MartinLoop helps engineering teams safely scale AI coding agents from experiments into accountable, measurable production workers.
AI coding agents are powerful, but they can also run too long, spend too much, change the wrong files, and leave you guessing what happened. MartinLoop gives every agent run a budget, safety checks, rollback, and a clear receipt showing what changed, what passed, what failed, and what it cost. Use the MCP or directly in your CLI. Model-agnostic above the agent.
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.
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?
One thing launch day made painfully clear: people don't really want smarter loops, they want safer ones. The trust breaks after the second or third retry when nothing changed and the system keeps spending anyway. The most useful rule we've found is simple: before another retry, show what changed. If nothing changed, stop.
One thing we've learned building this: a budget cap by itself doesn't save you. The expensive runs usually happen after the first failure, when the agent keeps trying without any new evidence. The rule that matters most is simple: before another retry, show what changed. If nothing changed, stop. If anyone here has a real agent failure story, I'd genuinely love to hear it because those cases shape the product more than feature wishlists do.
This is cool. Personally, I think I just built trust over time with the tools as I learned how to better use them and slowly started to run them for longer sessions and then progressed to overnight. I wouldn't say I've been tracking the cost (although perhaps I should do more of that) as much as the quality that I see. So if an agent went off course for a long run, I try to understand what happened and update my control plane around it for future runs to mitigate that in the future. I do think this is a nice additional layer to have.
One thing we’ve learned building this: a budget cap alone doesn’t really save you. The ugly runs happen after the first failure, when the agent keeps trying without any new evidence.
The rule that seems to matter most is simple: before another retry, show what changed. If nothing changed, stop.
If anyone here has a real agent failure story, I’d genuinely love to hear it. Those stories shape the product way more than feature wishlists do.
A pattern we keep hearing from teams is that the expensive part is rarely the first agent mistake. It is the quiet retry loop after the first mistake, where nothing meaningful changed but the system keeps spending anyway. The most useful rule we have found is simple: before another retry, show what changed. If nothing changed, stop the run. That has shaped MartinLoop more than any benchmark has.