The Tool Desk
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What “long horizon” should mean in an evaluation
A long-horizon evaluation asks whether an agent can maintain goals, constraints, state and verification over many dependent actions. The unit of measurement is a completed task, not an impressive intermediate action. A task is complete only when an independent check confirms the requested end state.
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The most interpretable horizon definition uses human work time as a difficulty scale. METR defines its measure this way: “The task-completion time horizon is the task duration (measured by human expert completion time) at which an AI agent is predicted to succeed with a given level of reliability.” In practice, an evaluator estimates how long an appropriately skilled person takes, measures agent success on tasks at those durations, and fits a success-versus-duration model.
This is not a stopwatch for autonomous operation. A reported 50%-time horizon does not mean the agent can run independently for that many hours. It means that, for tasks in the selected suite whose skilled-human completion time is around that value, the fitted model predicts a 50% chance of verified completion. The estimate changes with the task sample, human-time procedure, agent configuration and chosen probability threshold.
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Use more than one reliability threshold
Choose thresholds that match the risk of the work. A 50% point describes the difficulty at which success and failure are equally likely; it is useful for comparing capability frontiers but is too unreliable for unsupervised production work. An 80% point is a more demanding operational reference. You can also report 90% or a task-specific service-level target when failures are costly.
For each threshold, state the fitted curve and its uncertainty. A simple logistic model can be written as p(t) = 1 / (1 + exp(-(a + b log t))), where t is estimated human completion time and p is predicted success probability. The exact model, transformation, exclusions and confidence interval must be published; changing them can move the apparent horizon.
Why the measurement design changes the result
Long-horizon scores are conditional measurements. They describe an agent operating under a particular task distribution, starting state, interface, policy and attempt budget. Change any of those and the number answers a different question.
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| Axis | What to specify | Why it matters |
|---|---|---|
| Task domain | Software engineering, browser workflows, full desktop use, machine-learning engineering, cybersecurity or another domain | Skills do not transfer automatically between domains. |
| Horizon definition | Human-expert duration, elapsed agent time, action/tool-call count, or a combination | Action count and wall-clock time are not equivalent to task difficulty. |
| Task distribution | Number, diversity, duration range, starting-state realism and whether tasks are newly authored | A narrow or leaked set can inflate reliability. |
| Outcome rule | Binary completion, partial credit, executable state check, human grading or model-based judging | Different rules reward different kinds of progress. |
| Reliability and uncertainty | Attempts per task, confidence intervals, task-level variance and curve-fitting method | An average can hide a few catastrophic tasks or a wide error bar. |
| Human baseline | Participant expertise, tools and time-estimation procedure | Human minutes are only comparable when the conditions are documented. |
| Agent configuration | Model version, prompt, reasoning setting, tools, batching, context, retries and step limit | A score belongs to that configuration, not to an abstract model name. |
| Cost and safety | Tokens, wall-clock time, tool cost, approval boundaries and separate safety tests | Cheap or fast failure is still failure, while consequential actions need stricter controls. |
Build a task suite that exposes sustained-work failures
Start with a precise completion contract
Write the user goal, starting state, allowed resources, forbidden actions, intervention policy and completion checks before running an agent. Define what happens when information is missing: must the agent ask the user, choose a documented default, or stop? Record whether network access, credentials, external search, code execution and human approval are allowed.
Span routine, difficult and changing work
Include short tasks for calibration and progressively longer tasks with dependencies. A useful suite contains routine operations, multi-file changes, cross-application workflows, state changes that arrive during execution, and tasks where the correct move is clarification rather than guessing. Include recovery cases in which an earlier action fails and the agent must detect and repair it.
Make hidden state observable to the evaluator
Long workflows often depend on state that is not visible in the current screen: a database record, a permission, a file attribute, a browser session, a queued job or a setting changed in another application. Instrument the environment so an evaluator can check that state directly. Do not accept an action trace as proof that the intended state exists.
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Keep tasks independent and reproducible
Reset the environment to a documented snapshot for every attempt. Pin application versions, data, network fixtures, random seeds and account permissions. Prevent one attempt from leaving files, cookies or database records that make the next attempt easier. Preserve the exact task text and initial state so another evaluator can reproduce the run.
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Recruit participants who are competent in the task domain, and give them affordances as close as practical to those available to the agent. Record the start and stop rules, whether planning time counts, how interruptions are handled and how many participants perform each task. Report a distribution or robust summary rather than implying that one person’s time is universal.
Human-time estimates become uncertain for rare, highly specialized or unusually open-ended tasks. Mark those estimates and avoid fitting a precise frontier where the baseline is weak. METR reports that its current Time Horizon 1.1 suite draws from RE-Bench, HCAST and shorter novel software tasks, primarily in software engineering, machine learning and cybersecurity, and cautions that estimates above 16 hours are unreliable with that suite. That warning is about support in that suite, not a universal 16-hour limit on every evaluation.
Separate difficulty from agent speed
An agent may take longer than a person because it uses many tool calls, pauses for reasoning or encounters interface latency. Those costs should be reported separately. Human completion time supplies the difficulty axis; elapsed agent time, tokens and calls describe efficiency. Do not substitute one for the other when naming a horizon.
Run attempts and score verified outcomes
- Freeze the specification. Record benchmark version, task text, initial snapshot, model identifier, system prompt, reasoning setting, tools, context limits, step or time limit, retry policy and human-intervention rules.
- Execute repeated attempts. Run each task multiple times with independent resets. Keep the attempt count visible; one successful demonstration cannot estimate reliability.
- Apply executable checks. Use tests, database queries, file hashes, page-state assertions or other independent checks. A human judge can supplement these checks, but document the rubric and agreement rate.
- Classify the result. Mark binary completion, failure, unsafe action, timeout, clarification-required and invalid-environment cases separately. If partial credit is used, publish the component scores and weighting.
- Save traces and artifacts. Preserve screenshots, tool calls, intermediate files, logs and final state. Redact secrets while retaining enough evidence to diagnose failure.
- Estimate uncertainty. Provide confidence intervals or a hierarchical estimate for task-level success. Bootstrap by task when the number of tasks is small, because treating hundreds of attempts on one task as hundreds of independent tasks exaggerates certainty.
- Fit and report horizons. Show the fitted success curve, the 50% and 80% intersections when supported, the human-time range used, and regions with sparse data or poor fit.
Why task-level distributions matter
Two agents can have the same mean score while one fails predictably on a few high-impact tasks and the other has moderate variance everywhere. Publish a row for every task with human-time estimate, attempts, verified completions, partial score, safety incidents and failure category. Aggregate reliability should be accompanied by this distribution.
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Desktop and browser tasks are valuable because they combine planning, visual interpretation, state tracking and action. OSWorld 2.0’s authors describe failures in which agents lose constraints, miss information arriving during a task, guess instead of asking the user, skip verification and struggle when they must infer hidden state. Design tasks that make each weakness observable.
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Constraint tracking
Give the agent several simultaneous requirements, such as a file format, naming convention, date range, permission rule and destination. Change one constraint late in the workflow. Check every requirement independently; a polished final document that violates one hidden condition is not complete.
Dynamic information
Introduce a notification, updated record, changed price, newly generated file or other event while the agent is working. The evaluator should log when the information became available and whether the agent noticed and incorporated it. A workflow that succeeds only when all information is present at the start is not testing streaming interaction.
Cross-source reasoning
Require facts from multiple files, tabs or applications and make at least one source authoritative when values conflict. Verify both the selected value and the provenance. This catches agents that copy the first plausible number without reconciling sources.
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Clarification and approval
Include ambiguous requests and consequential actions. The task contract should specify when the agent must ask a question or obtain approval. Score an appropriate pause as success when proceeding would violate the policy; guessing should count as a failure, not as initiative.
Hidden-state inference
Place a relevant setting or record outside the visible page, such as a filter, permission, queued process or application preference. Provide legitimate ways to inspect it. Check that the final state, not merely the visible click sequence, satisfies the requirement.
Final verification
Require the agent to test its own result, then independently verify it. Compare the agent’s claimed completion with executable checks. Skipping a final test is a distinct failure mode because it can turn a recoverable mistake into a submitted error.
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What existing evaluations demonstrate
METR task-completion time horizons
METR’s Time Horizon 1.1 fits a logistic relationship between predicted success and human task duration. Its 50% and 80% horizons are the task durations at those fitted success levels. The suite combines RE-Bench, HCAST and shorter novel software tasks, and the page was last updated September 8, 2026. METR warns that values above 16 hours are unreliable with the current suite.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallMETR’s 2025 analysis found that the 50%-reliability task length for frontier agents doubled approximately every seven months over the six years it examined. In that analysis, models had almost 100% success on tasks taking humans less than four minutes and under 10% success on tasks taking more than around four hours. Those are historical, condition-specific observations, not forecasts or transferable guarantees for another model, suite or date.
OSWorld and OSWorld 2.0
The original OSWorld paper introduced an interactive environment spanning Ubuntu, Windows and macOS, with 369 tasks involving real web and desktop applications, file operations and multi-application workflows. Its paper reported human completion above 72.36% and best-model success of 12.24% for the systems evaluated at that time. These are 2024 paper results, not a current leaderboard claim.
OSWorld 2.0 focuses on 108 longer computer-use workflows. Its project materials report that 69.6% of tasks take skilled users more than an hour and that median human completion is about 1.6 hours. The authors report an average of 318 tool calls for Claude Opus 4.7 with maximum thinking, compared with about 30 in OSWorld 1.0. Under a 500-step limit and the primary binary-completion measure, the best listed configuration—Claude Opus 4.8 with maximum thinking and batched tool calls—completed 20.6% of tasks and achieved a 54.8% partial score.
Those figures belong to the named models and settings. They should always be presented with model version, reasoning setting, batching, step limit and scoring rule; the 20.6% value is not a generic success rate for AI agents.
WebArena as a state-based web test
Stanford’s 2026 AI Index describes WebArena as 812 long-horizon tasks in a realistic web environment. Its evaluator checks resulting website state, including databases, page content and URLs, instead of merely comparing action traces. The report attributes a 74.3% 2026 success rate and a 78.2% human baseline to the WebArena leaderboard. Leaderboard values can change, so identify the date and benchmark version whenever you quote them.
The same 2026 AI Index chart lists OSWorld accuracy of 66.3% for Claude Opus 4.5 against a 72.35% human baseline. This differs from the original OSWorld paper because it concerns later systems and results. Benchmark name alone is not enough to reconcile the numbers.
How to compare benchmark numbers without misleading readers
| Evaluation | Primary task and outcome | Reported evidence | What the number does not mean |
|---|---|---|---|
| METR Time Horizon 1.1 | Software, machine-learning and cybersecurity tasks; fitted success probability against skilled-human duration | 50% and 80% duration thresholds; current suite estimates above 16 hours are cautioned as unreliable | Not the number of hours an agent can run autonomously |
| OSWorld (2024 paper) | 369 interactive web and desktop tasks across Ubuntu, Windows and macOS; execution-based completion | Human completion above 72.36%; best-model success 12.24% in that paper’s evaluation | Not a current score for every model or version |
| OSWorld 2.0 (2026) | 108 long-horizon computer-use workflows; binary and partial outcomes | 69.6% of tasks take skilled users over an hour; median human time about 1.6 hours; best listed 500-step configuration 20.6% binary and 54.8% partial | Binary and partial scores are not interchangeable; configuration is decisive |
| WebArena (2026 report) | 812 realistic web tasks; resulting website state is checked | 74.3% reported success versus 78.2% human baseline, attributed to the leaderboard | Not directly comparable to desktop, coding or METR duration thresholds |
When comparing rows, match domain, task distribution, starting state, outcome rule, human baseline, attempt count, model setup and date. A higher percentage on a shorter or easier task distribution does not establish a longer horizon. A partial score can indicate useful progress while still failing the user’s required end state.
Report cost, speed and safety as companion metrics
Record wall-clock time, token use, tool calls, retries, compute cost and intervention time for every attempt. These metrics reveal whether an agent reaches the same reliability by spending ten times as many calls or by relying on a human to rescue it. They do not replace verified completion: a fast, inexpensive failure remains a failure.
Evaluate safety-sensitive actions separately. Define approval boundaries for deletion, publication, financial transactions, credential changes and other consequential operations. OSWorld 2.0 reports separate safety reports for safety-sensitive execution; follow the same separation in your own study. Count unauthorized action attempts even when the final state is later repaired.
Common evaluation mistakes and how to fix them
- Calling runtime a horizon: Replace “the agent ran for six hours” with a success probability indexed to human task duration.
- Reporting one successful demo: Run repeated attempts across independent tasks and publish failures.
- Using only aggregate accuracy: Show task-level outcomes, duration bins and confidence intervals.
- Mixing benchmark versions: Put version, date, model configuration and scoring rule beside every number.
- Counting actions as completion: Check the resulting file, database, page or test state independently.
- Letting a hidden human intervene: Log every intervention, restrict it by policy and report intervention-free results separately.
- Ignoring clarification: Include cases where asking is the correct action and score guessing as a policy failure.
- Overfitting to a tiny suite: Use diverse tasks, hold out newly authored items and disclose estimates with sparse support.
- Treating partial credit as success: Publish binary completion and partial score as separate measures.
- Extrapolating beyond evidence: Do not extend a fitted curve far beyond observed human-time estimates; METR’s 16-hour caution illustrates why.
A practical reporting template
For each published result, include:
- Benchmark name, version, release date and task count.
- Domain, task-duration range and how skilled-human time was estimated.
- Initial-state snapshot, software versions, network conditions and reset procedure.
- Model identifier, prompt or harness, reasoning setting, tools, context, batching, retries and step or time limit.
- Allowed user interaction, approval boundaries and treatment of clarification.
- Number of attempts per task, binary completion rule, partial-credit rubric and executable checks.
- Task-level results, aggregate reliability, confidence intervals and fitted 50% or 80% thresholds where justified.
- Wall-clock time, tokens, calls, cost, interventions and safety incidents.
- Known coverage gaps, unstable estimates and any leaderboard date used.
This level of disclosure lets a reader decide whether the result applies to their own work instead of mistaking a benchmark score for a universal capability claim.
Frequently Asked Questions
Does a 50% time horizon mean an agent can work autonomously for that long?
No. It is the human-expert duration of tasks at which the fitted model predicts a 50% chance of verified completion in the selected suite. It measures task difficulty at a reliability threshold, not uninterrupted operating time.
Should I use binary completion or partial credit?
Use both when partial progress is useful, but keep them separate. Binary completion answers whether the requested end state was achieved; partial credit describes progress toward it and cannot be substituted for success.
How many attempts are enough?
There is no universal count. Use repeated, independently reset attempts sufficient to show variability, publish the count and confidence interval, and avoid treating many retries on one task as equivalent to many independent tasks.
Why can two benchmarks report very different percentages for the same model family?
They may use different tasks, environments, human baselines, scoring rules, step limits, prompts, reasoning settings, tool batching and dates. Percentages are comparable only after those conditions are aligned.
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