Resource library

The Snorkel research team collaborated with the rLLM team at UC Berkeley on the Agentica project, using their open-source rLLM framework to fine-tune Qwen3-4B-Instruct-2507, delivering a model that beats Qwen3-235B-A22B on Snorkel AI’s expert-curated financial benchmarks – at 1/60th the size. A full breakdown of the results are published in the rLLM blog here. The key insight? Just focus on…

Error analysis of 8 models on Agentic Coding tasks Successful completion of complex tasks doesn’t come from models being always right. It comes from models being resilient when things go wrong. To get a deeper understanding of model behavior in agentic environments, our team analyzed all of the errors found in the full traces of tasks from our Agentic Coding…

Today, AI is marked by a growing asymmetry: the excitement around agentic AI is real — backed by quantitative progress on model cards and genuine leaps forward, especially in coding. But ask individuals or enterprises where they feel ready to deploy agentic automation in high-stakes, domain-specific settings outside of coding… and you will find hesitation. The reason: our ability to…

As AI agents integrate into enterprise applications, their evaluation demands benchmarks that reflect the complexity of real-world operations. Instead, existing benchmarks overemphasize open-domains such as code, use narrow accuracy metrics, and lack authentic complexity. We present UNDERWRITE, an expert-first, multi-turn insurance underwriting benchmark designed in close collaboration with domain experts to capture real-world enterprise challenges. UNDERWRITE introduces critical realism factors…

AI agents may soon become capable of autonomously completing valuable, long horizon tasks in diverse domains. Current benchmarks either do not measure real-world tasks, or are not sufficiently difficult to meaningfully measure frontier models. To this end, we present Terminal-Bench 2.0: a carefully curated hard benchmark composed of 89 tasks in computer terminal environments inspired by problems from real workflows….

A leading global firm transforming insurance subrogation operations with AI found that manual review processes capped their throughput to ~30% of available claims. This bottleneck left significant revenue on the table and froze their ability to scale. The path to automation was further blocked by severe data imbalances where the critical signals for coverage appeared in only a small fraction of claims, making traditional AI models unreliable.

Strategic dominance in the Indo-Pacific relies on the ability to track and coordinate friendly forces — ”blue objects” — with absolute precision. To maintain operational awareness in dynamic and contested environments, the Department of War identified a requirement for adaptable, dual-use technologies that enhance logistics and decision-making resilience.

SlopCodeBench reveals how AI coding agents degrade code quality over time—measuring “slop,” technical debt, and architectural erosion across iterations.

Today, we’re sharing details about the Snorkel Agentic Coding benchmark—a comprehensive evaluation suite designed to test whether agents can handle the full complexity of software engineering work.