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Multilingual Agents Need Workflow Tests, Not Translation Scores
PolyWorkBench landed on arXiv on 7 July 2026 with a useful correction to enterprise-agent hype: a global workflow is not a translated English task...
Agent Benchmarks Need Runtime Receipts, Not Model Labels
RuBench's revised 19 July 2026 release contains a small but important warning for coding-agent buyers: one audited product configuration silently...
Data Agents Need Exploration Budgets, Not SQL Magic
Data Agent Benchmark landed on arXiv on 21 March 2026 with a result that should make enterprise analytics teams pause: the best tested frontier model...
Assistant Agents Need Reminder Tests, Not Recall Scores
Most agent-memory benchmarks ask whether a model can recover old information. PM-Bench asks a harsher question: can an agent remember to do the right...
The 12-to-72 Problem: Computer-Use Agents Hit Human Scores but Miss the Point
Computer-use agents jumped from 12% to 72% on OSWorld in 18 months. The scores look like progress. The latency and efficiency numbers tell a different story.
Why Multi-Agent Papers Don't Replicate in Production
A paper from Tran and Kiela tested 28 multi-agent configurations across four architectures: Sequential, Parallel, Debate, and Ensemble. Every single one...
Multimodal Agents Score 40% Where Humans Score 72%
Every frontier lab now ships models that see, hear, and read. The assumption is that more modalities mean more capable agents. The benchmarks tell a...
Computer-Use Agents Fail Long Workflows, Not Mouse Clicks
Computer-use agents are clearing more short benchmark tasks, but the new failure line is workflow length. A June 2026 benchmark called OSWorld 2.0 tests...
How to Build Agent Evals That Catch Real Failures
Standard LLM benchmarks miss the failures that actually hurt in production. Here's how to build an evaluation system for agents that catches cascading errors, trajectory drift, and policy violations before they reach users.
Multi-Agent Systems Are Booming — But Real-Work Benchmarks Still Bite
Multi-agent workflows are growing fast, but APEX-Agents, AgentRx, Databricks, and Gartner show a gap between adoption, task success, and production readiness.