop on Product Hunt
“Control AI coding agents with limits, proof, + run receipts”
MartinLoop launched on Product Hunt on June 2nd, 2026 and earned 76 upvotes and 10 comments, placing #23 on the daily leaderboard. MartinLoop is the control plane for autonomous AI coding agents. Today, it wraps Claude, Codex, OpenCode, and other agents with spend limits, proof checks, safety rules, rollback, and run receipts. The bigger build turns that into a full agent control plane: dashboards, HeadlessOS-style execution, team oversight, cost visibility, and a trusted record of what every agent did, why it kept going, and why it stopped. Unbounded AI agents are expensive. Fix that. Run it. Prove it. Stop it
MartinLoop was featured in Developer Tools (513.4k followers), Artificial Intelligence (469.9k followers) and GitHub (41.2k followers) on Product Hunt. Together, these topics include over 189.5k products, making this a competitive space to launch in.
Who hunted MartinLoop?
MartinLoop was hunted by Keesan. 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.
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Keesan