Resource library

Long-horizon agents operate across many dependent states and transitions, often spanning multiple tools, environments, and periods of external feedback. The difficulty comes from preserving coherent progress as earlier decisions constrain later actions. A single workflow may involve researching evidence, changing files or records, waiting for external responses, revising plans, validating intermediate results, and returning to earlier systems with new information….

Most agent benchmarks still evaluate a thin slice of the job. The agent receives a task, produces an answer, gets scored, and the episode ends. Enterprise workflows work differently. An underwriting agent may need to read policy documents, inspect customer records, call internal tools, ask a simulated user for missing information, update state, and follow approval rules. A correct final…

Anthropic’s Claude Opus 5 recently debuted as the second model overall on the current Senior SWE-bench leaderboard, behind Fable 5. It also achieves the highest score of any evaluated model on the benchmark’s Bug & Performance Investigation category, reinforcing the rapid progress frontier coding models continue to make on increasingly realistic software engineering tasks. Just as notable, Opus 5 reaches…

At our latest Snorkel AI Reading Group, Henry Ehrenberg presented Senior SWE-Bench, an open-source, Harbor-compatible benchmark for evaluating coding agents on realistic, senior-level software engineering work. Its 100 tasks, with 50 public and 50 kept private to mitigate contamination, are sourced from real pull requests across 12 production repositories and cover complex features, migrations, bugs, and performance issues. Senior SWE-Bench…

We’ve evaluated Grok 4.5 on Snorkel’s GDPval+ dataset, Snorkel’s expert-created dataset of professional workplace reasoning tasks from across the economy. To compare performance against other frontier models, we ran the evaluation alongside GPT 5.5 and Claude Opus 4.8. Overall, Grok 4.5 demonstrated the strongest overall performance. Dataset GDPval+ is part of the Snorkel Data Series (SDS), Snorkel’s portfolio of expert-curated…

A top 10 US bank manages CLO portfolios totaling billions in assets, each governed by contracts up to 500 pages.

A global media intelligence firm analyzes hundreds of millions of sources daily – from public news, social, and broadcast to proprietary analyst-curated databases – to help large enterprise clients manage communications, reputation, and strategic decision-making. Their competitive advantage is the layer on top of publicly available data: in-house human editorial teams, proprietary scoring and analytics frameworks, and years of analyst judgment refined into decision-grade intelligence. When a crisis signal is building or a competitor’s narrative is gaining traction, speed and accuracy matter enormously. Historically, getting an answer meant waiting for a human analyst to manually aggregate across those sources: a process measured in hours, not seconds.

At our latest Snorkel AI Reading Group, Yiyou Sun and David (Xinyang) Han (UC Berkeley, Center for Responsible and Decentralized Intelligence) presented Agents’ Last Exam (ALE) — a benchmark designed to evaluate AI agents on long-horizon, economically valuable, real-world tasks with verifiable outcomes. ALE is a collaboration between Berkeley RDI, Snorkel AI, and 300+ expert contributors across 55 professional subfields. ALE asks a deceptively simple question: can…

Most agent benchmarks evaluate each task as an independent episode. The agent receives a task, produces an answer, gets scored, and moves on. The next task starts as if the previous one never happened. That setup misses a core requirement for deployed agents. A coding agent, research assistant, data analyst, or workplace assistant should improve as it works across repeated…