SAN FRANCISCO, April 24, 2026 /PRNewswire/ — Runloop, the best enterprise-grade infrastructure platform for the development, evaluation, and scalable deployment of AI agents, announced today the launch of its Benchmark Job Orchestration platform, alongside a new integration with Weights & Biases that brings full traceability to AI agent evaluation workflows.

Together, these capabilities give organizations the best new foundation for deploying AI agents with confidence, combining large-scale execution with unprecedented deep visibility into agent behavior – without the need to build their own harness.

Enabling Trust in Production AI Systems

“AI agents are rapidly moving from experimentation into real business workflows, where they generate code, interact with systems, and make decisions that directly impact outcomes,” said Jonathan Wall, co-founder and CEO of Runloop. “As adoption accelerates, a new requirement is emerging at the leadership level: trust. That’s what Runloop provides.”

Business leaders need to know that these systems are performing reliably across real-world scenarios, improving over time without introducing regressions, operating within defined boundaries, and ready to be deployed into production environments.

Runloop’s Benchmark Job Orchestration platform is designed to meet that requirement.

It provides a system for continuously evaluating agents at scale, enabling organizations to establish clear performance baselines, compare changes over time, and ensure readiness before deployment.

Why This Matters Now

The pace of AI development has shifted from static model releases to continuous iteration on agents tailored to specific applications.

At the same time, the scope of what these systems can do is expanding into domains like software development, financial workflows, and operational automation.

This combination of rapid iteration and increasing responsibility makes evaluation a central function.

Organizations need systems that allow them to validate performance across complex task sets, compare models and agent versions under consistent conditions, track changes with confidence, and establish release gates before production deployment.

Benchmark orchestration provides that control layer.

From Execution to Full Visibility

Runloop delivers the execution and orchestration layer, managing the full lifecycle of benchmark workloads across thousands of environments.

The newly launched integration with Weights & Biases extends this by providing full visibility into each run.

As part of a joint technical implementation, benchmark runs executed on Runloop can be exported directly into Weights & Biases Weave, where teams can analyze detailed traces of agent behavior. These traces capture how systems actually operate, not just how they score. This allows organizations to move beyond high-level metrics and directly understand what their agents are doing and why.

Benchmarking becomes a continuous, repeatable system rather than a one-time exercise. Every run is executed at scale, captured as a structured and versioned artifact, and made available for comparison across models, agents, and releases. This creates a consistent foundation for evaluating change over time and making informed decisions about what to ship.

In practice, Benchmark Orchestration allows teams to run thousands of benchmark scenarios in parallel across models and agent configurations, detect regressions before they reach production, compare approaches on real tasks rather than synthetic prompts, and choose the configuration that meets performance targets at the lowest cost. One of the most immediate applications is model and agent selection, where teams evaluate multiple approaches side by side and select the system that delivers the best outcomes for a given cost envelope.

Runloop executes benchmarks in fully functional environments, including real codebases, terminals, and browser-based workflows. This ensures agents are evaluated under the same conditions they will encounter in production, so results reflect actual behavior rather than simplified test scenarios.

Learn more how Runloop approaches benchmark orchestration on its blog.

As organizations move toward production deployment of AI agents, the ability to evaluate, understand, and trust these systems becomes foundational. Runloop’s Benchmark Job Orchestration platform, combined with trace-level visibility through Weights & Biases, provides the infrastructure required to support that transition.

Benchmark Job Orchestration is available today as part of the Runloop platform. Learn more at https://www.runloop.ai.

About Runloop

Runloop is the best enterprise-grade infrastructure platform for securely developing, evaluating and scaling deployment of AI agents. Used by companies ranging from top model labs to startups, Runloop reduces time to deploy from months to hours, allowing developers to focus on their agents, not infrastructure. Learn more at runloop.ai.

About Weights & Biases

Weights & Biases provides tools for tracking, visualizing, and analyzing machine learning experiments, helping teams build and deploy AI systems with confidence.

Media contact:
Michelle Faulkner
Big Swing
michelle@big-swing.com
617-510-6998

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SOURCE Runloop.ai