15 cloud situations. 43 merge-ready fixes. 100% loop closure. 12 minutes and $17 to creator as soon as; seconds and zero-cost apply all over the place after.
Gomboc AI at present printed the primary open benchmark for AI code remediation, documenting the results of its deterministic remediation platform throughout 15 real-world cloud situations spanning AWS, GCP, and Azure. The benchmark is publicly obtainable at https://www.gomboc.ai/show-your-work.
The methodology is open, and the situations are printed on GitHub. Any crew can run the identical benchmark and publish their very own numbers.
PR assessment time is up 91%. Builders are merging 98% extra code. AI-generated adjustments are reaching manufacturing quicker than any crew can validate them. And each time a repair must be redone, organizations pay the token value twice. Gomboc AI sits on high of the instruments groups are already utilizing – Cursor, Claude Code, Copilot – to make each repair correct, ruled, and cost-optimized. The benchmark is the proof.
Additionally Learn: AIThority Interview With Rohit Agarwal, Founder & CEO of Portkey
The benchmark covers 15 manufacturing cloud situations throughout safety, reliability, and price.
● The safety findings cowl misconfigured IAM insurance policies, open community safety teams, and unencrypted storage throughout AWS, GCP, and Azure.
● The reliability findings embody a manufacturing database dealing with 50,000 orders a day with no backups and no failover.
● The price findings embody greenback quantities: $2,050 per thirty days for duplicate CloudTrail configurations, $279 per thirty days for redundant NAT gateway routing, and $870 per thirty days for outsized EC2 cases.
Each repair generated by Gomboc is idempotent, examined, and traceable to a coverage. None requires a human to interpret the output earlier than it may be utilized.
The benchmark numbers symbolize the worst case, not the regular state. The 12 minutes and $17 in tokens cowl the one-time work of authoring insurance policies for situations Gomboc had by no means encountered earlier than. As soon as a coverage exists, making use of it throughout each repository, each pipeline, and each future incidence of the identical problem takes seconds and successfully incurs zero in token value.
That is the core of the Gomboc economics: enterprises pay as soon as to codify a repair, then apply it infinitely. Each different AI coding software pays the total technology value each time an analogous problem seems, as a result of there isn’t a reminiscence, no coverage layer, and no governance to hold the work ahead.
“The period of vibes-based AI tooling is over. Each crew evaluating AI for code remediation deserves to know 5 issues: Is the output idempotent? Is it ruled? Does it align to a coverage? Can it’s reproduced? Is there an audit path? We will reply sure to all 5. We’re inviting each different software within the stack to do the identical.” Ian Amit, CEO and Co-Founder, Gomboc AI
The benchmark methodology is absolutely documented and reproducible. The 15 situations are printed on GitHub as open ORL recordsdata, alongside the rule units, take a look at circumstances, and anticipated outputs for every repair. Any crew can clone the repository, run the benchmark in opposition to the identical situations, and publish their very own outcomes. That is intentional. Gomboc will not be asking the business to take its phrase for it. We’re asking the business to run the identical take a look at and present its work.
Additionally Learn: AI-Pushed Threat Intelligence: How FIs Are Predicting Systemic Shocks
[To share your insights with us, please write to psen@itechseries.com ]
