Agentic AI marks a shift from reactive chatbots to autonomous mission partners. Government must adopt unified Agentic Infrastructure—combining resilient agent execution and governed AgentOps—to enable machine-speed decisions. Platforms like Scale’s SGP and Agentex deliver interoperable, durable, and accountable autonomy for mission assurance.

We’re open-sourcing MCP-Atlas, including the dataset, evaluation environment, and updated results for a benchmark designed to measure how reliably AI agents use real tools. MCP-Atlas evaluates realistic, multi-step workflows that run against real Model Context Protocol servers, exposing where agents succeed—and where they still fail—when tool discovery, parameterization, and execution must work together.

Today on the podcast, the Enterprise team reviews AI predictions they made 6 months ago and sees if they came true or not. Spoiler: we got a lot wrong...but some unexpected things right! They conclude by predicting what we can expect to see in 2026 with enterprise AI.

Audio MultiChallenge is a new benchmark designed to stress-test native Speech-to-Speech models on what actually makes voice hard: mid-sentence corrections, audio-only cues, instruction drift, and long-horizon self-consistency. By evaluating real human conversations rather than synthetic text-to-speech we uncover where current audio systems still fail, and what it will take to build voice agents that truly listen.

2025 was a landmark year for AI reasoning, coding, and multimodal capabilities, but which models actually delivered the best results? After introducing 15 new benchmarks and publishing over 450 evaluations across the industry's top 50 models, the data is finally in. We are proud to announce the winners of the inaugural SEAL Models of the Year Awards. From the "Best Agentic Model" to the "People’s Favorite," find out which LLMs proved their dominance on the leaderboards and claimed the top spots in our rigorous analysis.

A first of its kind study from Oxford Economics shows how data annotation is an important part of AI innovation and creating flexible earning opportunities for people across the US. Meet the contributors behind the industry and learn about their impact on the economy.

Scale AI outlines how exporting the full U.S. AI tech stack can secure global leadership, standards, and economic competitiveness.

We often discuss "AI Risk" as if it were a single, shapeless shoggoth. But the truth is that risk comes from specific sources, each requiring a different defense. This article dismantles the monolith, categorizing the six distinct vectors of danger: Adversaries, Unforced Errors, Misaligned Goals, Dependencies, Societal Impact, and Emergent Behavior. Learn to distinguish between these threats so you can move from panic to precise preparation.

To measure the propensity of agents to make unsafe choices, Scale, the University of Maryland, and other collaborators developed PropensityBench. This benchmark simulates real-world pressure by allowing agents to choose between a safe approach that consistently fails and a functional, harmful shortcut, revealing their true inclinations. The benchmark reveals that agent safety compromises significantly under pressure.

The next generation of AI agents is shifting from passive workers that receive user commands and generate outputs to active agents that plan, act, observe, and improve on their own. Agents now choose how to complete a task, which tools to use, and whom (or which agent) to collaborate with. LLMs didn’t invent agency, but they democratized it by turning frontier-level reasoning into a simple API call, letting teams compose complex systems from simple building blocks.