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To know whether an AI agent can finish sustained work, measure verified task success across a range of difficulties—not how long its process runs. A defensible evaluation records the probability of completing each task, calibrates difficulty with skilled-human completion time, repeats attempts, and reports the exact model, tools, limits, environment and scoring rule. The result is an evidence-bound reliability estimate for a defined task suite, not a universal promise of autonomous operation.
What “long-horizon” performance means
A long-horizon task requires an agent to preserve goals and constraints through many dependent actions. It might involve editing several files, moving information between applications, responding to a state change, asking for clarification, and proving that the final result is correct. A system can perform every individual action competently yet fail the overall task because one early assumption becomes wrong or a final check is skipped.
The useful outcome is therefore probability of verified completion over a specified task distribution. For each task, define a binary outcome such as “the requested database record, files and permissions are in the required final state.” Then estimate how often the agent reaches that state under repeatable conditions. Partial progress, tool-call counts and elapsed time are useful diagnostics, but they do not substitute for the outcome check.
Human-calibrated task difficulty
One practical way to put tasks on a common difficulty scale is skilled-human completion time. 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.” The time is a difficulty index, not a timer for how long the agent can run.
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If a fitted curve predicts a 50% chance of verified success at a task that skilled humans complete in two hours, the reported 50% horizon is two human-hours for that suite and configuration. It does not mean the agent can operate autonomously for two hours, nor that every two-hour task has the same risk. A separate 80% horizon shows where reliability is higher. Both estimates depend on task selection, human-time estimates, the fitting method and the agent setup.
Why one success percentage is insufficient
An aggregate score can hide a dangerous distribution. An agent might complete easy tasks nearly every time and fail all tasks involving changing state, while another spreads moderate success more evenly. Publish task-level outcomes, the number of attempts and uncertainty alongside any mean. Include a reliability curve or success by human-time band when the suite is large enough.
Design the evaluation before running an agent
1. Specify the operational target
Write a task contract that an independent evaluator can apply without interpreting the agent’s intentions. Record:
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- Permitted tools, network access, credentials and file-system scope.
- Whether and when a person may answer questions, approve actions or repair the environment.
- The exact final state, including required values, locations, formats, permissions and side effects.
- Actions that are prohibited or require explicit approval, especially irreversible or safety-sensitive operations.
Define what happens when the correct action is to ask the user. A task should not penalize an agent for refusing to guess when a required parameter is genuinely ambiguous; score the clarification behavior against a stated policy.
2. Build a task distribution, not a demo set
Use routine, intermediate and difficult tasks, with enough examples at each level to expose a curve rather than a single point. Include workflows that require:
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- Tracking constraints across many steps and applications.
- Using information that appears or changes during execution.
- Reconciling conflicting values across files, pages or databases.
- Inferring hidden state, such as an existing filter, permission or unsaved change.
- Recognizing uncertainty and asking a targeted question.
- Performing an independent final verification.
Mix newly authored tasks with realistic starting states. If every task begins from a clean, predictable screen, the score measures scripted navigation more than robust computer use. Keep a held-out set for final comparison so prompts and task-specific fixes do not leak into the result.
3. Estimate skilled-human time carefully
Have appropriately skilled participants complete each task under conditions as close as possible to the agent’s affordances. Record the participant profile, applications, starting state, allowed references and whether the time includes reading instructions, waiting for systems or correcting mistakes. Use multiple observations where feasible and preserve uncertainty for tasks with wide variation.
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Human time is not automatically objective. A specialist may use shortcuts unavailable to the agent, while a novice baseline can make an agent look stronger than it is. State how the estimate was derived and avoid presenting a precise hour value when the underlying observation is uncertain.
4. Make completion executable where possible
Prefer independent checks: query a database, compare file hashes and contents, inspect a URL and page state, run tests, or verify permissions through the application itself. A human judge may be needed for quality or safety, but define the rubric before seeing agent outputs. Preserve the original environment, logs, screenshots and final artifacts so another evaluator can distinguish verified completion from plausible-looking partial work.
Run repeated attempts and preserve the evidence
One run per task cannot estimate reliability. Repeat each task enough times to show variability, using a predeclared randomization or retry policy. Report the number of attempts, successes, failures and confidence intervals or another uncertainty measure. If a task is expensive, explain the sampling design rather than silently treating a small sample as definitive.
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Keep task-level records with:
- Task identifier, human-time estimate and difficulty band.
- Model version, system prompt, task prompt and context window.
- Tools, browser or desktop environment, permissions and network conditions.
- Reasoning setting, batching, retry behavior, step or token limit and any human intervention.
- Outcome category, partial-credit score if used, wall-clock time, tool calls, token or monetary cost.
- Failure phase and a concise diagnosis: constraint loss, stale information, wrong inference, missing clarification, execution error or skipped verification.
Do not let automatic retries turn one attempt into an unreported collection of attempts. State whether a retry restarts from the original state, resumes after a checkpoint or receives an error message.
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For a METR-style analysis, place each task at its skilled-human duration and model the probability of success as a function of that duration. METR’s Time Horizon 1.1 method fits a logistic curve; the 50% and 80% horizons are the task durations at which the fitted probability reaches those levels. A simple conceptual form is:
p(success | t) = 1 / (1 + exp(-(a + b log(t))))
The exact model, transformation and fitting choices must be reported rather than assumed. Show the observed task outcomes and the fitted curve, including confidence bands. If the suite contains few long tasks, the tail estimate can be dominated by extrapolation. METR says estimates above 16 hours are unreliable with its current suite, which draws from RE-Bench, HCAST and shorter novel software tasks, primarily in software engineering, machine learning and cybersecurity. Its methodology page was updated September 8, 2026.
Use more than one reliability threshold
A 50% horizon is useful for comparing frontier capability, but it is not an operationally safe threshold. Add an 80% horizon—or another threshold tied to the consequence of failure—and explain the decision. For a low-risk drafting workflow, 50% may identify where human review becomes necessary. For a production migration, the relevant threshold may be 95% or a near-zero failure rate, with approvals and rollback controls regardless of the fitted curve.
Do not confuse difficulty with autonomous runtime
METR’s FAQ explicitly distinguishes its “50%-time horizon” from the duration an agent can act without stopping. The value is the human-measured length of a suite task at which predicted completion probability is 50%. An agent may run longer on an easy task, stop earlier on a hard one, or spend time waiting for a tool; none of those facts changes the difficulty index.
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What long computer workflows reveal
Short benchmarks often test isolated perception, clicking or coding skills. Sustained workflows expose failures that only appear after state accumulates. OSWorld 2.0 targets dynamic environments and streaming interaction, cross-source reasoning, implicit-state inference and visual-spatial precision. Its authors report that agents can lose constraints, miss information arriving during a task, guess instead of asking the user and skip verification, especially when success depends on hidden state.
“These results show that current agents are still far from professional-level computer use: rather than stumbling on basic GUI control or coding, they lose track of constraints, miss information that arrives mid-task, guess rather than ask the user, and skip verification, struggling most when a task hinges on hidden state they must recover.” — OSWorld 2.0 authors
Constraint loss
Give the agent several simultaneous requirements—for example, preserve a naming convention, exclude a confidential field, update two applications and leave an audit note. Check every requirement independently. A final document that looks polished can still fail because one exclusion or permission rule was forgotten.
Dynamic information
Insert a legitimate state change during the workflow: a new message, changed record, updated price or altered file. The evaluator should check whether the agent notices the change and adapts rather than continuing from a stale plan. Record when the information became available and whether the agent had a fair opportunity to observe it.
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Cross-source reasoning
Require reconciliation across sources with different schemas or terminology. Verify the final mapping against each source, not only the agent’s explanation. This distinguishes genuine state updates from copying a value that happens to look plausible.
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Clarification and refusal
Include cases where two outcomes are both plausible but only the user can choose. Score a concise, well-timed question more highly than an unapproved guess. Also test whether the agent refuses or escalates a prohibited action instead of silently substituting a safer-looking one that does not meet the contract.
Final verification
Make verification observable. Require a query, test, comparison or application-state check that can fail. An agent that reports “done” without running the check should not receive full credit even if the artifact happens to be correct.
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| Evaluation | What it measures | Reported evidence | How to qualify the number |
|---|---|---|---|
| METR Time Horizon 1.1 | Human-expert task duration at a selected predicted reliability, using a fitted success curve. | 50% and 80% intersections; current suite from RE-Bench, HCAST and shorter novel software tasks. | Task difficulty and reliability estimate, not autonomous runtime. METR cautions that estimates above 16 hours are unreliable with its current suite. |
| OSWorld (original paper, 2024) | Interactive multimodal tasks across Ubuntu, Windows and macOS, including real web and desktop applications and file operations. | 369 tasks; reported human completion above 72.36% and best-model success of 12.24% in that paper. | Historical paper results, not a current leaderboard. Environment, model and scoring setup belong with the figures. |
| OSWorld 2.0 (2026) | Long-horizon computer-use workflows in dynamic desktop environments. | 108 workflows; 69.6% take skilled users more than an hour; median human duration about 1.6 hours. Claude Opus 4.7 with maximum thinking averaged 318 tool calls, versus about 30 in OSWorld 1.0. The best listed 500-step configuration—Claude Opus 4.8 with maximum thinking and batched tool calls—reached 20.6% binary completion and 54.8% partial score. | Configuration-specific figures. State model, reasoning setting, batching, step limit and whether the result is binary or partial. Safety-sensitive actions are reported separately. |
| WebArena | Long-horizon website workflows evaluated by resulting website state. | Stanford’s 2026 AI Index attributes a 74.3% success rate and 78.2% human baseline to the WebArena leaderboard. | Leaderboard figures can change. The evaluator checks databases, page content and URLs rather than merely matching action traces. |
| OSWorld result in Stanford’s 2026 AI Index | Later-system desktop-task accuracy shown in the report’s chart. | 66.3% for Claude Opus 4.5 against a 72.35% human baseline. | Different from the original OSWorld paper because it concerns later systems and results; benchmark version and setup are essential. |
These scores are not interchangeable. They use different domains, task distributions, environments, human baselines, outcome rules and agent configurations. A WebArena website-state score cannot be converted into a METR human-time horizon, and an OSWorld partial score cannot be treated as binary completion.
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Interpret the major published trends cautiously
METR’s 2025 analysis estimated that the 50%-reliability task length for frontier agents doubled approximately every seven months over the six years it examined. In that analysis, models achieved almost 100% success on tasks taking humans less than four minutes and under 10% on tasks taking more than around four hours. Those are historical, suite-specific observations—not forecasts and not transferable performance guarantees for a different model, date or task mix.
Likewise, OSWorld 2.0’s increase in average tool calls—from about 30 in OSWorld 1.0 to 318 for Claude Opus 4.7 with maximum thinking—shows that longer workflows can require substantially more interaction. It does not by itself show better or worse capability: tool-call efficiency and verified completion must be reported together.
Efficiency, cost and safety are companion metrics
Record wall-clock duration, tool calls, tokens and monetary cost per successful completion. Break out failed-attempt cost and the cost of human review. These measures help choose a deployment architecture, but a cheap run that leaves the wrong record in a live system is not a successful run.
Evaluate consequential actions separately. Define approval boundaries for deletion, money movement, external communications, permission changes and production deployment. Use sandboxed data where possible, require explicit confirmation before irreversible steps and test whether the agent pauses at the boundary. OSWorld 2.0 reports separate safety reports for safety-sensitive execution; combine capability and safety results only after both have been measured.
A reproducible reporting template
- Scope: Name the benchmark version, domain, task count, starting-state policy and date.
- Agent: Identify the exact model, prompt, reasoning setting, context, tools, batching, retries and step or token limits.
- Human baseline: Describe participant expertise, affordances, timing procedure and uncertainty.
- Outcome: Define binary completion, partial credit, executable checks and human-judged dimensions.
- Sampling: State attempts per task, randomization, exclusions and confidence-interval method.
- Results: Publish task-level outcomes, aggregate reliability, difficulty bands and any fitted 50% or 80% horizons.
- Failures: Include traces and categorize the first consequential error, not only the final symptom.
- Operations: Report time, cost, tool calls, intervention rate and safety-boundary behavior.
- Limits: Identify unsupported tail regions, leaderboard volatility and conditions that prevent generalization.
Common evaluation mistakes
- Calling a runtime limit a time horizon: Human-calibrated horizons index task difficulty and predicted success.
- Reporting only an average: Aggregate scores conceal which tasks fail and how variable outcomes are.
- Mixing binary and partial scores: Label the scoring rule in every table and chart.
- Changing the harness silently: Tool batching, retries, reasoning effort and step limits can materially change results.
- Using an easy human baseline: A baseline with different tools or expertise invalidates direct comparison.
- Extrapolating beyond the data: A fitted curve is not evidence where the suite has few or no tasks.
- Counting plausible output as completion: Verify the resulting state independently.
- Ignoring intervention policy: A system that succeeds only with frequent unreported human repair is not equivalent to an autonomous configuration.
What a defensible conclusion looks like
Conclude in the same terms as the test: “Under this model, harness, environment and approval policy, the agent completed X of Y attempts; reliability declined in tasks above the measured human-time band; failures concentrated in dynamic updates and final verification.” Do not convert that statement into “the agent can work for X hours” or “the agent is ready for any workflow.” A useful evaluation tells a deployment team which tasks are safe to automate, where a person must review, and what additional controls are required.
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