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What a read-only evaluation slice should establish
An evaluation slice is a deliberately small set of representative inputs used to check whether a model behaves as required. Each case needs a clear expectation: a reference answer, a label, an annotation, or another criterion that makes it possible to judge the output. Include ordinary cases as well as edge cases and known blind spots; add cases when failures reveal gaps in the dataset.
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OpenAI describes evaluations as tests of model outputs against specified style and content criteria. Its dataset guide describes using dataset columns in prompts and graders, including ground-truth values, and recommends treating the dataset as something that can grow as new edge cases are found. For specialized or nuanced tasks, expert annotations can clarify what acceptable behavior means and help distinguish a model failure from a poorly specified test.
Match the grader to the requirement
A grader should measure the criterion you actually care about. A strict comparison can be useful when exact identity is required, but it can reject valid answers that use different wording. Conversely, a similarity score may accept a response that is close in phrasing but wrong on a critical detail.
#1 Best Overall
| What you need to check | Suitable approach | Watch for |
|---|---|---|
| Exact required text or value | Exact string or structured-value comparison | Use only when wording or value must match exactly. |
| Similarity to a reference where wording may vary | Text-similarity grader | Similarity is not proof that the answer is factually or operationally correct. |
| Subjective quality on a scale | Score model grader | Define the scoring dimensions and calibrate against human judgments. |
| Category such as concise or verbose | Label model grader | Specify categories clearly and inspect ambiguous cases. |
| A precisely expressible rule | Deterministic code | Evaluation code can execute code or invoke tools; run it with appropriate isolation. |
| Nuanced domain behavior or style | Human or subject-matter-expert annotations | Unclear annotations can make model and grader disagreements hard to interpret. |
OpenAI’s evaluation documentation describes annotations as a way to encode desired behavior, including specific cases and subjective dimensions, and to diagnose prompt shortcomings and align graders. Review disagreements between a model grader and human annotations rather than treating a single score as self-explanatory.
Keep the evaluation’s authority narrow
Give the run only the capabilities its task requires. If it needs to read a dataset and request inference, do not also expose file-writing tools, mutation APIs, or credentials that can alter state. Restrict filesystem paths, network destinations, and model endpoint configuration separately: removing one permission does not automatically remove the others.
Rank #2
The Harness Protocol makes the distinction explicit: “The permissions section documents intent — it does not grant permissions.” A read-only declaration is not enforcement. The tool or runtime that reaches the resource must actually reject writes. AWS AgentCore’s guidance similarly recommends application-layer validation for callers who are not fully trusted, including allowlisting model-configuration fields and scoping network access.
The Tool Desk
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Test the actual resource or tool, not just the displayed setting. Make a controlled write attempt in a disposable environment and confirm that the operation is denied. Check each route capable of changing state: the agent’s tools, shell access, custom integrations, APIs, and any local copy of a dataset or memory store.
Rank #3
Read-only protection may be scoped to particular interfaces. Anthropic’s managed-agent memory documentation says read-only memory stores are protected from uploads and writes through worker write/edit tools and memory-store endpoints, while shell commands and custom tools can still modify a local copy. If local immutability matters, remove shell access and any custom tool that can write to that filesystem; do not infer that a protected service endpoint makes every local copy immutable.
Isolate evaluation code and inspect data loading
Some evaluations run generated code. In the reviewed EvalHub integration guidance for LM Evaluation Harness, HumanEval, HumanEval Instruct, and MBPP execute generated Python code inside the evaluation Job container rather than a separate code-execution sandbox. The guidance warns against enabling this behavior on an untrusted shared host. A container is not automatically equivalent to a separately hardened sandbox, so assess what the job can reach and isolate it accordingly.
Inspect dataset paths, task names, and download behavior before deployment. A task may fetch data or require tokens, creating network or credential access beyond what a simple inference-and-read test needs. Keep those paths unavailable unless the evaluation genuinely depends on them.
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Check external inference terms, eligibility, and lifecycle
“Free inference” is not a general property of external models. OpenAI’s external-model evaluation documentation describes a specific Platform feature: third-party model access requires organization usage tier 1 or higher, administrator enablement, and acceptance of a usage disclaimer. Custom endpoints require administrator enablement, a chat-completions-compatible HTTPS endpoint, and an API key; endpoint settings are per project. The guide lists Google, Anthropic hosted on AWS Bedrock, Together, and Fireworks as available providers through that offering.
For that OpenAI Platform feature, the documented monthly covered inference limits are:
| Organization usage tier | Documented monthly covered inference limit |
|---|---|
| Tier 1 | $5 |
| Tier 2 | $25 |
| Tier 3 | $50 |
| Tier 4 | $100 |
| Tier 5 | $200 |
These are limits documented for the OpenAI Platform feature, not a promise that inference from other services is free. OpenAI also says external-model calls send data to third parties and are subject to different terms and weaker safety guarantees than calls to OpenAI models; tool calls are not currently supported for external-model evals. Confirm the current terms and eligibility before sending prompts, test data, or outputs to a provider.
The same OpenAI documentation currently says existing Evals content will become read-only for existing users on October 31, 2026, and that the platform is scheduled to shut down on November 30, 2026. These are dates for that OpenAI platform, not general dates for evaluation software; check the documentation for changes before relying on the service.
Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsExpand permissions only after reviewing failures
- Run the slice with the narrowest permissions that support the test.
- Review failures case by case, including disagreement between graders and human annotations.
- Fix unclear expectations, dataset gaps, or grader problems before interpreting a score as evidence of model quality.
- Grant write access only for a concrete operation that needs it, and scope that access to the specific tool, resource, or destination.
- Keep the read-only evaluation run distinct and auditable from any later write-enabled phase.
Provider and runtime capabilities differ, and the cited documentation does not establish one setup as best for every evaluation. Compare whether tool calls are supported, where inputs and outputs are processed, how filesystem, network, and tool permissions are enforced, how generated code is contained, what graders and annotation workflows are available, and what eligibility and lifecycle conditions apply.
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