fix-agent-issue

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Fix Agent Issue

修复Agent问题

The user wants to change an AI agent's behavior — either because something is wrong (pointing at a trace, pasting an answer they didn't like, describing a failure mode) or because they're introducing a new requirement, business rule, or policy ("lead with our premium line," "never reveal internal docs," "always confirm the order ID first"). A new business rule is not a bug, but it earns the same discipline: it's a behavior change that can silently break other behaviors, so it goes through the loop too. They want the change made with confidence that it sticks and doesn't regress anything else. Drive a disciplined improvement loop, never a one-shot patch.

用户希望更改AI Agent的行为——要么是因为某些功能异常（指向追踪记录、粘贴不满意的回复、描述故障模式），要么是因为要引入新需求、业务规则或策略（如“优先推荐我们的高端产品线”“绝不泄露内部文档”“始终先确认订单ID”）。新业务规则并非漏洞，但同样需要遵循这套规范：它属于行为变更，可能会悄然破坏其他行为，因此也需要经过完整循环。用户希望变更能够稳定生效，且不会导致其他功能退化。请严格遵循改进循环，绝不采用一次性补丁。

The non-negotiable loop

不可简化的循环

EXPLORE → PLAN → IMPLEMENT → VERIFY, in that order. Don't skip phases. Don't edit anything in EXPLORE. Don't write code in PLAN. Don't change tests in VERIFY.

If the user pushes for a quick fix, push back once: "Let me write a test first so we know the fix actually works." Most of the value of this skill is the discipline.

Two kinds of request trigger this skill, and both get the same test-first discipline:

Fixing a failure — the user shares a bad trace or describes a failure mode ("the agent is hallucinating," "this answer is wrong," "my agent keeps doing X — fix it").
Introducing a new behavior / business rule / policy — a new requirement that changes how the agent should respond ("lead with X," "new business rule: the agent should X," "always/never do X," "change the default"). Not a bug, but still a behavior change that can silently break other behaviors.

探索→规划→实现→验证，必须按此顺序执行，不得跳过任何阶段。探索阶段不得编辑任何内容，规划阶段不得编写代码，验证阶段不得修改测试。

如果用户要求快速修复，请拒绝一次：“让我先编写测试，这样我们就能确认修复是否真的有效。”该流程的核心价值就在于其规范性。

两种类型的请求会触发此流程，且都需要遵循“测试优先”的规范：

修复故障——用户分享异常追踪记录或描述故障模式（如“Agent在幻觉生成内容”“这个答案是错误的”“我的Agent一直执行X——修复它”）。
引入新行为/业务规则/策略——变更Agent响应逻辑的新需求（如“优先推荐X”“新业务规则：Agent应该执行X”“始终/绝不执行X”“更改默认行为”）。这并非漏洞，但属于可能悄然破坏其他行为的变更。

Phase 1: EXPLORE — understand what the trace shows

阶段1：探索——理解追踪记录所呈现的内容

Goal: a written diagnosis with three answers — what the agent did, what it should have done, why it failed.

Do NOT edit any agent code in this phase.

目标：撰写包含三个核心结论的诊断报告——Agent实际执行了什么、它应该执行什么、故障原因是什么。

本阶段不得编辑任何Agent代码。

Read the trace, the full trace

完整阅读追踪记录

Fetch the trace and inspect it span by span. The reference for trace anatomy lives in the

analyzing-mlflow-trace

skill — use it.

bash

mlflow traces get --trace-id <ID> > /tmp/trace.json

For each non-trivial span, surface:

LLM spans — system prompt, full message history, the model's emitted text and tool calls.
Tool spans — what tools were called, with what arguments, what they returned.
Tools that weren't called but should have been. This is usually where the bug lives. If the agent answered a how-to question from memory when it could have retrieved docs, the issue is upstream of the prompt.
Retrieval spans — what was searched, what was returned, what was filtered.

获取追踪记录并逐段检查。追踪记录的结构参考

analyzing-mlflow-trace

技能——请使用该技能。

bash

mlflow traces get --trace-id <ID> > /tmp/trace.json

对于每个非 trivial 的追踪段，重点关注：

LLM追踪段——系统提示、完整消息历史、模型生成的文本和工具调用。
工具追踪段——调用了哪些工具、使用了什么参数、工具返回了什么内容。
应该调用但未调用的工具——这通常是漏洞所在。例如，Agent本应检索文档却凭记忆回答了操作类问题，那么问题根源在提示上游。
检索追踪段——搜索了什么内容、返回了什么结果、过滤了什么内容。

Capture the user's verbal feedback on the trace

记录用户对追踪记录的口头反馈

Whatever the user just told you about why this answer is bad — log it on the trace as a HUMAN assessment so it persists for future iteration and other reviewers can see it.

python

import mlflow
mlflow.log_feedback(
    trace_id="<the trace id>",
    name="user_review",
    value=False,                            # or a numeric score / category
    rationale="<paste the user's complaint verbatim>",
    source=mlflow.entities.AssessmentSource(source_type="HUMAN", source_id="<user>"),
)

If the trace already has notes (

mlflow.notes

assessment), include them in your diagnosis too.

无论用户刚刚告诉你这个答案哪里不好——将其作为人工评估记录在追踪记录中，以便后续迭代留存，其他审阅者也能看到。

python

import mlflow
mlflow.log_feedback(
    trace_id="<the trace id>",
    name="user_review",
    value=False,                            # 或数字评分/分类
    rationale="<直接粘贴用户的投诉内容>",
    source=mlflow.entities.AssessmentSource(source_type="HUMAN", source_id="<user>"),
)

如果追踪记录已有备注（

mlflow.notes

评估），请将其也纳入诊断报告。

Write the diagnosis

撰写诊断报告

State plainly:

What the agent did — quote the actual response, list the actual tool calls.
What it should have done — restate the user's expectation in concrete terms.
Root-cause hypothesis — the most important sentence in the whole loop. Pick from:
- Missing capability / tool (the agent literally cannot do the right thing)
- Missing data source / retrieval (the agent doesn't have the facts)
- Wrong routing / planning (the agent has the tool but didn't call it)
- Wrong instruction (the prompt told it to do something else)
- Model knowledge gap about a feature it has no way to learn about
- Reward hacking (the prompt rewards the wrong thing)

The category matters because it dictates the layer of fix in PLAN. Be specific. "Bad prompt" is not a diagnosis; "the agent has FetchDocs available but isn't calling it for how-to questions" is.

清晰陈述：

Agent实际执行了什么——引用实际回复，列出实际工具调用。
它应该执行什么——用具体术语重述用户的期望。
根本原因假设——整个循环中最重要的句子。从以下选项中选择：
- 缺失能力/工具（Agent确实无法执行正确操作）
- 缺失数据源/检索（Agent没有相关事实信息）
- 路由/规划错误（Agent拥有工具但未调用）
- 指令错误（提示要求它执行其他操作）
- 模型知识缺口（无法通过现有途径了解某个功能）
- 奖励漏洞（提示奖励了错误的行为）

分类至关重要，因为它决定了规划阶段的修复层级。请具体描述。“提示错误”并非有效诊断；“Agent拥有FetchDocs工具，但在回答操作类问题时未调用”才是有效诊断。

Resist the urge to start coding

抵制立即编码的冲动

You will be tempted to start editing the system prompt right now. Don't. Move to PLAN.

你可能会忍不住立即编辑系统提示。不要这么做，进入规划阶段。

Phase 2: PLAN — write the test first

阶段2：规划——先编写测试

Goal: a runnable test suite that fails on the current agent for the right reasons, plus a written plan for the fix.

目标：创建可运行的测试套件，该套件会因正确的原因在当前Agent上失败，同时撰写修复方案。

Confirm the SCOPE of the change BEFORE writing tests

编写测试前确认变更范围

Every "make the agent do X" request carries a scope question the developer usually leaves implicit: should the agent do X globally — in every case — or only in specific cases? Never assume global; resolve it with the developer first, because the answer decides which tests you write. A scope you guess wrong gets baked into a regression guard that actively fights the behavior the developer wanted.

Before writing any test:

Ask: global or conditional? Is X the new default everywhere, or only under some condition C? Use
```
AskUserQuestion
```
(a plain follow-up if that tool isn't available; if you're a subagent, surface the question to your caller and wait — don't decide unilaterally). Most "prefer X" / "lead with X" / "always do X" asks turn out conditional once you probe — there is usually an input where X is the wrong answer.
If conditional, you MUST add negative tests — inputs where X should not happen, including ones where applying X would be actively wrong (the request carries a constraint X can't meet, or another option is plainly correct). A conditional rule tested only on positive cases proves the agent can do X, never that it stops when it shouldn't — so an over-eager change sails through green.
Enumerate the borderline inputs and confirm each — the ones a reasonable person could file on either side of the condition — and have the developer classify them before you encode them as tests. (For "only escalate to a human when the user is frustrated": is "I've asked three times now" frustration, or a neutral fact?)

Skip this only when the change is genuinely global and unconditional (a pure format/content rule that applies to every input). When in doubt, the input the developer originally complained about is itself a data point — make sure your test for it matches the direction they actually want.

每个“让Agent执行X”的请求都隐含一个范围问题，开发者通常不会明确说明：Agent应该全局执行X（在所有场景中），还是仅在特定场景中执行？绝不要默认全局执行；先与开发者确认，因为答案决定了你要编写的测试。如果范围判断错误，会导致回归测试反而阻碍开发者想要的行为。

编写任何测试前：

询问：全局执行还是有条件执行？ X是所有场景下的新默认行为，还是仅在条件C下执行？使用
```
AskUserQuestion
```
（如果该工具不可用，则直接跟进询问；如果你是子Agent，请将问题提交给调用者并等待——不要单方面决定）。大多数“优先X”“推荐X”“始终执行X”的请求，经过深入询问后会发现是有条件的——通常存在X并非正确答案的输入场景。
如果是有条件执行，必须添加否定测试——即X不应该执行的输入场景，包括应用X会导致错误的场景（请求包含X无法满足的约束，或其他选项明显正确）。仅针对正向场景测试有条件规则，只能证明Agent可以执行X，无法证明它在不应执行时会停止——因此过度激进的变更会轻易通过测试。
列举边界输入并逐一确认——即合理情况下可归为条件两侧的输入——在将其编码为测试前，让开发者进行分类。（例如“仅当用户不满时才升级给人工处理”：“我已经问了三次”属于不满，还是中性事实？）

仅当变更确实是全局无条件的（适用于所有输入的纯格式/内容规则）时，才可跳过此步骤。如有疑问，开发者最初投诉的输入本身就是一个数据点——确保测试与他们实际期望的方向一致。

Codify the user's expectation as a test

将用户期望编码为测试

Write each case as a normal pytest test marked

@mlflow.test

, running the scorers with

mlflow.genai.evaluate(data=[...], predict_fn=..., scorers=[...])

and asserting on the result with

assert result.passed, result.reason

— a mix of deterministic and judge-based scorers. (

@mlflow.test

is not a no-op — it requires the MLflow pytest plugin and raises at test time if the plugin isn't enabled. Enable it once with

pytest_plugins = ["mlflow.pytest.plugin"]

in your root

conftest.py

, or run pytest with

-p mlflow.pytest.plugin

; the plugin sets up an MLflow run for the marked test and enables tracing so each case's traces are grouped under one regression-test run.

evaluate

runs the scorers and returns an

EvaluationResult

whose

result.passed

result.reason

give a single pass/fail plus the failing reasons.) Save tests in a stable file named for the module or behavior under test, following the project's existing test-layout convention — not one file per issue or scenario. Before creating a new file, check whether a suite already covers that module/area and add your assertions there; only start a new file when that area genuinely isn't covered yet.

Pick the judge model from the project, not from memory. Resolve it in this order: (1) a model the project has already pinned for evaluation — check for the

MLFLOW_GENAI_JUDGE_DEFAULT_MODEL

env var, a config file, or the judge used by existing scorers/tests; (2) otherwise ask the user which model to use, in

provider:/model

form (e.g.

openai:/gpt-4o

anthropic:/claude-sonnet-4-6

, or a gateway route). Don't invent or hardcode a model id the project can't resolve. Set

MLFLOW_GENAI_JUDGE_DEFAULT_MODEL

once and your judge scorers don't need a

model=

argument at all — keep them all on the one judge. (Pass

model=

per scorer only if you deliberately want to override it.) Skip judge setup entirely if all your scorers are deterministic.

python

import mlflow
from mlflow.genai.scorers import RegexMatch, Guidelines, Safety, scorer

将每个场景编写为标记

@mlflow.test

的标准pytest测试，使用

mlflow.genai.evaluate(data=[...], predict_fn=..., scorers=[...])

运行评分器，并通过

assert result.passed, result.reason

断言结果——结合确定性评分器和基于评判模型的评分器。（

@mlflow.test

并非空操作——它需要MLflow pytest插件，如果插件未启用，测试时会报错。在根目录

conftest.py

中通过

pytest_plugins = ["mlflow.pytest.plugin"]

一次性启用，或运行pytest时添加

-p mlflow.pytest.plugin

；该插件会为标记的测试设置MLflow运行，并启用追踪，以便每个场景的追踪记录归到一个回归测试运行下。

evaluate

运行评分器并返回

EvaluationResult

，其

result.passed

result.reason

提供单一的通过/失败状态及失败原因。）将测试保存到以被测模块或行为命名的稳定文件中，遵循项目现有的测试布局约定——不要为每个问题或场景单独创建文件。创建新文件前，检查是否已有套件覆盖该模块/领域，如有则将断言添加到其中；仅当该领域确实未被覆盖时才创建新文件。

**从项目中选择评判模型，不要凭记忆选择。**按以下顺序确定：(1) 项目已固定用于评估的模型——检查

MLFLOW_GENAI_JUDGE_DEFAULT_MODEL

环境变量、配置文件或现有评分器/测试使用的评判模型；(2) 否则询问用户要使用哪个模型，格式为

provider:/model

（例如

openai:/gpt-4o

、

anthropic:/claude-sonnet-4-6

或网关路由）。不要使用项目无法解析的模型ID。一次性设置

MLFLOW_GENAI_JUDGE_DEFAULT_MODEL

后，所有评判评分器无需添加

model=

参数——统一使用同一个评判模型。（仅当故意要覆盖时，才为每个评分器传递

model=

。）如果所有评分器都是确定性的，则完全跳过评判模型设置。

python

import mlflow
from mlflow.genai.scorers import RegexMatch, Guidelines, Safety, scorer

Judge model comes from MLFLOW_GENAI_JUDGE_DEFAULT_MODEL — no model= needed below.

评判模型来自MLFLOW_GENAI_JUDGE_DEFAULT_MODEL——以下无需指定model=

There is no built-in "excludes" scorer — write a small custom @scorer for must-NOT-contain checks.

没有内置的"排除"评分器——编写一个小型自定义@scorer用于检查必须不包含的内容。

@scorer def excludes_cli_commands(outputs): forbidden = ["mlflow runs create", "log-artifact"] return not any(f in str(outputs) for f in forbidden)

@mlflow.test def test_should_lead_with_ui_path_when_on_experiments_page(): result = mlflow.genai.evaluate( # inputs keys are passed to predict_fn as kwargs; predict_fn exercises the # real, instrumented agent path (see "Exercise the real instrumented code path"). data=[{"inputs": {"prompt": "How do I organize my experiments?", "context": {"currentPage": "Experiments"}}}], predict_fn=my_agent.invoke, scorers=[ RegexMatch(pattern=r"workspaces", case_insensitive=True), # deterministic excludes_cli_commands, # deterministic (custom) RegexMatch(pattern=r"mlflow.org/docs/.+/workspaces"), # deterministic Guidelines( guidelines="The agent should lead with the UI path since the user is on the relevant page.", ), # semantic (uses default judge) ], ) assert result.passed, result.reason


> **Full copy-paste templates** — `conftest.py`/`pyproject.toml` plugin setup, paired guard tests, parametrized cases with meaningful ids, and parallel runs with `pytest-xdist` — live in [references/regression-test-suite.md](references/regression-test-suite.md).

@scorer def excludes_cli_commands(outputs): forbidden = ["mlflow runs create", "log-artifact"] return not any(f in str(outputs) for f in forbidden)

@mlflow.test def test_should_lead_with_ui_path_when_on_experiments_page(): result = mlflow.genai.evaluate( # inputs键作为关键字参数传递给predict_fn；predict_fn调用真实的、已埋点的Agent路径（参见"调用真实的已埋点代码路径"）。 data=[{"inputs": {"prompt": "How do I organize my experiments?", "context": {"currentPage": "Experiments"}}}], predict_fn=my_agent.invoke, scorers=[ RegexMatch(pattern=r"workspaces", case_insensitive=True), # 确定性 excludes_cli_commands, # 确定性（自定义） RegexMatch(pattern=r"mlflow\.org/docs/.+/workspaces"), # 确定性 Guidelines( guidelines="The agent should lead with the UI path since the user is on the relevant page.", ), # 语义化（使用默认评判模型） ], ) assert result.passed, result.reason


> **完整可复制模板**——`conftest.py`/`pyproject.toml`插件设置、配对守卫测试、带有意义ID的参数化场景、以及使用`pytest-xdist`并行运行的模板——可在[references/regression-test-suite.md](references/regression-test-suite.md)中找到。

Write guideline text in prescriptive "should/must" voice

使用规定性的"应该/必须"语气编写指南文本

Guideline strings are stored on the trace and displayed in the regression-test UI as the assertion label. Start each guideline with a prescriptive "should" or "must" statement so it reads as a requirement. You may add PASS/FAIL criteria after the requirement to help the judge, but the leading sentence must state the rule.

Do:

"The agent should lead with exactly one brick recommendation. PASS if it names one family up front; FAIL if it lists multiple without choosing."

Do:

"The response must include the order ID."

Don't:

"The agent leads with one recommendation. PASS if it names exactly one brick family up front; FAIL if it lists multiple or refuses to choose."

- starts with a description, not a requirement.

指南字符串会存储在追踪记录中，并在回归测试UI中作为断言标签显示。每个指南以规定性的“应该”或“必须”语句开头，使其读起来像一个需求。你可以在需求后添加通过/失败标准以帮助评判模型，但开头句子必须明确规则。

正确示例：

"The agent should lead with exactly one brick recommendation. PASS if it names one family up front; FAIL if it lists multiple without choosing."

正确示例：

"The response must include the order ID."

错误示例：

"The agent leads with one recommendation. PASS if it names exactly one brick family up front; FAIL if it lists multiple or refuses to choose."

——开头是描述，而非需求。

Exercise the real instrumented code path

调用真实的已埋点代码路径

The test must invoke the agent through the same code path production uses, so the production tracing-enablement actually runs during the test — same

autolog()

(or

@mlflow.trace

wrapping), same single nested trace shape. A trace the test produced differently from prod verifies the wrong thing, and the VERIFY phase's "inspect a trace by eye" step becomes meaningless.

Never re-declare tracing in the test. Do not add
```
mlflow.<lib>.autolog()
```
(or
```
set_tracking_uri
```
/ span decorators) to the test file to "make a trace show up." That forks the instrumentation: the test now traces a path prod doesn't, and the two silently drift — exactly the bug you're trying to prevent.
If the enablement isn't firing during the test, fix it in the app, don't paper over it in the test. It means the tracing lives in an entry point the test bypasses (e.g.
```
server.py
```
, a FastAPI lifespan, a
```
main()
```
). Move the enablement down into the code both the entry point and the test share — the agent's build/invoke path (
```
build_agent()
```
, the agent's
```
__init__
```
, the function you wrap with
```
@mlflow.trace
```
) — so that constructing-and-running the agent enables tracing everywhere. Keep only deploy-time routing (
```
set_tracking_uri
```
,
```
set_experiment
```
) in the entry point / test harness; that's destination config, not instrumentation.
Symptom to watch for: one user turn shows up as several disconnected top-level traces (e.g. a flat
```
Completions
```
trace per LLM call) instead of one nested agent trace. That almost always means the test drove the raw model loop without the agent-framework autolog prod relies on — the grouping parent is missing because the real instrumentation never ran.

测试必须通过生产环境使用的相同代码路径调用Agent，以便生产环境的追踪埋点在测试中实际运行——相同的

autolog()

（或

@mlflow.trace

包装）、相同的嵌套追踪结构。与生产环境生成方式不同的追踪记录，验证的是错误的内容，且验证阶段的“人工检查追踪记录”步骤将失去意义。

绝不要在测试中重新声明追踪埋点。不要在测试文件中添加
```
mlflow.<lib>.autolog()
```
（或
```
set_tracking_uri
```
/追踪段装饰器）来“让追踪记录显示”。这会导致埋点分叉：测试现在追踪的是生产环境未使用的路径，两者会悄然偏离——这正是你要预防的漏洞。
如果测试中埋点未触发，请在应用中修复，不要在测试中掩盖。这意味着追踪埋点位于测试绕过的入口点（例如
```
server.py
```
、FastAPI生命周期、
```
main()
```
）。将埋点下移到入口点和测试共享的代码中——Agent的构建/调用路径（
```
build_agent()
```
、Agent的
```
__init__
```
、用
```
@mlflow.trace
```
包装的函数），以便构建并运行Agent时，所有地方都能启用追踪。仅将部署时的路由配置（
```
set_tracking_uri
```
、
```
set_experiment
```
）保留在入口点/测试工具中；这是目标配置，而非埋点。
需要注意的症状：一次用户交互显示为多个断开的顶级追踪记录（例如每个LLM调用对应一个扁平的
```
Completions
```
追踪记录），而非一个嵌套的Agent追踪记录。这几乎总是意味着测试调用了原始模型循环，而未使用生产环境依赖的Agent框架自动埋点——由于真实埋点未运行，分组父节点缺失。

Name the test for the behavior it guarantees

为测试命名以体现它所保障的行为

The function name is the contract — and it surfaces as the

mlflow.test.name

tag on the trace and as the row label in the regression-test run + per-scorer summary, so a vague name is a vague row in the UI. Name it for the guarantee, never the bug, the fix, or the raw complaint.

Follow BDD's Given–When–Then (Dan North) — encode the expected behavior and the condition — in the Should/When form, which leads with the guarantee:

test_should_<expected_behavior>_when_<condition>
— drop the

_when_…

clause when the behavior is unconditional (a global format/content rule).

✅

test_should_not_push_brickfather_when_customer_states_a_color

✅

test_should_recommend_brickfather_when_customer_is_undecided

✅

test_should_not_leak_internal_sops_when_asked_for_policy_docs

✅

test_should_lead_with_one_recommendation

(unconditional — no
when
)

❌
```
test_brickfather
```
— push it or withhold it?
❌
```
test_fix_verbose_bug
```
/
```
test_issue_1234
```
— names the bug/ticket, not the contract
❌
```
test_case_3
```
/
```
test_agent_response
```
— says nothing; useless as a UI label

Rules:

Lead with
should_<expected>
, positive voice — the name reads true when green and names the broken guarantee when red.

Paired guard tests share the skeleton with flipped expectations, so the boundary is obvious:

test_should_recommend_brickfather_when_customer_is_undecided

beside

test_should_not_push_brickfather_when_customer_states_a_color

The
when_
clause is the Given+When — the specific attribute that flips the behavior (
```
when_customer_states_a_color
```
), not the raw probe text.
Don't encode the fix or transient details — no
```
test_after_adding_filter
```
, no dates, ticket IDs, or model names. The name outlives today's implementation.

Parametrized cases need meaningful ids (they become

mlflow.test.case_id

@pytest.mark.parametrize("probe", [...], ids=["red_wall", "hurricane_rated", "cheapest"])

— never the default

0/1/2

(References: Dan North, "Introducing BDD" (2006) — Given-When-Then; Roy Osherove, The Art of Unit Testing —
UnitOfWork_StateUnderTest_ExpectedBehavior
.)

函数名就是契约——它会作为

mlflow.test.name

标签显示在追踪记录中，并作为回归测试运行及每个评分器摘要中的行标签，因此模糊的名称会导致UI中的行标签也模糊不清。为测试命名时要体现保障的行为，而非漏洞、修复或原始投诉。

遵循BDD的Given–When–Then（Dan North）——将期望行为和条件编码为Should/When格式，以保障的行为开头：

test_should_<expected_behavior>_when_<condition>
——当行为是无条件的（全局格式/内容规则）时，省略

_when_…

子句。

✅

test_should_not_push_brickfather_when_customer_states_a_color

✅

test_should_recommend_brickfather_when_customer_is_undecided

✅

test_should_not_leak_internal_sops_when_asked_for_policy_docs

✅

test_should_lead_with_one_recommendation

(无条件——无
when
)

❌
```
test_brickfather
```
——是推荐还是拒绝？
❌
```
test_fix_verbose_bug
```
/
```
test_issue_1234
```
——命名的是漏洞/工单，而非契约
❌
```
test_case_3
```
/
```
test_agent_response
```
——无意义；作为UI标签毫无用处

规则：

以
should_<expected>
开头，使用肯定语气——测试通过时名称符合事实，测试失败时名称明确指出被破坏的保障。

配对守卫测试共享骨架，仅翻转期望，以便边界清晰：

test_should_recommend_brickfather_when_customer_is_undecided

与

test_should_not_push_brickfather_when_customer_states_a_color

并列。

when_
子句对应Given+When——即触发行为变更的特定属性（
```
when_customer_states_a_color
```
），而非原始探测文本。
不要编码修复或临时细节——不要使用
```
test_after_adding_filter
```
，不要包含日期、工单ID或模型名称。测试名称要比当前实现更持久。

参数化场景需要有意义的ID（它们会成为

mlflow.test.case_id

）：

@pytest.mark.parametrize("probe", [...], ids=["red_wall", "hurricane_rated", "cheapest"])

——绝不要使用默认的

0/1/2

。

(参考资料：Dan North, "Introducing BDD" (2006) — Given-When-Then; Roy Osherove, The Art of Unit Testing —
UnitOfWork_StateUnderTest_ExpectedBehavior
.)

Scorer-choice rules

评分器选择规则

Deterministic first.
```
RegexMatch
```
(substring / format / URL patterns / code blocks — use
```
case_insensitive=True
```
for plain "contains" checks) and small custom
```
@scorer
```
functions (e.g. a must-NOT-contain check, or
```
Equivalence
```
/
```
Correctness
```
against a ground truth) for surface concerns. They cost zero LLM calls and are reproducible. (There is no
```
Contains
```
/
```
Excludes
```
/
```
Matches
```
/
```
Equals
```
scorer —
```
RegexMatch
```
plus a custom
```
@scorer
```
cover those cases.)
Use one judge model everywhere. Configure it once via
```
MLFLOW_GENAI_JUDGE_DEFAULT_MODEL
```
(resolved above) so every
```
Guidelines
```
scorer shares it — never a different or hardcoded model per scorer.
Guidelines
only when semantic. "Leads with the UI path", "asks ONE clarifying question first", "primary recommendation is X not Y" — these need an LLM judge. Guidelines need both inputs and outputs, so make sure each
```
data
```
row carries an
```
inputs
```
field (and either an
```
outputs
```
field or a
```
predict_fn
```
that produces it) —
```
evaluate
```
passes both to the judge.
Pick scorers from the intent, not from the fix. Choose scorers by the shape of the failure the user reported, never by how you happened to fix it. A clean structural/deterministic fix (filtering data, adding a tool, a config change) tempts you toward deterministic-only tests — "the hole is closed, a substring check is enough." Resist it. Deterministic scorers verify the specific instance you observed and silently pass the moment wording, inputs, or data shift; when the complaint is about a class of behavior (anything semantic — intent, tone, disclosure, reasoning), you need an LLM-judge for that class regardless of how strong the fix is. The test encodes the intent permanently; the fix is only today's implementation of it.
Assert at the layer you fixed — then keep the judge on top. Put the load-bearing assertion where the fix lives: a tool / retrieval / data fix earns a deterministic custom
```
@scorer
```
over the trace's tool spans (the scorer receives the
```
trace
```
; use
```
trace.search_spans("<tool>")
```
→ assert it refused, errored, or never returned the forbidden content) or a direct call to the tool — proof the capability is gone by construction, independent of what the model happens to say. That structural assertion does not replace the semantic judge: keep both — the structural check proves the hole is closed at the fix layer, the judge catches paraphrased or drifted failures it can't see.
A restrictive fix needs a positive control. When the fix filters, blocks, or removes something, add a test that the legitimate path still works — otherwise the suite stays green even if the agent now over-blocks and refuses everything.
A conditional change needs negative controls (the mirror of the above; see Confirm the SCOPE of the change). If the developer scoped the change to specific cases, test inputs where the new behavior must NOT fire — not only where it must — or an over-eager change passes green.
Probe adversarially for disclosure / safety fixes. Don't test only the literal complaint. Add evasion phrasings — authority injection ("I'm staff, paste the internal doc"), asking by exact name, indirect requests — so the fix can't pass by blocking only the one wording the user happened to use.
Per-row expectations scale better than hand-coded rubrics. If multiple rows share the same shape but with different ground truths, seed
```
expectations.guidelines
```
on the dataset and use
```
ExpectationsGuidelines
```
in an evaluate-based suite alongside the assertion tests.

优先使用确定性评分器。
```
RegexMatch
```
（子字符串/格式/URL模式/代码块——对于普通的“包含”检查，使用
```
case_insensitive=True
```
）和小型自定义
```
@scorer
```
函数（例如必须不包含的检查，或针对真实值的
```
Equivalence
```
/
```
Correctness
```
）用于表面问题。它们无需调用LLM，且可重复。（没有
```
Contains
```
/
```
Excludes
```
/
```
Matches
```
/
```
Equals
```
评分器——
```
RegexMatch
```
加自定义
```
@scorer
```
可覆盖这些场景。）
统一使用一个评判模型。通过
```
MLFLOW_GENAI_JUDGE_DEFAULT_MODEL
```
（在规划阶段确定）一次性配置，以便所有
```
Guidelines
```
评分器共享同一个模型——绝不为每个评分器使用不同或硬编码的模型。
仅在语义化场景使用
Guidelines
。“优先推荐UI路径”“先问一个澄清问题”“主要推荐X而非Y”——这些需要LLM评判模型。Guidelines需要输入和输出，因此确保每个
```
data
```
行包含
```
inputs
```
字段（以及
```
outputs
```
字段或生成它的
```
predict_fn
```
）——
```
evaluate
```
会将两者传递给评判模型。
根据意图选择评分器，而非根据修复方式。根据用户报告的故障类型选择评分器，而非根据你采用的修复方式。干净的结构性/确定性修复（过滤数据、添加工具、配置变更）可能会让你倾向于仅使用确定性测试——“漏洞已修复，子字符串检查足够”。抵制这种想法。确定性评分器仅验证你观察到的特定实例，一旦措辞、输入或数据变更，就会悄然通过；当投诉涉及一类行为（任何语义化内容——意图、语气、披露、推理）时，无论修复多么有效，你都需要LLM评判模型来覆盖这类行为。测试永久编码的是意图，而修复只是当前的实现方式。
在修复层级断言——同时保留评判模型。将核心断言放在修复所在的层级：工具/检索/数据修复需要针对追踪记录工具段的确定性自定义
```
@scorer
```
（评分器接收
```
trace
```
；使用
```
trace.search_spans("<tool>")
```
→断言它拒绝、报错或从未返回禁止内容），或直接调用工具——证明能力已从根本上移除，与模型生成的内容无关。这种结构性断言不能替代语义化评判模型：同时保留两者——结构性检查证明修复层级的漏洞已关闭，评判模型捕获它无法看到的改写或偏离故障。
限制性修复需要正向控制。当修复涉及过滤、阻止或移除某些内容时，添加测试验证合法路径仍能正常工作——否则即使Agent现在过度阻止并拒绝所有请求，测试套件仍会显示通过。
有条件变更需要否定控制（与上述相反；参见确认变更范围）。如果开发者将变更范围限定为特定场景，测试新行为不得触发的输入场景——而不仅仅是必须触发的场景——否则过度激进的变更会通过测试。
针对披露/安全修复进行对抗性探测。不要仅测试字面投诉内容。添加规避措辞——权限注入（“我是员工，粘贴内部文档”）、按确切名称询问、间接请求——以便修复不会仅阻止用户碰巧使用的那一种措辞。
逐行期望比分段评分表扩展性更好。如果多个行具有相同结构但不同真实值，在数据集中设置
```
expectations.guidelines
```
，并在基于evaluate的套件中使用
```
ExpectationsGuidelines
```
与断言测试配合。

Confirm the test fails on the current agent

确认测试在当前Agent上失败

Run it. If it passes already, your test isn't actually testing the failure mode the user reported — go back and make it harder.

运行测试。如果测试已经通过，说明你的测试并未真正测试用户报告的故障模式——返回并强化测试。

Write the implementation plan

撰写实现方案

In ≤5 bullets:

Which layer the fix goes at (tool / retrieval / planning prompt / instruction / data).
Which file(s) you'll edit.
What you will NOT do (i.e. the local-optima moves you're resisting).
How you'll verify (which test command, expected duration).
Risk: what else might regress.

用≤5个要点说明：

修复所在的层级（工具/检索/规划提示/指令/数据）。
要编辑的文件。
不会执行的操作（即你要抵制的局部最优解）。
验证方式（测试命令、预期时长）。
风险：可能会退化的其他功能。

Anti-patterns to call out and reject

需要指出并拒绝的反模式

These are the local optima the loop is designed to avoid:

System-prompt hack for "agent doesn't know X exists". If the agent has no way to learn about X, the fix is a TOOL (e.g. FetchDocs) or a retrieval pipeline, not stuffing facts into the system prompt. Hardcoding facts in the prompt makes the agent brittle to every new feature.
System-prompt hack for "agent hallucinates command Y for task Z". Same root cause as above — the agent needs access to the docs, not a static disclaimer.
Per-question if-then patches. "If user asks about prompts, mention the Prompt Registry" is brittle. The agent should find the Prompt Registry by fetching docs, not because we encoded it as a special case.
Tightening the test to pass. If your test fails, fix the agent. Don't loosen the rubric. (Exception: the test was genuinely overspecified — but that's a PLAN-phase mistake worth admitting.)
Touching the prompt as a first move. The prompt is the easiest thing to edit, so it's the most overused. Diagnose the actual layer first.
Letting the fix dictate the test (see Scorer-choice rules → "Pick scorers from the intent, not from the fix"). A strong fix is not a license to drop the semantic judge or the paired case.
Re-declaring tracing in the test (see Exercise the real instrumented code path). A trace that only appears because the test file calls
```
autolog()
```
verifies instrumentation prod doesn't share.

The rule to apply ruthlessly: never hardcode a fix for one instance of a class of failures. If the complaint is about a kind of behavior, fix the capability that produces the whole class — not the single example the user happened to show you.

这些是循环旨在避免的局部最优解：

针对“Agent不知道X存在”的系统提示补丁。如果Agent无法了解X，修复方法是添加工具（如FetchDocs）或检索管道，而非将事实硬编码到系统提示中。在提示中硬编码事实会导致Agent对每个新功能都变得脆弱。
针对“Agent为任务Z幻觉生成命令Y”的系统提示补丁。根源与上述相同——Agent需要访问文档，而非静态免责声明。
针对单个问题的if-then补丁。“如果用户询问提示，提及Prompt Registry”非常脆弱。Agent应该通过检索文档找到Prompt Registry，而非因为我们将其编码为特殊情况。
放宽测试以使其通过。如果测试失败，修复Agent。不要放宽评分标准。（例外：测试确实过度指定——但这是规划阶段的错误，应承认。）
将编辑提示作为第一步。提示是最容易编辑的内容，因此被过度使用。先诊断实际问题层级。
让修复决定测试（参见评分器选择规则→“根据意图选择评分器，而非根据修复方式”）。有效的修复并非放弃语义化评判模型或配对场景的理由。
在测试中重新声明追踪埋点（参见调用真实的已埋点代码路径）。仅因为测试文件调用
```
autolog()
```
才显示的追踪记录，验证的是生产环境未使用的埋点。

需严格遵循的规则：绝不为一类故障的单个实例硬编码修复。如果投诉涉及一类行为，修复产生整类行为的能力——而非用户碰巧展示的单个示例。

Phase 3: IMPLEMENT — smallest change at the diagnosed layer

阶段3：实现——在诊断层级进行最小变更

Goal: the agent now passes the new test, ideally for the right reason.

目标：Agent现在通过新测试，且理想情况下是因为正确的原因通过。

Make the change

实施变更

Implement at the layer your PLAN identified. Examples:

Missing tool → add the tool to the agent's tool schema + implementation + per-tool span trace. Unit-test the tool itself in isolation.
Missing retrieval → add or extend the retrieval source. Make sure it's actually called (verify in trace).
Wrong routing → minimal prompt edit telling the agent when to use which tool. Be specific about the trigger.
Wrong instruction → minimal prompt edit. Cite the exact behavior you're changing.
Model knowledge gap → exposure to data via a tool, not a prompt patch.

在规划阶段确定的层级实施变更。示例：

缺失工具→将工具添加到Agent的工具模式+实现+工具段追踪中。单独对工具进行单元测试。
缺失检索→添加或扩展检索源。确保它确实被调用（在追踪记录中验证）。
路由错误→最小化编辑提示，告知Agent何时使用哪个工具。明确触发条件。
指令错误→最小化编辑提示。引用你要更改的确切行为。
模型知识缺口→通过工具让模型接触数据，而非补丁提示。

Stay minimal

保持最小化

The diff should be small and targeted. If you find yourself rewriting half the system prompt to make one test pass, you're chasing a local optimum — go back to PLAN.

代码差异应小而有针对性。如果你发现自己重写了一半系统提示才让一个测试通过，那么你正在追求局部最优解——返回规划阶段。

Phase 4: VERIFY — confirm the fix and check for regressions

阶段4：验证——确认修复并检查退化

Goal: green tests, including the previously-passing ones.

目标：所有测试通过，包括之前已通过的测试。

Ensure the test runner and plugin are set up

确保测试运行器和插件已设置

The suite runs under pytest with the MLflow pytest plugin (

mlflow.pytest.plugin

) active —

@mlflow.test

raises at test time if the plugin isn't enabled. Enable it once in your root

conftest.py

python

undefined

套件在pytest下运行，且MLflow pytest插件（

mlflow.pytest.plugin

）已激活——如果插件未启用，

@mlflow.test

在测试时会报错。在根目录

conftest.py

中一次性启用：

python

undefined

conftest.py

pytest_plugins = ["mlflow.pytest.plugin"]


…or pass `-p mlflow.pytest.plugin` on the command line. pytest itself isn't guaranteed to be in the environment; if it's missing, **install it yourself and continue** — do not stop and ask the user (an `ImportError` here is a missing-dependency problem, not a test failure):

```bash
python -c "import pytest" 2>/dev/null || uv pip install pytest

(Use whatever installer the project uses —

uv pip install

here; fall back to

pip install

uv

isn't available.)

pytest_plugins = ["mlflow.pytest.plugin"]


…或在命令行添加`-p mlflow.pytest.plugin`。pytest并非一定在环境中；如果缺失，**自行安装并继续**——不要停止并询问用户（此处的`ImportError`是依赖缺失问题，而非测试失败）：

```bash
python -c "import pytest" 2>/dev/null || uv pip install pytest

（使用项目使用的任何安装程序——此处为

uv pip install

；如果

uv

不可用，回退到

pip install

。）

Run the full assertion suite

运行完整的断言套件

bash

undefined

bash

undefined

point at the test file you wrote — wherever the project's layout puts it

指向你编写的测试文件——根据项目布局确定位置

MLFLOW_TRACKING_URI=<server> pytest <path/to/test_file.py> -p mlflow.pytest.plugin -v

judge calls are I/O-bound — run in parallel with pytest-xdist once the suite grows

评判模型调用受I/O限制——套件变大后，使用pytest-xdist并行运行

MLFLOW_TRACKING_URI=<server> pytest <path/to/tests/> -p mlflow.pytest.plugin -n auto


For tests that need a judge model, set the relevant env vars before running — the judge model (`MLFLOW_GENAI_JUDGE_DEFAULT_MODEL`, resolved in PLAN) and whatever credentials it requires (`OPENAI_API_KEY`, gateway base URL, etc.). See [references/regression-test-suite.md](references/regression-test-suite.md) for the `pytest-xdist` worker-consolidation `conftest.py` snippet.

MLFLOW_TRACKING_URI=<server> pytest <path/to/tests/> -p mlflow.pytest.plugin -n auto


对于需要评判模型的测试，运行前设置相关环境变量——评判模型（`MLFLOW_GENAI_JUDGE_DEFAULT_MODEL`，在规划阶段确定）及其所需的凭据（`OPENAI_API_KEY`、网关基础URL等）。`pytest-xdist`工作进程整合的`conftest.py`代码片段可在[references/regression-test-suite.md](references/regression-test-suite.md)中找到。

Read the failure if any test still fails

读取失败信息（如果有测试仍失败）

If the test you just added is still red, go back to EXPLORE for that test. Look at the new trace — did the agent call the new tool? Did the tool return what you expected? Don't immediately patch — diagnose first.

如果你刚添加的测试仍失败，针对该测试返回探索阶段。查看新追踪记录——Agent是否调用了新工具？工具是否返回了你期望的内容？不要立即补丁——先诊断。

Check for regressions

检查退化

If a previously-passing test now fails, that's a real signal — your fix changed something else. Investigate before "fixing" the regression. Sometimes the right answer is to back out your change and try a different layer.

如果之前通过的测试现在失败，这是真实信号——你的变更影响了其他功能。在“修复”退化前先调查。有时正确的做法是撤销变更，尝试不同层级的修复。

Inspect at least one new trace by eye

至少人工检查一个新追踪记录

A green test is not the same as a good answer. Pull the latest trace for the question you fixed and read the agent's response. Does it actually match what the user wanted, or did you just satisfy the rubric? If the latter, the rubric was too loose — go back to PLAN.

测试通过并不等同于答案正确。提取你修复的问题的最新追踪记录，阅读Agent的回复。它是否真正符合用户需求，还是仅仅满足了评分标准？如果是后者，说明评分标准过于宽松——返回规划阶段。

Loop until done

循环直到完成

If multiple issues were reported, repeat the loop per issue. Don't try to fix three things at once — each iteration should have one diagnosis and one targeted change.

如果报告了多个问题，针对每个问题重复循环。不要尝试一次修复三个问题——每次迭代应有一个诊断和一个针对性变更。

When to stop

停止时机

All tests green.
The user has eyeballed at least one fresh trace and confirmed the agent now does what they wanted.
No regressions in pre-existing tests.

If you've iterated 3+ times on the same test without converging, that's a signal to escalate: tell the user the diagnosis was probably wrong and propose a different root cause hypothesis.

所有测试通过。
用户至少人工检查了一个新追踪记录，并确认Agent现在符合他们的需求。
现有测试未出现退化。

如果你针对同一个测试迭代了3次以上仍未收敛，这是需要升级的信号：告知用户诊断可能错误，并提出不同的根本原因假设。

MLflow APIs referenced

引用的MLflow API

```
mlflow.log_feedback(...)
```
— persist user verbal feedback as a HUMAN assessment on the trace
```
mlflow traces get --trace-id <id>
```
— fetch the full trace to inspect spans
```
@mlflow.test
```
marker — requires the
```
mlflow.pytest.plugin
```
pytest plugin (enable via
```
pytest_plugins = ["mlflow.pytest.plugin"]
```
or
```
-p mlflow.pytest.plugin
```
); sets up the MLflow run + tracing for a regression-test case

mlflow.genai.evaluate(data=..., predict_fn=..., scorers=[...])

→

EvaluationResult

; assert with

result.passed

result.reason

— the assertion API

mlflow.genai.scorers.{RegexMatch, Guidelines, Safety, ExpectationsGuidelines, Correctness, Equivalence, RelevanceToQuery, ...}

and the

@scorer

decorator for custom deterministic checks — scorer building blocks (no

Contains

Excludes

Matches

Equals

)

mlflow.genai.datasets.get_dataset(...).merge_records([...])

— seed per-row

expectations.guidelines

for

ExpectationsGuidelines

```
mlflow.log_feedback(...)
```
——将用户口头反馈作为人工评估持久化到追踪记录
```
mlflow traces get --trace-id <id>
```
——获取完整追踪记录以检查追踪段
```
@mlflow.test
```
标记——需要
```
mlflow.pytest.plugin
```
pytest插件（通过
```
pytest_plugins = ["mlflow.pytest.plugin"]
```
或
```
-p mlflow.pytest.plugin
```
启用）；为回归测试场景设置MLflow运行+追踪

mlflow.genai.evaluate(data=..., predict_fn=..., scorers=[...])

→

EvaluationResult

；使用

result.passed

result.reason

断言——断言API

mlflow.genai.scorers.{RegexMatch, Guidelines, Safety, ExpectationsGuidelines, Correctness, Equivalence, RelevanceToQuery, ...}

和用于自定义确定性检查的

@scorer

装饰器——评分器构建块（无

Contains

Excludes

Matches

Equals

）

mlflow.genai.datasets.get_dataset(...).merge_records([...])

——为

ExpectationsGuidelines

设置逐行

expectations.guidelines

fix-agent-issue

Original

Translation

Fix Agent Issue

修复Agent问题

The non-negotiable loop

不可简化的循环

Phase 1: EXPLORE — understand what the trace shows

阶段1：探索——理解追踪记录所呈现的内容

Read the trace, the full trace

完整阅读追踪记录

Capture the user's verbal feedback on the trace

记录用户对追踪记录的口头反馈

Write the diagnosis

撰写诊断报告

Resist the urge to start coding

抵制立即编码的冲动

Phase 2: PLAN — write the test first

阶段2：规划——先编写测试

Confirm the SCOPE of the change BEFORE writing tests

编写测试前确认变更范围

Codify the user's expectation as a test

将用户期望编码为测试

Judge model comes from MLFLOW_GENAI_JUDGE_DEFAULT_MODEL — no model= needed below.

评判模型来自MLFLOW_GENAI_JUDGE_DEFAULT_MODEL——以下无需指定model=

There is no built-in "excludes" scorer — write a small custom @scorer for must-NOT-contain checks.

没有内置的"排除"评分器——编写一个小型自定义@scorer用于检查必须不包含的内容。

Write guideline text in prescriptive "should/must" voice

使用规定性的"应该/必须"语气编写指南文本

Exercise the real instrumented code path

调用真实的已埋点代码路径

Name the test for the behavior it guarantees

为测试命名以体现它所保障的行为

Scorer-choice rules

评分器选择规则

Confirm the test fails on the current agent

确认测试在当前Agent上失败

Write the implementation plan

撰写实现方案

Anti-patterns to call out and reject

需要指出并拒绝的反模式

Phase 3: IMPLEMENT — smallest change at the diagnosed layer

阶段3：实现——在诊断层级进行最小变更

Make the change

实施变更

Stay minimal

保持最小化

Phase 4: VERIFY — confirm the fix and check for regressions

阶段4：验证——确认修复并检查退化

Ensure the test runner and plugin are set up

确保测试运行器和插件已设置

conftest.py

conftest.py

Run the full assertion suite

运行完整的断言套件

point at the test file you wrote — wherever the project's layout puts it

指向你编写的测试文件——根据项目布局确定位置

judge calls are I/O-bound — run in parallel with pytest-xdist once the suite grows

评判模型调用受I/O限制——套件变大后，使用pytest-xdist并行运行

Read the failure if any test still fails

读取失败信息（如果有测试仍失败）

Check for regressions

检查退化

Inspect at least one new trace by eye

至少人工检查一个新追踪记录

Loop until done

循环直到完成

When to stop

停止时机

MLflow APIs referenced

引用的MLflow API

Related skills

相关技能