Before connecting an inference service to tools that can change files or other state, test it on a small, representative evaluation slice with write-capable tools and credentials unavailable. Give each case a clear expected answer or annotation, choose graders suited to the behavior being checked, and verify that the runtime—not just a configuration label—blocks writes.
What a read-only evaluation slice should contain
An evaluation slice is a focused set of inputs that represents the task you plan to run, paired with expectations you can use to judge the outputs. Those expectations may be exact reference values, acceptable-answer criteria, or human annotations for behavior that cannot be reduced to a single answer.
Start with representative cases, then add edge cases and blind spots as you discover them. OpenAI’s dataset guide describes datasets as dynamic and supports columns for prompt inputs, grader inputs, and ground-truth values. When a judgment depends on specialist knowledge or nuanced style, use a subject-matter expert to annotate examples; expert annotation is especially useful when the dataset author is not an expert in the domain.
- Include cases that reflect real inputs, not only easy or ideal examples.
- Record the expected behavior for each case clearly enough that a grader or reviewer can apply it.
- Add newly identified edge cases rather than treating the first dataset as complete.
- Inspect dataset-loading paths and download behavior before deployment; a task may fetch data or require tokens.
Match each grader to the behavior being tested
A grader should measure the criterion you care about, not merely produce a convenient score. OpenAI’s evaluation documentation describes evaluations as tests of model outputs against specified style and content criteria, and its guidance discusses annotations, graders, and diagnosing prompt shortcomings.
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| What you need to assess | Suitable approach | Important limitation |
|---|---|---|
| Exact required value or wording | Exact string or deterministic rule check | Use only when identity really matters; harmless wording variation will fail. |
| Answer close in meaning to a reference | Text-similarity grader | Similarity does not by itself establish factual correctness. |
| Subjective quality on a scale | Score model grader | Define the scale and align it with annotated examples. |
| Membership in a category, such as concise or verbose | Label model grader | Specify category boundaries and inspect disagreements. |
| Precisely expressible rule | Custom deterministic code | Code execution adds risk to the evaluation environment. |
Use annotations to encode desired behavior, including specific cases and subjective dimensions. They can help diagnose prompt problems and align graders; weak annotations provide weak evidence about the model. Review disagreements between graders and per-case failures before treating an aggregate score as evidence of model quality.
Remove write authority from the evaluation run
“Read-only” is a property of the actual runtime and tools, not a promise written in a prompt or permission file. Harness Protocol states that its permissions section documents intent but does not grant permissions. The tool or runtime that controls access must enforce the boundary.
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Begin with only the authority the experiment requires. If the slice needs inference and read access to evaluation data, do not expose mutation APIs, write tools, or credentials capable of changing state. Treat these as separate surfaces and constrain each independently:
- Tools: expose only the tools the evaluation needs; omit write-capable tools.
- Filesystem: restrict accessible paths and ensure the process cannot modify evaluation inputs or other protected files.
- Network: limit reachable destinations, including the model endpoint, to what the run requires.
- Credentials: withhold secrets that can modify state; do not assume a read-only tool makes a powerful credential safe.
- Endpoint configuration: control which model endpoint can be selected and who can change that selection.
AWS AgentCore recommends application-layer validation when callers are not fully trusted, including allowlisting model configuration fields and scoping network access. The principle applies to an evaluation harness too: do not let untrusted inputs freely choose a more privileged endpoint or broaden connectivity.
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Verify that the boundary blocks writes in practice
Test the enforcement point, not just the displayed setting. Attempt a controlled write to a disposable target and confirm that the actual tool or resource rejects it. Check alternate routes as well: a restriction on one interface does not necessarily stop another process, shell, or custom tool from changing a local copy.
Anthropic’s managed-agent documentation explains that read-only memory stores are protected from upload 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 and any custom tools that can write to that filesystem.
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When the evaluation runs generated code
Code-execution evaluations need stronger isolation because the model output may be executable. The reviewed EvalHub integration guidance for LM Evaluation Harness says HumanEval, HumanEval Instruct, and MBPP execute generated Python inside the evaluation Job container rather than a separate code-execution sandbox. It warns against enabling this behavior on an untrusted shared host. Use an isolated environment and review task code, dataset paths, and downloads before running such benchmarks.
Review failures before expanding permissions
- Run the slice with write-capable tools and mutation credentials absent.
- Inspect failed cases and grader disagreements; correct dataset or grader problems before interpreting the score.
- Confirm the runtime blocks attempted writes at the relevant tool or resource boundary.
- Only if a concrete use case requires mutation, grant the narrowest operation to the specific destination that needs it.
- Keep the read-only evaluation run auditable as a distinct phase from any later write-enabled run.
What “free inference” means in OpenAI’s external-model evals
Free inference is not a general guarantee across providers. 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, an HTTPS endpoint compatible with chat completions, and an API key; endpoint settings are per project.
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For this OpenAI feature, the documentation lists these monthly covered inference limits by organization usage tier:
| Organization usage tier | Monthly covered inference limit |
|---|---|
| Tier 1 | $5 |
| Tier 2 | $25 |
| Tier 3 | $50 |
| Tier 4 | $100 |
| Tier 5 | $200 |
These are the limits documented for OpenAI’s feature, not a promise that other services are free or that usage beyond the covered amount has no cost. The documentation names Google, Anthropic hosted on AWS Bedrock, Together, and Fireworks as available providers through that offering. It 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.
OpenAI currently states that existing Evals content becomes read-only for existing users on October 31, 2026, and that the platform is scheduled to shut down on November 30, 2026. These dates concern OpenAI’s platform specifically; check its current documentation before relying on them.
Compare evaluation setups on the risks that matter
No single setup is established as universally best. Before choosing a runtime or inference provider, compare the capabilities and boundaries that affect your particular evaluation:
Quick Recap
- Whether the setup supports the tool calls your task requires.
- Where prompts, test data, and outputs are processed and which provider terms apply.
- Whether filesystem, tool, and network restrictions are enforced by the runtime.
- Whether generated code runs, and what isolation contains it.
- Which graders and human-annotation workflows are available.
- Current eligibility, usage limits, costs, and platform lifecycle.
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