Cloud Orchestration for Benchmarks on Runloop

Benchmarks are indispensable for evaluating and training AI agents, but large-scale execution is painful. Traditionally, the burden of orchestrating thousands of runs and then parsing, collating & interpreting results falls on you. If you want to run reinforcement learning pipelines or compare agents against one another, you are responsible for building the supporting infrastructure: not just once, but for every benchmark you want to use.

Most benchmarks are also designed to run locally. Even when test cases execute on cloud infrastructure, the benchmark usually orchestrates all aspects of execution from a single machine. The benchmark runner assumes the role of central coordinator for all test scheduling and result collation. Scaling benchmarks across multiple models or agent configurations typically means writing custom orchestration code, manually managing environments, and hoping nothing crashes overnight.

These constraints prevent many teams from developing effective learning pipelines using benchmarks. Today we’re introducing Benchmark Jobs: Runloop’s solution to these pernicious problems.

Benchmark Jobs turn compatible benchmarks into a cloud-scale workload that can run across hundreds or thousands of isolated environments in parallel. It’s as easy as submitting a job and letting Runloop handle the execution lifecycle.

Benchmarks that took days, or even weeks, to execute on a local machine now complete in minutes as execution is distributed and managed for you with results collated and displayed in the Runloop platform automatically. Best of all, you don’t need to hawkishly watch over the job to make sure there are no errors.

Introducing Benchmark Jobs

Runloop already offers support for running benchmarks at large scale with dashboard results, audit traces, and protection against agent hacking. Benchmark Jobs extend these features. In Runloop a Benchmark Job is a managed execution workflow that runs a benchmark across a fleet of Runloop Devboxes.

When you launch a job, Runloop handles:

• Provisioning isolated Devboxes for each task with fast environment initialization
• Parallel execution across the cluster
• Robust anti-cheating protection via network controls & separation of scoring logic from the execution environment
• Collection and aggregation of benchmark results
• Clean teardown of environments after execution

Runloop provides the infrastructure required to run your favorite benchmark logic, with the same scoring scripts and task definitions, at scale, reliably and asynchronously.

A New Operational Model for Benchmarks

When benchmarks can run across hundreds or thousands of environments in parallel, their role changes. They become repeatable workloads that teams can run whenever they need to evaluate agents, compare models, or validate changes.

Runloop's Benchmark Jobs feature removes the infrastructure burden from benchmark execution, so teams can focus on what actually matters: improving agent performance.

As long-time fans of AI benchmarks, we’re very excited about this release.

What's Next?

Stay tuned for the next announcement: we'll show you how to use and run Benchmark Jobs. We'll also cover supported agents and benchmarks.

Learn More

Find out more about Benchmarks and Benchmark Jobs in our docs site.

Need help? Our customer engineering team would love to help.

Are you interested in running benchmarks at scale inside your VPC? Get in touch: sales@runloop.ai