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This format separates a model producing an undesirable answer from a software defect in orchestration, tools, permissions, state, truncation or deployment. It also gives maintainers enough context to reproduce the failure without guessing.
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What a reproducible AI bug report contains
A developer should be able to start from a clean environment, run your commands, observe the same failure and know what success means. Microsoft’s VCSamples repro guide describes a good repro as “minimal, being as small and as simple as possible while still demonstrating the issue.” That principle applies to an AI coding assistant, an inference API, an agent workflow or an evaluation harness.
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- What exactly failed? Name the product, model, build, repository, commit and runtime.
- What should have happened? State one testable expectation or invariant.
- What happened instead? Preserve the exact response, error, traceback, request and relevant logs.
- Can someone else trigger it? Supply a self-contained fixture and numbered commands from a clean setup.
- What must remain true after the fix? Propose a safe verification command and regression test.
Do not lead with a theory such as “the model is confused” or “the latest commit broke it.” A plausible explanation is useful as a hypothesis, but it is not evidence of cause. The official incident workflow explicitly treats a recent code change, a successful test on another release or a convincing explanation as insufficient proof by itself.
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Start with scope and an exact identity record
Record identity before describing symptoms. AI systems can change behavior when a model alias moves, a tool build changes, a dependency is resolved differently or a deployment is rolled back. Include the UTC time window so maintainers can correlate your run with deployment and service logs.
| Field | What to record | Why it matters |
|---|---|---|
| AI product and component | Product name, feature or endpoint, agent/tool subsystem, and the failing operation | Routes the report to the team that owns the failing path rather than to a general queue. |
| Model identity | Exact model name or deployment ID, model revision if exposed, seed, temperature and other sampling settings | Distinguishes a deterministic software defect from a change in model or sampling behavior. |
| Tool build | CLI, SDK, extension or service version and build number | Lets a maintainer install the same client and check regressions. |
| Code state | Repository URL, branch, commit SHA, release or deployment identifier | Prevents a fix attempt against the wrong source revision. |
| Runtime | Operating system and version, architecture, language/runtime version, compiler version where relevant | Native libraries, shells and runtimes can alter behavior. |
| Dependencies | Lockfile name and hash, package-manager version, and any system packages | Pinning resolves “works on my machine” differences. |
| Configuration | Feature flags, endpoint region, tool permissions, environment variables by name, timeouts and retry policy | Configuration often determines whether an AI request is retried, truncated or allowed to mutate state. |
| Time window | Start and end timestamps in UTC, plus timezone if local times appear in logs | Operators can find matching traces, deploys and incidents. |
Never paste secret values into this identity block. List variable names and describe whether each is set, or provide a sanitized fixture that supplies non-sensitive values.
Separate expected behavior from observed behavior
Write one testable expectation
Put the expected result first and make it falsifiable. State the invariant that should hold, not a broad goal such as “the assistant should be smarter.” Examples include:
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- For this input schema, the response must contain a valid JSON object with the required keys and no trailing prose.
- When a file is unchanged, applying the generated patch must leave its checksum unchanged.
Preserve the actual result exactly
Copy the complete error message, traceback, response body and relevant tool-call transcript. Include whether the process crashed, hung, timed out, returned a wrong answer, mutated state or silently dropped data. A screenshot is supplementary; text is searchable and executable, so use a screenshot only when layout, rendering or a visual state is itself the defect.
Record frequency and timing precisely: for example, “fails 10 of 10 runs with temperature 0,” “fails 3 of 20 runs with temperature 0.7,” or “first failed at 14:32:18 UTC after a 42-second timeout.” If output is truncated, show the truncation marker and the configured context or output limit.
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Classify model variability before calling it a software bug
AI output can vary legitimately. A report is stronger when it demonstrates whether the failing path is deterministic.
Repeat under fixed settings
Run the same fixture several times with the model revision, seed, temperature, top-p and tool settings recorded. Keep the prompt, retrieved documents, tool responses and conversation state identical. Report the number of failures and successes, not just one surprising sample.
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Distinguish answer quality from product behavior
An incorrect explanation or weak code suggestion may be a model-quality issue. A product defect is more specific: an incorrect tool call, unauthorized state mutation, permission bypass, malformed serialization, lost conversation state, truncation, timeout handling error or regression in orchestration. Show the boundary between generated text and the product action. Preserve the model message, the tool request generated from it, the tool response and the final state.
Use a safe fixture
Do not reproduce against a live customer database or production account. Build a local or isolated fixture that exercises the same branch. Record which setup steps were verified and which external services are deliberately mocked. If the failure disappears with a mock, say so; that result narrows the cause instead of weakening the report.
Build the smallest self-contained reproducer
A minimal reproducible example (MRE) is not merely a short prompt. It contains everything needed to run the failing path: imports, setup, data acquisition, model and tool settings, fixture data and exact commands. scikit-learn recommends reading a project’s issue template and warns that English-only reproduction instructions are ambiguous; code that includes imports and data retrieval can be copied and run without interpretation.
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Reduce in a controlled order
- Copy the failing request, prompt or test and run it unchanged.
- Remove unrelated files, libraries, tools and conversation turns one at a time.
- Replace customer or production data with the smallest synthetic fixture that still fails.
- Pin the model/tool version, dependency lock and configuration.
- Move network calls behind a recorded response or local mock where possible.
- Verify the reduced case in a fresh environment, then record the command and result.
Keep a change log while minimizing. If removing a component makes the failure disappear, restore it and identify it as a required precondition. “It only happens in our full application” is not a reproducer until the report identifies which part of that application is necessary.
Include exact commands and checkpoints
Commands should be copy-pasteable and should fail at a named checkpoint. Include shell, working directory and required environment setup. A good sequence looks like this:
- Create an empty virtual environment with the stated runtime version.
- Install the exact locked dependencies.
- Write or fetch the sanitized fixture.
- Set non-secret configuration values and model settings.
- Run the single reproduction command.
- Compare the output with the expected checkpoint and save the exit code.
State whether each command was actually run on a clean machine. Do not present an unverified sequence as proof.
Capture evidence developers can inspect
Attach evidence in layers so a maintainer can move from symptom to cause without requesting the same context again.
- Request and prompt fixture: the exact messages, tool definitions, retrieved context, system instructions and serialized request, with secrets and personal data removed.
- Response fixture: the raw model response, tool calls, response headers or metadata that affect behavior, and the final rendered output.
- Logs: complete relevant lines before, during and after the failure, including timestamps, severity, component and correlation or trace IDs.
- Source location: file paths, line numbers and the commit used to produce the run.
- Baseline: the last known good version and the first failing version, if both can be tested with the same fixture.
- State evidence: checksums, database rows in a disposable fixture, generated files or permission state showing what changed.
Keep the original ordering and do not paste only the final line of a traceback. If logs are huge, provide a sanitized attachment or excerpt with clear boundaries and explain how it was selected. Correlation IDs should remain intact unless they themselves contain sensitive information.
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Redact secrets, tokens and personal data without destroying the repro
Sanitize before posting to an issue tracker or sharing with a vendor. Microsoft Security’s SARIF AI guidance recommends redacting credentials, tokens and personally identifiable information in structured reproduction evidence.
- Replace API keys, cookies, authorization headers, private certificates and session tokens with stable placeholders such as
REDACTED_TOKEN_1. - Replace names, email addresses, account numbers, prompts containing customer content and retrieved documents with synthetic values of the same shape and length where possible.
- Remove production hostnames and internal paths when they are not required; retain a stable component name so logs can still be correlated.
- Check generated archives, screenshots, shell history and environment dumps for secrets before upload.
- Explain every redaction that could affect behavior, and provide a safe substitute fixture.
Never ask maintainers to run a command that contacts a live service, deletes data or executes untrusted generated code. Prefer a local fixture, a dry-run flag and a command that prints intended mutations before applying them.
Describe impact, boundaries and version history
Impact lets maintainers prioritize and test the right boundary. State who or what is affected, how often, and whether data or permissions are at risk.
- Users, tenants, repositories, jobs or environments affected.
- Severity: data loss, unauthorized action, denial of service, incorrect output, degraded latency or cosmetic error.
- Whether sensitive data was exposed, persisted or sent to an external model.
- Workarounds that are safe, verified and their limitations.
- First version known to fail and versions tested that do not fail.
- Whether the issue survives a clean installation and whether it depends on a particular region, operating system or runtime.
Do not infer causality from chronology. If the problem began after a deployment, report the first failing release and the comparison result, then label the suspected change as a hypothesis until a reproducer or trace proves it.
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Name the suspected invariant
Translate the symptom into a property the implementation must preserve. For example: “A tool marked read-only must never receive a write-capable execution request,” or “A retry after a 429 must reuse the same idempotency key.” Phrase it as a proposal and ask maintainers to confirm the root cause.
Propose a regression test
Give the exact fixture, expected assertion and safe command that should remain after the fix. Include both the failing case and a nearby success case when they distinguish the boundary. If the model is nondeterministic, assert the product invariant—such as no unauthorized tool call or valid schema—rather than one exact sentence.
Identify compatibility constraints
Note supported runtimes, API versions, latency budgets, token limits and behavior that must not change. A fix that prevents the bug by disabling all tool calls may pass the narrow reproducer while breaking the feature; state the required capability explicitly.
Copy-paste report template
Title: [AI tool/model] [verb] [unexpected result] in [component]
Scope and identity
- Product/feature:
- Model and revision:
- Tool or SDK version/build:
- Repository and commit SHA:
- Release or deployment ID:
- Operating system and architecture:
- Runtime/compiler:
- Dependency lock and package-manager version:
- Relevant configuration and feature flags:
- UTC reproduction window:
Expected behavior
- Invariant:
- Expected output or state:
Actual behavior
- Exact output, response body or traceback:
- Crash, hang, timeout or state mutation:
- Frequency and deterministic/non-deterministic result:
Minimal reproducer
- Preconditions:
- Sanitized fixture or request:
- Exact setup commands:
- Exact reproduction command:
- Expected checkpoint and observed checkpoint:
Evidence
- Logs and correlation/trace IDs:
- Prompt, retrieved context and tool-call fixtures:
- Relevant file paths and line numbers:
- Last known good version:
- First known failing version:
Impact and boundaries
- Affected users or systems:
- Severity and data exposure:
- Safe workaround:
- Versions, platforms or regions not affected:
Fix handoff
- Suspected violated invariant (hypothesis):
- Proposed regression test:
- Compatibility constraints:
- Safe verification command:Replace each field with concrete values. Keep template syntax inside the code block when sharing it; the submitted report itself should contain real identities, commands and outputs.
Common report failures and how to repair them
| Weak report pattern | Why it blocks a fix | Repair |
|---|---|---|
| “The AI is broken.” | No component, expectation or observable result is defined. | Name the failing operation, state one invariant and paste the exact output. |
| One long production repository | Maintainers cannot tell which dependency or file is necessary. | Reduce to a self-contained fixture and list every required precondition. |
| Prompt-only reproduction | Model settings, retrieved context and tool responses may differ. | Include serialized inputs, settings, fixtures and tool-call evidence. |
| Screenshot instead of text | Errors cannot be searched, copied or compared. | Paste the complete text; attach the screenshot only for visual defects. |
| “It happens sometimes” | Intermittency cannot be measured or triaged. | Run repeated trials and report counts, seed, sampling settings and timing. |
| Unredacted logs | Credentials or personal data create a second incident. | Sanitize, use stable placeholders and explain behavior-sensitive redactions. |
| Unsafe reproduction command | Running it could mutate production or expose data. | Use a disposable fixture, dry run, mock service and explicit safety limits. |
| Cause stated as fact | Chronology or a plausible theory does not establish causation. | Label the theory as a hypothesis and provide evidence that can confirm or reject it. |
Use the report in an incident workflow
For a production incident, put the report into the incident record as an UTC-windowed summary: expected and observed behavior, impact, release identity, correlated logs and trace IDs, safe reproduction fixtures, repository guidance and the test baseline. Keep incident response separate from root-cause speculation. A successful test on a different release does not prove that the failing release is safe; it only supplies a comparison point.
Microsoft Security reported 155 internally validated reports across 23 open-source projects over three months in 2026, with 93 reports across 14 projects or project families having documented maintainer acknowledgement or acceptance at publication time. Those figures illustrate why validation and acceptance depend on evidence maintainers can act on, not merely on the number of reports submitted.
A reproducible trigger is valuable beyond the immediate fix. Microsoft Security describes it this way: “A reproducible trigger strengthens the report, guides the fix, and can seed a regression test.” Treat the fixture and assertion as durable engineering assets: keep them sanitized, versioned and runnable after the issue is closed.
Quick Recap
Final pre-submission checklist
- The title names the AI tool or model, unexpected result and component.
- Product, model revision, build, repository commit, runtime, lockfile, configuration and UTC window are recorded.
- Expected behavior and invariant appear before the observed behavior.
- The complete error, traceback, response, tool call and relevant logs are included.
- The smallest self-contained fixture contains imports, setup, data and exact model/tool settings.
- Numbered commands run from a clean environment and include checkpoints.
- Repeated runs establish frequency and whether sampling causes variability.
- Secrets, tokens, credentials, customer data and PII are removed.
- Impact, affected boundaries, first failing version, known-good baseline and safe workaround are stated.
- The report proposes a safe verification command and regression test without presenting an unproven cause as fact.
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