Dd Audit Ai Activity

Backend

Detection — At the start of every invocation, before taking any action, determine which backend to use:

1. If the user passed --backend pup anywhere in their invocation → use pup mode immediately, regardless of whether MCP tools are present. Skip steps 2–4. 2. Check whether MCP tools are present in your active tool list. The canonical signal is whether mcp__datadog-llmo-mcp__list_llmobs_evals appears in your available tools. 3. If MCP tools are present → use MCP mode throughout. Call MCP tools exactly as named in this skill's workflow sections. 4. If MCP tools are absent → check whether pup is executable: run pup --version via Bash. A JSON response containing "version" confirms pup is available. 5. If pup responds → use pup mode throughout. Translate every MCP tool call to its pup equivalent using the Tool Reference appendix at the bottom of this file. 6. If neither is available → stop and tell the user:

"Neither the Datadog MCP server nor the pup CLI is available. Connect the MCP server (claude mcp add --scope user --transport http datadog-llmo-mcp 'https://mcp.datadoghq.com/api/unstable/mcp-server/mcp?toolsets=llmobs') or install pup."

--backend pup is accepted anywhere in the invocation arguments and is stripped before passing remaining args to the skill logic.

pup invocation rules:

• Invoke via Bash: pup llm-obs <subcommand> [flags]
• pup always outputs JSON. Parse directly — no content-block unwrapping (unlike MCP results, which may wrap JSON in [{"type": "text", "text": "<json>"}]).
• If pup returns an auth error, tell the user to run pup auth login and stop.
• Parallelization: issue multiple Bash tool calls in a single message (one pup command per call).
• Time flags: pup accepts bare duration strings (1h, 7d, 30m) and RFC3339 timestamps. Do not use now--prefixed strings — strip the prefix when converting from a skill --timeframe argument: now-7d → 7d, now-24h → 24h, now-30d → 30d.
• --summary on pup llm-obs spans search strips payload fields to essential metadata only. Use it in bulk/search phases where content is not needed.

Invocation ID: At the very start of each invocation, before any MCP tool call, generate an 8-character hex invocation ID (e.g., 3a9f1c2b). Keep it constant for the entire invocation.

Intent tagging: On every MCP tool call, prefix telemetry.intent with skill:agent-observability-eval-bootstrap[<inv_id>] — followed by a description of why the tool is being called. On the first MCP tool call only, use skill:agent-observability-eval-bootstrap:start[<inv_id>] — instead (note the :start suffix). Example first call: skill:agent-observability-eval-bootstrap:start[3a9f1c2b] — Phase 0: map existing eval coverage for task-cruncher

Eval Bootstrap — Generate Evaluators from Production Traces

Given a sample of production LLM traces, analyze input/output patterns and quality dimensions, then propose a ready-to-use evaluator suite. Four output modes — online evaluators are the default; SDK code, the JSON spec, and the dataset-emit mode are produced on request:

• `publish` (default) — propose online LLM-judge evaluators, then — only after you confirm the suite — write them to Datadog via create_or_update_llmobs_evaluator as disabled drafts (enabled: false). Nothing is created until you confirm at the Phase 2 checkpoint, and nothing scores any spans until you enable it in the UI — the skill never auto-publishes a live evaluator. Once you enable a draft, it runs automatically on matching production spans, traces, or sessions (no dataset, no task function). The skill auto-classifies each proposed evaluator as span-scoped, trace-scoped, or session-scoped based on what the judgment requires (a per-LLM-call tone check vs. an agent goal completion that needs the whole trace vs. user satisfaction across a whole multi-trace conversation) — you accept or override the classification at that checkpoint. Session-scoped evaluators are only proposed when the app's spans carry a session_id (verified by a probe in Phase 1).
• `sdk_code` (on request — `--sdk-code`, or ask after a publish run) — Python .py file using the Datadog Evals SDK (BaseEvaluator / LLMJudge) for offline experiments.
• `data_only` (on request — `--data-only`) — self-contained JSON spec, framework-agnostic.
• `emit_dataset` (on request — `--emit-dataset <path>`) — sample production traces and write a DatasetRecordRaw[] JSON file shaped for LLMObs.create_dataset(records=...). Skips evaluator proposal and generation entirely — this mode produces a dataset, not evaluators. Used by agent-observability-eval-pipeline (Phase 4) to seed an experiment dataset from production behavior.

After a publish run, if the user wants the same suite as offline code or a portable spec, they just ask — the skill regenerates the already-confirmed suite in sdk_code / data_only mode without re-exploring (see "On-request code generation" in Phase 3). The emit_dataset mode is independent of the evaluator workflow and never re-uses a prior proposal — it always re-samples traces.

Usage

/eval-bootstrap <ml_app> [--timeframe <window>] [--sdk-code | --data-only | --emit-dataset <path>] [--trace-limit <N>]

Arguments: $ARGUMENTS

Inputs

If ml_app is missing, ask the user before proceeding. With no mode flag, the skill defaults to `publish` — it proposes online evaluators and, only after you confirm, creates them as disabled drafts (it never auto-enables them). If more than one of --sdk-code, --data-only, --emit-dataset is supplied, error out and ask which mode the user wants.

Available Tools

Key get_llmobs_span_content Patterns

Use the path parameter to extract targeted data without fetching full payloads:

How to Use search_llmobs_spans

Additional filters combine with space (AND): @status:error @ml_app:my-app. Dedicated params (span_kind, root_spans_only, ml_app) work alongside query, but query takes precedence over tags.

To find spans with a specific eval: @evaluations.custom.<eval_name>:* — you can only query for eval presence, not specific results.

To detect whether the app uses sessions: session_id:* matches any span carrying a session_id (session_id is a first-class field — no @ prefix). The Phase 1 session probe uses this to gate session-scope evaluators.

Parallelization Rules

1. `get_llmobs_span_details`: Group span_ids by trace_id. One call per trace_id with ALL its span_ids. Issue ALL calls for a page in a single message. 2. `get_llmobs_span_content`: Each call is independent — always issue ALL in a single message. 3. `get_llmobs_trace` / `get_llmobs_agent_loop`: Parallelize across different traces in a single message. 4. Pipeline parallelism: Start get_llmobs_span_details for page 1 results immediately — don't wait to collect all pages.

---

Evaluator SDK Reference

Applies to `sdk_code` mode only. In data_only mode, use this section as domain context when writing rubric prompts — no SDK classes are emitted.

Imports

# Core classes
from ddtrace.llmobs._experiment import BaseEvaluator, EvaluatorContext, EvaluatorResult

# LLM-as-judge
from ddtrace.llmobs._evaluators.llm_judge import (
    LLMJudge,
    BooleanStructuredOutput,
    ScoreStructuredOutput,
    CategoricalStructuredOutput,
)

# Built-in evaluators (use only if needed)
from ddtrace.llmobs._evaluators.format import JSONEvaluator, LengthEvaluator
from ddtrace.llmobs._evaluators.string_matching import StringCheckEvaluator, RegexMatchEvaluator

Only import what the generated file actually uses.

EvaluatorContext (what evaluate() receives)

@dataclass(frozen=True)
class EvaluatorContext:
    input_data: dict[str, Any]          # Task inputs (from dataset record, NOT from span)
    output_data: Any                     # Task output (from task function return, NOT from span)
    expected_output: Optional[JSONType] = None  # Ground truth (if available)
    metadata: dict[str, Any] = {}        # Additional metadata
    span_id: Optional[str] = None        # LLMObs span ID
    trace_id: Optional[str] = None       # LLMObs trace ID

Important — span data vs evaluator data: When exploring production traces, you see span I/O (e.g., input.value, output.messages). But evaluators run in offline experiments where input_data and output_data come from the user's dataset records and task function, not from spans. The dataset schema is user-defined and may not match span structure. Write evaluator prompts with generic {{input_data}} / {{output_data}} placeholders and add comments describing what data the evaluator was designed for, so the user can adapt to their dataset shape.

EvaluatorResult (what evaluate() returns)

EvaluatorResult(
    value=...,                    # Required. JSONType (str, int, float, bool, None, list, dict)
    reasoning="...",              # Optional. Explanation string
    assessment="pass" or "fail",  # Optional. Pass/fail assessment
    metadata={...},              # Optional. Evaluation metadata dict
    tags={...},                  # Optional. Tags dict
)

LLMJudge — LLM-as-Judge Evaluator

judge = LLMJudge(
    user_prompt="...",              # Required. Supports {{template_vars}}
    system_prompt="...",            # Optional. Does NOT support template vars
    structured_output=...,          # Optional. Boolean/Score/Categorical output, or a dict for custom JSON schema
    provider="openai",              # "openai" | "anthropic" | "azure_openai" | "vertexai" | "bedrock"
    model="gpt-4o",                # Model identifier
    model_params={"temperature": 0.0},  # Optional. Passed to LLM API
    name="eval_name",              # Optional. Must match ^[a-zA-Z0-9_-]+$
)

Template variables in user_prompt: {{input_data}}, {{output_data}}, {{expected_output}}, {{metadata.key}} — resolved from EvaluatorContext fields via dot-path into nested dicts.

Structured Output Types

Boolean — true/false with optional pass/fail:

BooleanStructuredOutput(
    description="Whether the response is factually accurate",
    reasoning=True,                    # Include reasoning field in LLM response
    reasoning_description=None,        # Optional custom description for reasoning field
    pass_when=True,                    # True → pass when true, False → pass when false, None → no assessment
)

Score — numeric within a range with optional thresholds:

ScoreStructuredOutput(
    description="Helpfulness score",
    min_score=1,                       # Minimum possible score
    max_score=10,                      # Maximum possible score
    reasoning=True,
    reasoning_description=None,
    min_threshold=7,                   # Scores >= 7 pass (optional)
    max_threshold=None,                # Scores <= N pass (optional)
)

Categorical — select from predefined categories:

CategoricalStructuredOutput(
    categories={
        "correct": "The response correctly answers the question",
        "partially_correct": "The response is partially correct but missing key information",
        "incorrect": "The response is factually wrong or irrelevant",
    },
    reasoning=True,
    reasoning_description=None,
    pass_values=["correct"],           # Which categories count as passing (optional)
)

Custom JSON schema — arbitrary structured responses for multi-dimensional evals:

# Pass a raw dict as structured_output — used as the JSON schema directly
structured_output={
    "type": "object",
    "properties": {
        "relevance": {"type": "boolean", "description": "Whether the response addresses the question"},
        "confidence": {"type": "number", "description": "Confidence score (0.0 to 1.0)"},
        "reasoning": {"type": "string", "description": "Explanation for the evaluation"},
    },
    "required": ["relevance", "confidence", "reasoning"],
    "additionalProperties": False,
}

Always write standard JSON schema — the SDK adapts it per provider automatically (e.g., Anthropic doesn't support minimum/maximum on number fields, so the SDK moves range constraints into the description; Vertex AI converts const/anyOf to enum). The full parsed JSON dict becomes the eval value; a "reasoning" key (if present) is automatically extracted. No automatic pass/fail assessment.

LLMJudge Prompt Guidelines

The structured_output parameter enforces the response format via JSON schema. Do not prescribe the format in the prompt (no "Answer YES/NO", "Rate 1-10", etc.). Instead, describe the evaluation criteria and let the structured output handle the format.

• system_prompt: Set the judge's role and the app's domain context. Does NOT support template vars.
• user_prompt: Present the data via {{input_data}} / {{output_data}}, then describe what good vs. bad looks like for this dimension.

BaseEvaluator — Custom Code-Based Evaluator

For deterministic checks that do not need LLM judgment:

class MyEvaluator(BaseEvaluator):
    def __init__(self, name=None, ...custom_params...):
        super().__init__(name=name)
        self._param = ...  # Store config as private attrs

    def evaluate(self, context: EvaluatorContext) -> EvaluatorResult:
        # Access: context.input_data, context.output_data, context.expected_output, context.metadata
        # Must NOT modify self attributes (thread safety)
        passed = ...  # Your logic here
        return EvaluatorResult(
            value=passed,
            reasoning="...",
            assessment="pass" if passed else "fail",
        )

Built-in Evaluators

# Validate JSON syntax + optional required keys
JSONEvaluator(required_keys=["name", "age"], output_extractor=None, name=None)

# Validate length (characters, words, or lines)
LengthEvaluator(count_by="words", min_length=10, max_length=500, output_extractor=None, name=None)
# count_by: "characters" | "words" | "lines"

# String matching
StringCheckEvaluator(operation="contains", expected="success", case_sensitive=False, name=None)
# operation: "eq" | "ne" | "contains" | "icontains"

# Regex matching
RegexMatchEvaluator(pattern=r"\d{4}-\d{2}-\d{2}", match_mode="search", name=None)
# match_mode: "search" | "match" | "fullmatch"

Evaluator Type Decision Matrix

Source Verification

If you have access to dd-trace-py locally, verify the API surface by reading the corresponding modules:

• ddtrace.llmobs._evaluators.llm_judge — LLMJudge, BooleanStructuredOutput, ScoreStructuredOutput, CategoricalStructuredOutput
• ddtrace.llmobs._experiment — BaseEvaluator, EvaluatorContext, EvaluatorResult
• ddtrace.llmobs._evaluators.format — JSONEvaluator, LengthEvaluator
• ddtrace.llmobs._evaluators.string_matching — StringCheckEvaluator, RegexMatchEvaluator

---

Workflow

Phase 0: Resolve Inputs & Entry Mode

Entry mode detection:

Parse arguments: Extract ml_app (first non-flag argument), --timeframe (default now-7d), --trace-limit (default 20), --sdk-code, --data-only, and --emit-dataset <path> flags. Set output_mode as follows (at most one of the three mode flags may be set; error if more than one is present):

• --emit-dataset <path> set → output_mode = emit_dataset. Skip the rest of the workflow entry-mode logic and jump directly to Phase 3D below.
• --sdk-code set → output_mode = sdk_code.
• --data-only set → output_mode = data_only.
• otherwise → output_mode = publish (the default — propose online evaluators, gated on user confirmation, created as disabled drafts).

Resolution steps:

1. If ml_app not provided → ask the user. 2. Auto-detect entry mode:

• If the conversation contains an RCA report (look for "Failure Taxonomy" heading, structured failure modes, or severity ratings) → from_rca. Extract the taxonomy.
• If the user provides a free-text failure hypothesis (e.g., "the system prompt lacks grounding") → from_rca. Use the hypothesis as the starting eval target.
• Otherwise → cold_start.

3. If timeframe not provided → default to now-7d. 4. Map existing eval coverage — skip if `output_mode = data_only` (there is no Datadog eval project to check coverage against): Call list_llmobs_evals (org-wide; filter the result client-side to entries where ml_app == <ml_app>). Then, for each eval with source=custom, call get_llmobs_evaluator(eval_name=...) to inspect its prompt template, target, sampling, and filter, and infer which quality dimension it covers. Issue all evaluator calls in a single message (parallelize). Skip source=ootb evals — their names are self-describing and they may not have a fetchable config.

By the end of this step you have a complete coverage map: {eval_name → source, enabled, dimension}. Carry this into Phase 2 for deduplication.

In `publish` mode, also note any template-variable convention the existing custom evaluators already use (so a new suite reads consistently). Online evaluator templates resolve against the full span JSON, not against EvaluatorContext. See the "Online Template Variables" section under "Publishing Conventions" for the supported syntax ({{span_input}}, {{span_output}}, dot-paths, array selectors, filter accessors).

5. Notebook context detection: Scan the current conversation for a Datadog notebook URL that was produced by /eval-trace-rca (pattern: https://app.datadoghq.com/notebook/{numeric-id}). If found, store it as rca_notebook_url and extract the numeric ID as rca_notebook_id. This is used after Phase 3 to offer appending the evaluator suite to that notebook instead of creating a new one.

---

Phase 1: Explore Traces & Identify Eval Targets

Goal: Sample production traces, understand what the app does, and identify quality dimensions worth measuring.

Cold Start Path

1. Sample the app: search_llmobs_spans(query="@ml_app:\"<ml_app>\" @status:ok", root_spans_only=true, limit=50, from=<timeframe>). Filter by @status:ok — error spans have no output to evaluate.

Session probe (gates session-scope proposals; `publish` mode): in the same message, also call search_llmobs_spans(query="@ml_app:\"<ml_app>\" session_id:*", limit=20, from=<timeframe>).

• ≥ 1 result → set sessions_present = true. Note the distinct session_id values and, critically, whether the same session_id appears across multiple `trace_id`s — that cross-trace span is the real signal that a session carries context worth a session-scope evaluator. (A session_id that only ever maps to one trace adds nothing over trace scope.)
• 0 results → sessions_present = false. Do not propose any session-scope evaluator; record a one-line "session scope skipped — no session_id on sampled spans" note for the proposal.

2. Profile the app and identify evaluation target spans: Call get_llmobs_span_details for span_ids grouped by trace_id. Inspect content_info to classify:

For agent/multi-step apps, also call get_llmobs_trace on 2-3 traces to see the full span hierarchy. Compare content_info between the root span and its sub-spans. Then ask two questions for each candidate quality dimension, in this order:

1. Does the verdict depend on more than one span? (e.g., faithfulness depends on a retrieval span's documents AND an llm span's answer; goal completion depends on the chain of tool calls AND the final response.) If yes → trace scope in publish mode. Don't try to compress this into a single span. 2. Only if the answer to (1) is no: pick the single span with the richest signal for that dimension (root has the summary; LLM sub-spans have the full system prompt + tool call results + reasoning chain).

Record the span-kind histogram (agent + tool + llm + retrieval) — multiple kinds under one root is a strong signal you'll have at least one trace-scope evaluator in the suite. See Phase 2's "Span vs. Trace vs. Session Scope Classification" for the mandatory walk-through of canonical trace-scope use cases (and, when sessions_present, the canonical session-scope use cases).

3. Extract content and identify targets: Call get_llmobs_span_content for representative spans. Fetch fields based on app profile:

Issue all calls in a single message. As you read, capture two streams of signal:

Generic quality signals — what does "success" look like? What variance exists across outputs? Each observed quality dimension becomes a candidate evaluator, with the traces you've just read as evidence. Also look for safety signals (scope violations, sensitive data in outputs, out-of-character responses) and add a safety evaluator if you find them.

Domain signals — these become the domain-specific evaluator category in Phase 2 (the highest-leverage category). For every 5–10 traces, write down:

• Recurring intents / question categories — what classes of request does this app handle? (applying for benefit X, comparing flight options, summarizing a policy, creating a widget)
• Entities the app emits in outputs — URLs, agency / company names, code identifiers, monetary amounts, dates, IDs, file paths, phone numbers. Note which ones the user acts on downstream (those are worth a correctness evaluator) versus which are passing references.
• Tool argument shapes (for agent apps) — name each tool the agent calls and the rough schema of its inputs. Tools with non-trivial schemas (≥ 3 fields, structured types) are candidates for argument-correctness evaluators.
• Persona / voice rules — does the app always cite a source, always refuse certain topics (medical, legal, financial advice), always speak in a particular tone? Extract the rules implicitly followed across observed outputs.
• Failure modes specific to the domain — fabricated identifiers, outdated policy references, currency / locale mismatches, off-by-one errors in IDs, wrong units. One observed instance is enough to seed a candidate evaluator.

Don't try to enumerate domain signals exhaustively before reading traces — let the patterns surface as you read. The goal is breadth in the eventual proposal, not completeness in this exploration step.

From RCA Path

1. Extract the failure taxonomy from the RCA report. Each failure mode with High or Medium severity becomes an eval target. Also run the Phase 1 session probe (query="session_id:*") to set sessions_present — a failure that only manifests across a multi-trace conversation (lost context, repeated mistakes, mounting frustration) is a session-scope target.

2. Check root cause categories for infrastructure failures. Before proposing evaluators, scan the Root Cause column of the taxonomy for any of: Instrumentation Deficiency, Harness Deficiency, Runtime Error, Upstream Data Issue, or any other root cause that points to infrastructure/environment rather than model behavior. If any are present, pause and ask:

"Some failure modes were diagnosed as infrastructure or instrumentation issues rather than model behavior (e.g., {list the infra root causes}). Evaluators can be designed two ways:

- Behavior-targeted (recommended for ongoing quality): measure whether the model produces correct, specific output — useful once the infrastructure is fixed and you want to track real quality

- Artifact-targeted (useful as regression guard): detect the specific broken output observed (e.g., generic placeholder responses) — catches regressions if the infrastructure breaks again

>

Which approach do you want, or both?"

• If behavior-targeted: design evaluators for what correct output looks like, not what the broken output looked like. Use the RCA's expected_output / gold-standard examples as the quality bar.
• If artifact-targeted: design evaluators that detect the specific failure symptom (e.g., StringCheckEvaluator for a known bad string, LLMJudge that checks for generic placeholders).
• If both: propose each category separately, clearly labelled.

If all root causes are behavioral (System Prompt Deficiency, Tool Gap, Tool Misuse, Retrieval Failure, etc.) → skip this step and proceed directly.

3. For each target: if the RCA includes trace IDs, use them directly; otherwise search for matching traces. Fetch 2-3 traces per target with get_llmobs_span_content to understand the concrete pattern.

---

Phase 2: Propose Evaluator Suite

Goal: Present a concrete evaluator proposal for user confirmation.

In sdk_code / data_only mode — and for eval_scope: span in publish mode — each evaluator judges one data point: input and output for a single record/span, not a full trace or batch. In publish mode, eval_scope: trace judges a whole trace and eval_scope: session a whole multi-trace session — design those against the trace / session payload instead (see "Span vs. Trace vs. Session Scope Classification" below). Design evaluators accordingly for their scope.

Targeting depends on `output_mode`:

• sdk_code / data_only → offline experiments. Template variables use EvaluatorContext fields ({{input_data}}, {{output_data}}). The actual data shape depends on the user's dataset and task function (see EvaluatorContext note in SDK Reference).
• publish → online evaluation on production spans. Template variables resolve against the full span JSON via dot-paths ({{meta.input.value}}, {{meta.output.messages[*].content}}, …) or the built-in span-kind-aware aliases ({{span_input}}, {{span_output}}). For eval_scope: trace and eval_scope: session, templates resolve against the trace payload ({{spans[...]}}) or the session payload ({{traces[*].spans[...]}}) instead. See "Online Template Variables" under Publishing Conventions for the full syntax. Each evaluator also needs eval_scope, sampling_percentage, and (optionally) filter — surface these in the proposal table so the user can confirm before publishing. Session scope is only used when the Phase 1 probe set sessions_present.

Order proposals from broadest signal to most granular. Propose broadly, let the user curate — see "How many evaluators to propose" below.

1. Domain-specific evaluators — What does "good" mean for this specific app? These are the highest-leverage proposals because they capture quality bars generic evaluators miss. Derive them from the domain signals Phase 1 captured:

• Recurring intents / question categories the app handles (e.g., "applying for a federal benefit", "comparing flight options", "explaining a policy"). Propose an intent_classification or intent_handling_correctness evaluator scoped to the dominant intents.
• Specific entities the app produces (URLs, agency names, code identifiers, monetary amounts, dates, IDs). Propose a per-entity correctness evaluator for the ones with real downstream cost when wrong (e.g., cited_url_is_real, agency_name_matches_request, monetary_amount_is_consistent_with_input).
• Tool argument shapes observed across tool spans. Propose a per-tool argument-correctness evaluator for the tools with non-trivial schemas (e.g., search_flights_args_match_user_request, update_dashboard_widget_targets_correct_widget).
• Persona / voice expectations — does the app always cite sources, always refuse out-of-scope requests, always speak in a specific tone? Propose evaluators for the voice rules you can extract from observed outputs (cites_a_source, refuses_medical_advice, tone_matches_brand).
• Domain-specific failure modes seen across traces (fabricated identifiers, outdated policy references, unit mismatches, currency / locale mismatches). One evaluator per recurring failure mode.

Name each evaluator after the user-facing concern, not the technical check (agency_url_is_real over regex_url_match). Use the trace IDs you read in Phase 1 as evidence — at least one passing case and one failing case per evaluator if you saw both.

2. Outcome evaluators — Did this span / trace produce a good result for the request?

• Examples: task_completion, answer_correctness, response_groundedness

3. Format evaluators — Does the output meet structural requirements?

• Examples: valid_json_output, response_length, citation_format

4. Safety evaluators — Does the output stay within appropriate boundaries?

• Examples: no_pii_leakage, scope_adherence, no_hallucination

How many evaluators to propose

The default 4-6 cap from the older skill version was too tight — it pushed the skill toward generic evaluators only and left domain signals on the table. Updated guidance:

• Aim for 8–15 evaluators in the proposal, distributed across all four categories (with domain-specific usually the largest bucket, outcome second, format and safety smaller). For very simple single-LLM-call apps, fewer is fine; for agent / RAG apps with rich domain signals, lean toward the upper end.
• Quality > generic: every domain-specific proposal should be backed by at least one observed pattern in the sampled traces. Don't invent generic domain evaluators ("response_quality") if you don't have evidence for them.
• Let the user curate: the MANDATORY CHECKPOINT below explicitly asks the user to remove what doesn't apply, not just to approve. Treat the proposal as a candidate set the user trims.

Deduplication Against Existing Coverage

In `data_only` mode: skip this section entirely (coverage map was not built in Phase 0). Proceed directly to the proposal table.

Before building the proposal, apply the coverage map from Phase 0. Coverage is keyed on `(dimension, scope)` — not on dimension alone: every OOTB evaluator runs at span scope, and an enabled OOTB eval does NOT preclude proposing a trace-scope or session-scope evaluator for the same dimension. The three scopes answer different questions.

1. Enabled span-scope eval (OOTB or custom) for dimension D:

• Do NOT propose a new span-scope evaluator for D — that dimension is already covered at span scope.
• DO propose a trace-scope or session-scope evaluator for D when the trace or session shape calls for it (multi-step app, or multi-trace session — judgment depends on cross-span or cross-trace context). Note the relationship in the rationale: e.g., "OOTB Goal Completeness evaluates each LLM span in isolation; this trace-scope goal_completion checks whether the agent's full sequence of steps achieved the user's request, and a session-scope session_goal_completion checks it across the whole conversation — three different questions."

2. Enabled trace-scope custom eval for dimension D: do NOT propose another trace-scope evaluator for the same dimension; that's a real duplicate. Span-scope on the same dimension is still fair game if the data also fits a single span, and session-scope is fair game if the dimension also needs cross-trace context. Likewise, an enabled session-scope custom eval for D blocks only another session-scope eval for D — span and trace scope remain fair game.

3. Disabled OOTB eval: Do NOT propose a new custom span-scope evaluator for that dimension. Instead, surface it in a short note within the proposal and suggest enabling it in the Datadog UI rather than creating a duplicate. Example:

hallucination (ootb, disabled) — consider enabling in Datadog UI (Evaluations → Configure) instead of creating a custom span-scope eval. (A trace-scope rag_faithfulness is still in scope and covers a different question.)

4. Gap identification: Open the proposal with a coverage summary line: "Existing coverage: N evaluator(s) already configured ({names}, all span-scope unless noted). Proposing evaluators for uncovered dimensions and uncovered scopes."

5. All dimensions covered: A dimension is "fully covered" only when the relevant scopes are present (span, plus trace and/or session where the app shape calls for them). If the coverage map accounts for every identified quality dimension at the appropriate scope(s), surface this explicitly and ask the user what they want: (a) review/improve existing eval prompts, (b) add coverage for additional dimensions, or (c) proceed anyway.

For each proposed evaluator:

• Name: Must match ^[a-zA-Z0-9_-]+$ (alphanumeric, underscore, hyphen only)
• Type: LLMJudge (Boolean/Score/Categorical/custom JSON schema), built-in (JSONEvaluator, RegexMatchEvaluator, etc.), or BaseEvaluator subclass. In `publish` mode, only LLM-judge evaluators are supported by the MCP tool — code-based checks must NOT be silently dropped. List them in the same proposal table with `Type` set to the code-based class, mark them under a "Not publishable in this mode" subsection of the proposal, and tell the user they can get them as offline code on request (`--sdk-code`, or ask after the publish run) or as a `--data-only` spec. Treat the code-based proposals as part of the suite for counting and coverage purposes.
• What it measures: 1-2 sentence plain-language description
• Target span: Which span's data the evaluator was designed for (e.g., "root agent span", "LLM sub-span anthropic.request", "all llm spans"). If the root span's I/O is too lossy for the quality dimension (e.g., tool call results aren't visible), note this and specify which sub-span has the signal. In `publish` mode this maps to a combination of `eval_scope` (`span`/`trace`/`session`), `root_spans_only`, and the EVP `filter` query (e.g. `@meta.span.kind:llm` or `service:web`).
• Pass/fail criteria: pass_when=True, min_threshold=7, pass_values=["correct"], or "no automatic assessment" for custom JSON schema
• Template variables: Which of input_data, output_data, expected_output, metadata.* it uses (offline) — or which span paths / aliases it pulls from (publish mode: {{span_input}}, {{span_output}}, {{meta.input.messages[*].content}}, {{meta.metadata.<key>}}, etc.)
• Evidence: At least one trace where it would have caught a failure (or confirmed correct behavior)
• Publish-only fields (only in `publish` mode): integration_provider (default openai), model_name (default gpt-5.4-mini), sampling_percentage (default 10), eval_scope (default span), and any filter query needed to scope to the right spans. Surface defaults in the proposal so the user can override before publishing.
• `integration_account_id` (only in `publish` mode): the integration account the judge LLM is called through. Auto-detected from existing evaluators in the same ml_app (Phase 0 coverage map). Never asked from the user as a raw UUID. If no existing evaluator has one, the field is omitted and the user picks an account in the UI before activating. All evaluators are published with `enabled: false` regardless — see "Always publish as draft" in Phase 3C for the full activation workflow.

Span vs. Trace vs. Session Scope Classification (publish mode)

Don't ask the user; classify per evaluator and let them override at the checkpoint.

Mandatory: walk the four canonical trace-scope use cases first

If Phase 1 found multi-step traces (≥ 2 span kinds, or any tool / retrieval / workflow span under an agent root), you MUST walk through the four canonical trace-scope use cases below before finalizing the suite. For each, decide explicitly: applies (include with eval_scope: trace) or does not apply (record a one-line reason in a "Skipped trace-scope candidates" subsection of the proposal). Skipping all four without per-item justification is a sign you've over-anchored on span scope — re-check.

Mandatory: walk the canonical session-scope use cases (only when sessions_present)

Gate: perform this walk-through only if the Phase 1 session probe set sessions_present = true. If sessions are absent, skip session scope entirely and note "session scope skipped — no session_id on sampled spans" in the proposal.

When sessions_present, you MUST walk through the four canonical session-scope use cases below. For each, decide explicitly: applies (include with eval_scope: session) or does not apply (one-line reason in a "Skipped session-scope candidates" subsection). Session scope answers questions that span more than one trace under the same session_id — a single trace cannot see prior or later turns.

For other proposed evaluators (e.g. tone, format, safety), apply this scope test in order:

1. Can the judgment be answered correctly from one span's meta.input + meta.output, where "correctly" means the verdict cannot change if you considered other spans in the trace? → `eval_scope: span`. 2. Otherwise, if it is answerable from the spans of a single trace → `eval_scope: trace`. Default to trace when the evaluator name contains grounding, faithfulness, hallucination, completeness, correctness across steps, consistency, or workflow — these almost always need cross-span context. 3. Otherwise, if the verdict needs context from more than one trace in the same session_id (overall satisfaction, behavior over time, multi-turn coherence) and sessions_present → `eval_scope: session`. Default to session when the name contains session, conversation, across turns, over time, satisfaction, frustration, or abandonment. If sessions_present is false, fall back to trace scope and note the limitation.

Trade-offs (don't let these dominate the choice)

Trace scope costs more than span scope: one judgment per completed trace (vs. per matching span), larger prompt payloads, and a 3-minute trigger latency (Datadog waits 3 minutes of inactivity before considering a trace complete; later spans are excluded). Session scope costs the most: one judgment per completed session (a session_id is complete after 30 minutes of inactivity — vs. 3 minutes for a trace — and spans arriving > 30 min after the prior span are excluded), with the largest payloads (every span of every trace in the session, capped at 10,000 spans). These are cost-control levers — handle with sampling_percentage and filter, not by demoting scope. The correctness of the eval is what picks the scope.

Surface the classification

Add a Scope column to the proposal table and a one-sentence rationale per evaluator. If you skipped a canonical trace-scope or session-scope use case, list it under the matching "Skipped …-scope candidates" subsection with the reason — the user will see and can override.

Example rationales:

- tone_check — span. Judging "is this single response polite" needs only one LLM span's meta.output.messages[*].content; no other span in the trace can change that verdict.

- goal_completion — trace. Whether the agent finished the user's request depends on the sequence of tool calls and the final LLM response together — meta.output of any single span only shows that step's output.

- tool_use_correctness — trace. Comparing tool inputs against the request and the final response requires correlating ≥ 3 spans (root, tool, final LLM).

- rag_faithfulness — trace. Grounding pairs the retrieval span's documents with the LLM span's answer.

- session_goal_completion — session. Whether the user's overall goals were met depends on every trace in the session_id, not just the last one — only session scope sees the full conversation.

- user_frustration_signals — session. Frustration surfaces as repeated or rephrased asks across traces; a single trace can't reveal the pattern.

>

Example "Skipped trace-scope candidates" entry:

- conversation_quality — skipped: traces contain a single LLM call (no multi-turn signal in this app's instrumentation).

>

Example "Skipped session-scope candidates" entry:

- session_goal_completion — skipped: every session_id maps to a single trace (no cross-trace context — trace scope already covers it).

MANDATORY CHECKPOINT

You MUST output the proposal and wait for user confirmation before proceeding.

## Proposed Evaluator Suite

**App profile**: {LLM | RAG | Agent | Multi-agent}
**Entry mode**: {cold_start | from_rca}

| # | Name | Type | Scope | Measures | Pass Criteria |
|---|------|------|-------|----------|---------------|
| 1 | task_completion | LLMJudge (Boolean) | span | Whether the task was completed on this span | pass_when=True |
| 2 | tool_use_correctness | LLMJudge (Categorical) | trace | Right tool with right arguments across the agent run | pass_values=["correct"] |
| 3 | session_goal_completion | LLMJudge (Categorical) | session | Whether the user's goals were met across the whole multi-trace session | pass_values=["completed"] |
| 4 | ... | ... | ... | ... | ... |

(Drop the **Scope** column when not in `publish` mode.)

For each evaluator:
- **{name}**: {what it measures}
  - Target span: {which span's data it was designed for}
  - Rationale: {which quality dimension it covers and why}
  - {Only in publish mode:} Scope: {span | trace | session} — {one-sentence rationale}
  - Evidence: [Trace {id_short}](https://app.datadoghq.com/llm/traces?query=trace_id:{full_id})

{Only in publish mode, for multi-step apps. Required if any of the four canonical trace-scope use cases was not included above:}

**Skipped trace-scope candidates:**
- `{canonical_use_case}` — {one-line reason it does not apply to this app}

{Only in publish mode, when `sessions_present`. Required if any of the four canonical session-scope use cases was not included above:}

**Skipped session-scope candidates:**
- `{canonical_use_case}` — {one-line reason it does not apply, e.g. "every `session_id` maps to a single trace"}

{Only in publish mode, when the session probe found no sessions:}

**Session scope skipped** — no `session_id` on sampled spans; session-scope evaluators not proposed.

{Only in publish mode, when the suite contains code-based evaluators (JSONEvaluator, RegexMatchEvaluator, LengthEvaluator, StringCheckEvaluator, BaseEvaluator). Required when any code-based proposal exists.}

**Not publishable in this mode** (code-based evaluators — the publish API is LLM-judge only):
- `{name}` ({type}) — {what it would check}. Ask me to emit these as offline SDK code (or run `/eval-bootstrap {ml_app} --sdk-code`), or `/eval-bootstrap {ml_app} --data-only` for a framework-agnostic JSON spec.

Which evaluators should I generate? Treat the proposal as a candidate set — the suite below is intentionally broad so you can pick what matters for your team's quality bar. Reply with which to keep, which to drop, and which to rename; not every domain-specific proposal will fit your priorities. In sdk_code mode you may also add custom evaluators or change provider/model. In publish mode you may override integration_provider, model_name, sampling_percentage, eval_scope, root_spans_only, or filter per evaluator. (In the default publish mode these are created as online drafts in Datadog on confirmation — you review and enable them in the UI. Prefer offline SDK code or a JSON spec instead? Say so and I'll generate the confirmed suite that way.)

Do NOT proceed to code generation until the user confirms.

---

Phase 3: Generate Output

Branch on output_mode:

• publish (default) → skip to Phase 3C
• sdk_code → Phase 3A below
• data_only → skip to Phase 3B
• emit_dataset → skip to Phase 3D (Phases 0 step 4, 1, and 2 are bypassed — see Phase 3D for the dataset-mode workflow)

On-request code generation (after a publish run)

The default path publishes online evaluators. If the user then asks for the suite as offline code or a portable spec (e.g. "now generate the SDK code for these", "give me a JSON spec"), do not re-run Phase 1–2. Reuse the already-confirmed evaluator suite and jump straight to Phase 3A (sdk_code) or Phase 3B (data_only), translating each published online evaluator into the offline form:

• The online prompt template (span/trace/session placeholders) becomes an offline LLMJudge with generic {{input_data}} / {{output_data}} placeholders (offline data comes from the user's dataset/task function, not spans — see the EvaluatorContext note), preserving the rubric and pass criteria.
• Code-based checks that couldn't be published online (the "Not publishable in this mode" set) are emitted as real BaseEvaluator / built-in evaluators here.

This works the other way too: a user who started with --sdk-code can ask to publish the confirmed suite online (Phase 3C). The emit_dataset path is separate from the evaluator workflow — it never has a "confirmed suite" to translate.

---

Phase 3A: Generate & Write Evaluator Code

Goal: Generate the final .py file and write it to disk.

For each confirmed evaluator, generate production-quality Python code following the SDK Reference patterns above.

Code Generation Rules

1. Ground prompts in traces: LLMJudge system prompts and user prompts must reference patterns actually observed in production traces. Never write generic prompts like "evaluate whether the response is good" — ground them in the app's domain, observed failure patterns, and success criteria.

2. Keep template variables generic, add comments for context: Use {{input_data}} and {{output_data}} as top-level placeholders in prompts — do NOT reference nested span paths like {{input_data.messages[-1].content}}. The evaluator's data comes from the user's dataset and task function, not directly from spans. Instead, add a comment above each evaluator describing what data it was designed for and what the user should adapt:

   # Designed for: input_data = user query, output_data = assistant response text
   # Observed from: root agent span (input.value → output.value)
   # If your dataset uses a different structure, adapt the prompt references below.

3. Use the narrowest evaluator type: If a check can be done with JSONEvaluator, RegexMatchEvaluator, StringCheckEvaluator, or LengthEvaluator, do NOT use an LLMJudge. Code-based evaluators are faster, cheaper, and deterministic.

4. BaseEvaluator subclasses:

• Call super().__init__(name=name) in __init__
• Return EvaluatorResult from evaluate()
• Do NOT modify instance attributes in evaluate() (thread safety)

5. Names: Must match ^[a-zA-Z0-9_-]+$. Use snake_case descriptive names.

6. Imports: Consolidate at the top of the file. Only import classes that are actually used.

7. Evaluator list: Collect all evaluators into an evaluators list at the bottom of the file.

8. Anonymize PII: Strip emails, names, and sensitive data from any trace content included in LLMJudge prompts or the header comment.

Output Format

The generated .py file should follow this structure:

"""
Auto-generated evaluators for {ml_app}
Generated: {YYYY-MM-DD} by eval-bootstrap

App profile: {LLM | RAG | Agent | Multi-agent}

Quality dimensions covered:
  - {target_name}: {description}
    Evidence: https://app.datadoghq.com/llm/traces?query=trace_id:{full_id}
  ...

Usage:
    from ddtrace.llmobs import LLMObs

    experiment = LLMObs.experiment(
        name="my-experiment",
        task=my_task_fn,
        dataset=dataset,
        evaluators=evaluators,
    )
    experiment.run()
"""

{imports — only what is used}


# --- Outcome Evaluators ---

{evaluator code}


# --- Format Evaluators ---

{evaluator code}


# --- Safety Evaluators ---

{evaluator code}


# --- Evaluator Suite ---

evaluators = [
    {eval_1_variable_name},
    {eval_2_variable_name},
    ...
]

Only include section comments (Outcome/Format/Safety) for categories that have evaluators.

Write the file

Write the generated code to the output path (suggest ./evals/{ml_app}_evaluators.py if not specified), then display a summary:

## Generated Evaluators

Wrote {N} evaluators to `{output_path}`:

| # | Name | Type | Covers |
|---|------|------|--------|
| 1 | ... | ... | ... |

### Next Steps

1. **Review**: Check the generated prompts and criteria match your expectations
2. **Test offline**: Use `LLMObs.experiment(evaluators=evaluators)` to batch-evaluate against a labeled dataset and verify scores

Notebook export (after summary)

After displaying the summary, offer notebook export.

• If `rca_notebook_url` was detected in Phase 0:

An RCA notebook was created earlier in this session: {rca_notebook_url}

Would you like to (a) append the evaluator suite summary to that notebook, or (b) create a new standalone notebook?

If append: use the notebook creation fallback pattern (see below) with mcp__datadog-mcp__edit_datadog_notebook (id={rca_notebook_id}, append_only=true, evaluator suite summary cell).

If new: use the notebook creation fallback pattern (see below) with mcp__datadog-mcp__create_datadog_notebook.

• If no `rca_notebook_url`:

Would you like to export this evaluator suite summary to a Datadog notebook?

If yes: use the notebook creation fallback pattern (see below) with mcp__datadog-mcp__create_datadog_notebook:

• `name`: Eval Bootstrap: {ml_app} — YYYY-MM-DD
• `type`: report
• `cells`: single markdown cell with the evaluator suite summary
• `time`: { "live_span": "1h" }

Notebook creation fallback pattern (apply to every create_datadog_notebook / edit_datadog_notebook call):

1. Try the MCP tool first. 2. If the MCP call fails, inspect the error:

• Auth / permission error (401, 403) → stop and tell the user.
• Field validation error (error names a specific field) → fix that field and retry the MCP call once.
• Any other error (binding, serialization, unexpected response) → fall back to pup:
• Write the payload to /tmp/nb_bootstrap_{ml_app}.json as a full API envelope: {"data": {"attributes": {"name": "...", "time": {...}, "cells": [...]}, "type": "notebooks"}}
• Run pup notebooks create --file /tmp/nb_bootstrap_{ml_app}.json
• If pup is not available either, render the notebook content as markdown in chat.

3. After successful creation by either method, output the URL: Evaluator suite exported to notebook: <url>

Notebook cell content — the markdown cell should contain:

## Eval Bootstrap: {ml_app}

**Generated**: YYYY-MM-DD | **App profile**: {LLM | RAG | Agent | Multi-agent} | **Entry mode**: {cold_start | from_rca}
**Generated code**: `{output_path}`

{One sentence: what does this app do?}

**Coverage**: {N} new evaluators ({comma-separated dimension names}) | {N} existing (unchanged: {names}) | {gaps if any: dimensions identified but not covered, and why}

### Evaluator Suite

| # | Name | Type | Measures | Pass Criteria |
|---|------|------|----------|---------------|
| 1 | ... | ... | ... | ... |

### Evidence

{For each evaluator: name — 1-line description — [Trace link]}

### Next Steps

1. Review generated prompts in `{output_path}`
2. Run against a labeled dataset to validate scores
3. Deploy to Datadog LLM Experiments

---

Phase 3B: Generate & Write Eval Spec JSON

Goal: Serialize the confirmed evaluator suite and representative trace samples to a single self-contained JSON file — zero SDK dependencies.

Output path: ./evals/{ml_app}_eval_spec.json

JSON Schema

{
  "schema_version": "1",
  "generated_at": "<ISO 8601 UTC>",
  "generated_by": "eval-bootstrap",
  "app": {
    "ml_app": "<string>",
    "app_type": "LLM | RAG | Agent | Multi-agent",
    "trace_window": "<timeframe param, e.g. now-7d>",
    "trace_count": "<integer>"
  },
  "evaluators": [
    {
      "name": "snake_case_name",
      "category": "outcome | format | safety",
      "type": "llm_judge | code_check",
      "description": "<1-2 sentence plain-language description>",
      "target_span": "<which span: root, llm sub-span, etc.>",
      "scoring": {
        "scale": "boolean | score_1_10 | categorical",
        "categories": ["<only present when scale=categorical>"],
        "pass_criteria": "<human-readable: true, >= 7, in [correct], etc.>"
      },
      "rubric": "<full prompt text for llm_judge; null for code_check>",
      "implementation_hints": {
        "type_if_code_check": "json_valid | regex | contains | length_words | null",
        "pattern_if_code_check": "<pattern string or null>",
        "notes": "<optional framework-agnostic implementation guidance>"
      },
      "evidence": [
        {
          "trace_id": "<32-char hex>",
          "span_id": "<16-char hex>",
          "url": "https://app.datadoghq.com/llm/traces?query=trace_id:<trace_id>",
          "observation": "<why this trace illustrates the evaluator>"
        }
      ]
    }
  ],
  "sample_records": [
    {
      "trace_id": "<string>",
      "span_id": "<string>",
      "input": {},
      "output": "<string>",
      "suggested_labels": {
        "<evaluator_name>": "pass | fail | <score>"
      }
    }
  ]
}

Field Notes

• `evaluators[].type`: "llm_judge" for semantic evaluators; "code_check" for deterministic checks (regex, length, JSON validity, etc.).
• `evaluators[].rubric`: For llm_judge — full prompt text grounded in observed trace patterns. Use {{input}} and {{output}} as generic placeholders (not {{input_data}} — that's ddeval-specific). For code_check — null.
• `evaluators[].implementation_hints.notes`: Optional framework-agnostic guidance, e.g. "For OpenAI Evals, use rubric as a model-graded criterion. For Braintrust, use as an LLM scorer. For Promptfoo, use as an llm-rubric assertion."
• `sample_records`: 10–20 representative traces from Phase 1. suggested_labels are Claude's best-read from trace inspection — not ground truth. The field name communicates this explicitly.
• PII rule: Strip emails, names, and sensitive data from all input, output, and evidence[].observation fields before writing (same as Phase 3A).

Writing Instructions

1. Assemble the JSON object in memory following the schema above. 2. Populate sample_records from traces already fetched in Phase 1. Fetch additional traces (up to 20 total) if fewer than 10 were read. 3. Anonymize PII in all input, output, and evidence[].observation fields. 4. Write the file with 2-space indentation using the Write tool. 5. Display a completion summary:

## Generated Eval Spec

Wrote `./evals/{ml_app}_eval_spec.json`:

- **{N} evaluators** ({outcome_count} outcome, {format_count} format, {safety_count} safety)
- **{M} sample records** with suggested labels

| # | Name | Category | Type | Pass Criteria |
|---|------|----------|------|---------------|
| 1 | ... | ... | ... | ... |

### Next Steps

1. **Review**: Open `./evals/{ml_app}_eval_spec.json` and verify the rubrics match your expectations
2. **Implement**: Use the `rubric` field to configure evaluators in your framework of choice:
   - OpenAI Evals: use `rubric` as a model-graded criterion
   - Braintrust: create an LLM scorer with the rubric text
   - Promptfoo: use as an `llm-rubric` assertion
   - Custom code: call your LLM API with the rubric and parse the structured output
3. **Label**: `suggested_labels` are Claude's best guesses from trace inspection — verify against ground truth before using as training data

Notebook export (after summary)

Same logic as Phase 3A — offer to append to the RCA notebook if rca_notebook_url was detected, or create a new standalone notebook. Use the same notebook cell format as Phase 3A, substituting output_path with the JSON spec file path. In pup mode, use pup notebooks create / pup notebooks edit as described in Phase 3A.

---

Phase 3C: Publish Online Evaluators to Datadog (as disabled drafts)

Reached only after the user confirms the suite at the Phase 2 checkpoint — nothing below is written to Datadog before that.

Goal: For each confirmed evaluator, write an LLM-judge configuration to Datadog via create_or_update_llmobs_evaluator as a disabled draft (enabled: false). It scores no spans until the user reviews and enables it in the UI; once enabled, it runs automatically on matching production spans.

Pre-publish checks (single message — parallelize)

For every proposed eval_name, call get_llmobs_evaluator(eval_name=...):

• Not found → safe to create.
• Found → existing evaluator with the same name. Surface a diff to the user (existing dimension/prompt vs. proposed) and ask:

Evaluator {name} already exists. Overwrite, rename, or skip?

If overwrite: keep the fetched config as the base and merge your generated fields on top, then send the complete object back. The MCP tool is full-replace — any field you omit (e.g. temperature, max_tokens, filter, sampling_percentage) reverts to its default. Never re-publish without round-tripping the existing config.

If rename: append a suffix (e.g. _v2) and treat as new.

If skip: drop from the publish set.

Publishing Conventions

Required parameters for each create_or_update_llmobs_evaluator call: eval_name, application_name (= ml_app), enabled, integration_provider, model_name, prompt_template, parsing_type, output_schema, plus a telemetry.intent string.

Defaults to use unless the user overrides:

Prompt template: convert the LLMJudge prompt into the MCP shape — an ordered array of {role, content} messages. The system prompt becomes {role: "system"}, the user prompt becomes {role: "user"}. Use span-data placeholders (see below) — not the offline {{input_data}} / {{output_data}} form, which only exists in EvaluatorContext.

Online Template Variables

Online evaluator prompts run through the dd-source template library (domains/ml-observability/shared/libs/template). Missing paths → empty string. The data shape templates resolve against depends on `eval_scope`:

• `eval_scope: span` (default) — placeholders resolve against a single span's JSON (the llmobs.Span JSON-marshaled to a map). Use the span aliases / dot-paths below directly.
• `eval_scope: trace` — placeholders resolve against the trace payload { spans: [...] }. Use {{spans[N]...}}, {{spans[*]...}}, or {{spans[field.path:value]...}} to select span(s) before applying field paths. The {{span_input}} / {{span_output}} aliases are not available in trace scope — reference span data through the spans array instead.
• `eval_scope: session` — placeholders resolve against the session payload { session_id, traces: [ { trace_id, root_span_id, spans: [...] }, … ] }. Reach spans through {{traces[*].spans[*]...}} (nested one level deeper than trace scope) plus the array selectors below. The {{span_input}} / {{span_output}} aliases and trace scope's top-level {{spans[...]}} are not available — spans live under traces[*].spans. Only used when sessions_present (Phase 1 probe).

Span-scope (eval_scope: span)

Built-in span-kind-aware aliases (preferred when the evaluator is generic across span kinds):

Common explicit dot-paths (use when the evaluator is purpose-built for one span kind):

Trace-scope (eval_scope: trace)

Session-scope (eval_scope: session)

Note: the filter selector applies to the `spans` array (traces[*].spans[meta.span.kind:llm]), not to traces — a trace object has only trace_id, root_span_id, and spans, so there is nothing to filter on at the trace level.

Array selector syntax (applies to all scopes)

• [N] — index (0-based)
• [START,END] — inclusive range, END is clamped to slice length
• [*] — wildcard (fan-out over all elements)
• [field.path:value] — filter array elements by a nested field equality, e.g. messages[role:user] or spans[meta.span.kind:tool]

Resolution rules to keep in mind when writing prompts:

• Arrays of strings → newline-joined
• Arrays of objects / mixed values → compact JSON
• Single empty slice → empty string
• Implicit fan-out: messages.content behaves the same as messages[*].content
• Negative indices are not supported (parse error) — use [N] with a known index, or [*] for "last assistant turn" semantics

When to pick which form:

• Generic span evaluator (e.g. tone_check, output_format) → use {{span_input}} / {{span_output}} so it works across span kinds.
• LLM-span-specific evaluator (e.g. system_prompt_adherence) → reach for explicit meta.input.messages[*].content / meta.output.messages[*].content so you can split system vs. user vs. assistant turns.
• Span-scope RAG evaluator (single retrieval+generation span) → combine {{meta.input.documents}} with {{span_output}}.
• Trace-scope evaluator → see "Trace-scope evaluator examples" below for the four canonical patterns (goal completion, tool-use correctness, RAG faithfulness, conversation quality).
• Session-scope evaluator → see "Session-scope evaluator examples" below for the four canonical patterns (session goal completion, multi-turn conversation quality, user frustration signals, agent consistency across the session).
• Metadata-aware evaluator → reference {{meta.metadata.<key>}} directly.

If the user has existing custom evaluators in the same ml_app (Phase 0 coverage map), match their convention when there is no strong reason to deviate.

Trace-scope evaluator examples

Concrete user-prompt bodies for the four canonical trace-scope use cases, drawn from the public docs (Trace-Level Evaluations). Each goes alongside a static System prompt that describes the rubric (no placeholders).

Use these as starting points. Adapt the filter and span paths to the actual span names / kinds the app emits (observed during Phase 1).

Session-scope evaluator examples

Concrete user-prompt bodies for the four canonical session-scope use cases (adapted from the public Session-Level Evaluations docs). Each goes alongside a static System prompt that describes the rubric (no placeholders). The filter is matched against the session's root span (not every span), so scope it with root-span attributes; there is no @parent_id:undefined requirement beyond that.

Adapt the filter and span paths to the actual span kinds the app emits (observed in Phase 1).

`output_schema` wrapper format (required for all providers)

The output_schema field is NOT a bare JSON Schema. It must use the OpenAI json_schema object shape. `name` is a fixed type discriminator, not the evaluator name — the UI validates it against a strict allowlist and rejects any other value:

The property key inside schema.properties must match name exactly. The required array may only be ["<type_key>"] or ["<type_key>", "reasoning"] — any other value is rejected. Always include "reasoning": {"type": "string"} for UI display.

Boolean (BooleanStructuredOutput(pass_when=True)):

{
  "output_schema": {
    "name": "boolean_eval",
    "strict": true,
    "schema": {
      "type": "object",
      "properties": {
        "boolean_eval": {"type": "boolean", "description": "Whether the criterion is met"},
        "reasoning": {"type": "string", "description": "Explanation for the evaluation"}
      },
      "required": ["boolean_eval", "reasoning"],
      "additionalProperties": false
    }
  },
  "assessment_criteria": {"pass_when": true}
}

Score (ScoreStructuredOutput(min_score=1, max_score=10, min_threshold=7)):

{
  "output_schema": {
    "name": "score_eval",
    "strict": true,
    "schema": {
      "type": "object",
      "properties": {
        "score_eval": {"type": "number", "description": "Score from 1 to 10", "minimum": 1, "maximum": 10},
        "reasoning": {"type": "string", "description": "Explanation for the score"}
      },
      "required": ["score_eval", "reasoning"],
      "additionalProperties": false
    }
  },
  "assessment_criteria": {"min_threshold": 7}
}

Add max_threshold to assessment_criteria if set.

Categorical (CategoricalStructuredOutput(categories={...}, pass_values=[...])):

{
  "output_schema": {
    "name": "categorical_eval",
    "strict": true,
    "schema": {
      "type": "object",
      "properties": {
        "categorical_eval": {
          "type": "string",
          "anyOf": [
            {"const": "correct", "description": "The response correctly answers the question"},
            {"const": "partially_correct", "description": "Partially correct but missing information"},
            {"const": "incorrect", "description": "The response is wrong or irrelevant"}
          ]
        },
        "reasoning": {"type": "string", "description": "Explanation for the category chosen"}
      },
      "required": ["categorical_eval", "reasoning"],
      "additionalProperties": false
    }
  },
  "assessment_criteria": {"pass_values": ["correct"]}
}

Note: categorical uses "type": "string" alongside anyOf (each const is a string value), unlike the offline SDK which uses bare anyOf at the property root.

Custom / multi-dimensional: not directly supported via the fixed-name schema. Implement as a score or categorical evaluator where possible, or split into multiple evaluators. The name must be one of the three fixed values above.

Filter scoping: when the proposal targets a specific span kind (e.g. an LLM sub-span), translate it into an EVP filter query — e.g. @meta.span.kind:llm, service:checkout-agent, or a more specific tag. Combine with root_spans_only:true only when the target is the trace root.

For `eval_scope: trace`:

• The evaluator triggers once per completed trace, after a 3-minute inactivity window. Late-arriving spans (>3 min after the prior span on the same trace) are excluded from the evaluation. Surface this in the proposal so the user knows about both the latency and the potential miss for sparse-activity agents (long-running agents whose steps are sparser than 3 minutes apart).
• The filter query must match the trace's root span only — always include @parent_id:undefined (or root_spans_only: true) to avoid double-firing across descendants. Combine with @meta.span.kind:agent (or whatever kind the app uses for root spans, observed in Phase 1) for narrowing.
• Sampling at trace scope is heavier than at span scope (one trace = many spans on the judge's side). Default sampling_percentage to `5` for trace-scope evaluators (instead of the span default 10); the user can raise it after a manual review pass.

For `eval_scope: session`:

• Only publish session-scope evaluators when sessions_present (Phase 1 probe). Otherwise the evaluator never fires — no span carries a session_id.
• The evaluator triggers once per completed session. A session_id is complete after 30 minutes of inactivity (vs. 3 minutes for a trace); spans arriving > 30 min after the prior span are excluded. Surface both the latency and the exclusion — long-running or sparse sessions may be split or truncated, and very large sessions are capped at 10,000 spans.
• The filter is matched against the session's root span (the same prefilter trace scope uses), so scope it with root-span attributes — e.g. @meta.span.kind:agent (or the app's root span kind, observed in Phase 1). You do not need @parent_id:undefined: the session root is already a root span.
• Sampling at session scope is the heaviest of all (one session = many traces = many spans on the judge's side). Default sampling_percentage to `5` (matching trace) and flag it as the heaviest scope; the user can lower it further after a manual review pass.

Always publish as draft (enabled: false)

Always create / update evaluators with `enabled: false` — regardless of whether integration_account_id was auto-detected from existing evaluators. The UI is the source of truth for activation; the skill should never auto-enable evaluators on the user's behalf. The user reviews each draft in the UI, confirms the integration account is correct (the auto-detected ID may belong to a different judge LLM than the one they want for this app), and flips the toggle when they're satisfied.

This makes the workflow safe by default: a wrong integration_account_id, a mistuned prompt, or an over-broad filter never goes live without a human pass. Auto-detection of the account ID still helps because the draft renders with the right account pre-selected — review is faster.

integration_account_id resolution

The integration_account_id is an opaque UUID that the UI matches against the org's integration accounts list to populate the account section dropdown. Users typically don't know this value, so never ask the user to supply a raw UUID.

Resolution order:

1. Inherit from existing evaluators — in Phase 0 you called get_llmobs_evaluator for each existing custom evaluator. Check the llm_provider.integration_account_id field on those responses. If any of them have a value, use that same ID on the published drafts. If multiple different IDs appear across existing evaluators, pick the most common one and note which you chose so the user can correct it during the UI review pass.

2. Omit if no existing evaluator has one — if no custom evaluator in the ml_app has an integration_account_id, omit the field from the publish payload. The draft will render without an account pre-selected; the user picks one during the UI review pass before activating.

Either way, the evaluator is published with enabled: false. The user is the gate — see "Always publish as draft" above.

Publish (single message — parallelize)

Issue all create_or_update_llmobs_evaluator calls in a single message (one per evaluator). Set telemetry.intent to a short English description like "skill:agent-observability-eval-bootstrap — Bootstrap evaluator suite for ml_app=<ml_app> from production trace analysis.".

If any call fails, capture the error and continue with the remaining evaluators — never silently abort the batch. Report failures explicitly in the summary.

Summary

## Published Evaluators (drafts — pending UI review)

Wrote {N} online evaluators to ml_app `{ml_app}`. **All published as drafts (`enabled: false`)** — review and activate them in the UI before they start scoring spans.

| # | Name | Action | Provider/Model | Sampling | Scope | Account auto-detected | Status |
|---|------|--------|----------------|----------|-------|-----------------------|--------|
| 1 | task_completion | created (draft) | openai/gpt-5.4-mini | 10% | span | yes | ok |
| 2 | response_groundedness | overwrote (draft) | openai/gpt-5.4-mini | 10% | span | yes | ok |
| 3 | scope_adherence | renamed (`_v2`) (draft) | openai/gpt-5.4-mini | 10% | span | no — pick in UI | ok |
| 4 | citation_format | failed | openai/gpt-5.4-mini | 10% | span | — | error |
| 5 | session_goal_completion | created (draft) | openai/gpt-5.4-mini | 5% | session | yes | ok |

{If any failed:}
**Errors**:
- `{name}`: {error message}

{If any code-based proposals were dropped:}
**Not published** (code-based, not supported by online evaluator API):
- `{name}` ({type}) — ask me to emit it as offline SDK code, or run `/eval-bootstrap {ml_app} --sdk-code`.

### Next Steps — review and activate in the UI

The drafts are intentionally not running yet. Walk through each one in the Datadog UI before flipping the enable toggle:

1. **Open the drafts**: Datadog → Agent Observability → Evaluations → filter by ml_app `{ml_app}` (the new drafts appear with status `Disabled`).
2. **For each draft**:
   - **Verify the integration account** in the Provider section. If the column above shows `auto-detected: yes`, confirm it's the correct account for the judge LLM you want this evaluator to call through. If `no`, pick an account from the dropdown.
   - **Skim the prompt template** and the structured-output schema — make sure the span / trace / session scope, filter, and sampling match what you actually want to measure.
   - **Click into a sample span/trace** and use the test pane to dry-run the prompt against real data. Confirm the result matches your expectation.
3. **Enable**: once each draft passes review, toggle it to enabled. Datadog starts scoring incoming spans immediately.
4. **Wait for first scores**: with `sampling_percentage=10` (span scope) or `5` (trace / session scope), expect first results within minutes for high-traffic apps — except **session scope**, where the first result appears only after the session's 30-minute inactivity window closes.
5. **Tune sampling/filter**: if results are noisy or volume is too high, reduce `sampling_percentage` or tighten the `filter` from the UI. Re-running `/eval-bootstrap {ml_app}` will round-trip the existing config before overwriting — your manual tweaks survive across reruns.
6. **Prefer offline code?** These evaluators run online in Datadog. If you'd rather iterate on the same suite as offline SDK code (`sdk_code`) or a portable JSON spec (`data_only`), just ask — I'll regenerate the **confirmed** suite without re-exploring.

Notebook export (after summary)

Same logic as Phase 3A — offer to append to the RCA notebook if rca_notebook_url was detected, or create a new standalone notebook. The notebook cell should list the published evaluators with their UI links and the ml_app they target. In pup mode, use pup notebooks create / pup notebooks edit as described in Phase 3A.

---

Phase 3D: Emit Dataset from Production Traces

Goal: Sample production traces for ml_app, extract input / output pairs, and write a DatasetRecordRaw[] JSON file shaped for LLMObs.create_dataset(records=...). This mode does not propose or generate evaluators — Phases 1 and 2 are bypassed. The dataset is the sole artifact.

When this mode runs: triggered by --emit-dataset <path>. Skip Phase 0 step 4 (eval coverage map) — it's unrelated to dataset extraction.

Sampling

1. Call search_llmobs_spans(query="@ml_app:\"<ml_app>\" @status:ok", root_spans_only=true, limit=<trace-limit>, from=<timeframe>). Filter to @status:ok so the resulting dataset records reflect intended app behavior (errored traces have no usable output baseline). 2. For each sampled root span, call get_llmobs_span_details (group by trace_id) and get_llmobs_span_content to read the root span's input and output fields. Issue all calls for a page in a single message (parallelize).

Record extraction

For each sampled root span, build one DatasetRecordRaw entry:

PII scrub. Before writing, run the same scrub used elsewhere in the agent-observability skill set: replace email / phone / SSN / API-key regex matches in all string values with <REDACTED:pii-type>. Surface a one-line warning listing affected record indices in the summary.

Tag normalization (MANDATORY pre-write step). The SDK's Dataset.append() calls validate_tags_list which rejects any tag without a : separator (Tag '<tag>' is malformed. Tags must be in 'key:value' format.). Before writing the JSON, run this normalization on every record's tags list:

1. Drop any non-string, None, or empty-string entries. 2. Already `key:value` (":" in tag and both halves non-empty) → keep as-is. 3. Bare string (no :) → wrap as "tag:<value>" (so e.g. "interactive" becomes "tag:interactive"). Never silently drop — the original observation is preserved under the generic tag: key. 4. Trailing or leading `:` (e.g. "foo:", ":bar") → wrap as "tag:<full-original-string>" so the malformed value is preserved without crashing the SDK.

Track the count of normalizations and surface in the summary as a one-line note (Tag normalizations applied: N (bare strings auto-prefixed with 'tag:')). Do not write the dataset if any record's tags list still contains a non-key:value string after normalization — that's a bug in this step.

Skip records where:

• meta.output is empty or missing (nothing usable as a baseline).
• The root span's I/O is purely structural (e.g. only tool calls, no natural-language output). Record the count of skipped records and surface in the summary.

Write the file

1. Write the JSON array (2-space indented) to <path> using the Write tool. The path was supplied by the user via --emit-dataset <path> — use it verbatim. 2. Do not push to Datadog from this mode. Publishing to Datadog is the next step in the onboarding flow (LLMObs.create_dataset(records=...)) and is handled by the caller (the onboarding orchestrator or the user directly).

Summary

After writing, print:

## Generated Dataset

Wrote `<path>` — {N} records sampled from `<ml_app>` ({timeframe}).

| Stat | Value |
|---|---|
| Records emitted | {N} |
| Records skipped (no output) | {M} |
| PII redactions applied | {P} (indices: {list or "none"}) |
| Tag normalizations applied | {T} (bare strings auto-prefixed with `tag:`) |

**Important**: `expected_output` is the **current production behavior baseline**, not ground truth. Use this dataset for regression-style experiments (does my refactor change observed outputs?) before promoting it to a labelled gold set.

### Next Steps

1. **Review**: open `<path>` and spot-check a few records. Adjust `expected_output` where the production output was wrong.
2. **Publish to Datadog**:

from ddtrace.llmobs import LLMObs import json, os LLMObs.enable(api_key=os.getenv("DD_API_KEY"), app_key=os.getenv("DD_APPLICATION_KEY"), site=os.getenv("DD_SITE", "datadoghq.com"), project_name="<your-project>", agentless_enabled=True) records = json.load(open("<path>")) ds = LLMObs.create_dataset(dataset_name="<your-name>", records=records) print(ds)

3. **Run an experiment** against the published dataset via `agent-observability-experiment-py-bootstrap --dataset-name <your-name>`.

Skip the notebook export prompt in this mode — datasets aren't well-served by a markdown summary in a notebook, and the next step (experiment generation) will produce its own artifacts.

---

Operating Rules

• Breadth over precision; let the user curate: Propose 8–15 evaluators distributed across domain-specific (largest bucket — derived from Phase 1 domain signals), outcome, format, and safety. Users can always remove what doesn't fit their quality bar; they cannot easily add what was not proposed. Anchor every domain-specific proposal in at least one observed trace pattern — don't invent generic domain evaluators without evidence.
• Don't overfit: Write criteria that generalize beyond the specific sampled traces. Use examples as grounding, not as the sole criteria.
• Show your work: Every proposed evaluator cites at least one trace as evidence with a clickable link: [Trace {first_8}...](https://app.datadoghq.com/llm/traces?query=trace_id:{full_32_char_id}).
• New file only: Never modify existing evaluator code or experiment configurations.
• Honest about uncertainty: If fewer than 5 traces support a proposed evaluator, flag it as tentative.
• Session scope is gated: only propose or publish eval_scope: session evaluators when the Phase 1 probe confirmed sessions_present (spans carry a session_id, ideally one that spans multiple traces). If sessions are absent, skip session scope and say so — never publish a session evaluator that can't fire.

---

Tool Reference

This appendix applies only in pup mode. In MCP mode, use the tool names in the workflow sections directly.

Spans and traces

Evaluators

create_or_update_llmobs_evaluator in pup mode

pup uses a flat JSON file (all fields top-level). get-evaluator returns a nested object. Transform as follows:

1. Round-trip check: Call pup llm-obs evals get-evaluator EVAL_NAME first. If it exists, start from its config. 2. Flatten `llm_provider`: hoist integration_provider, model_name, integration_account_id, temperature to top level, dropping the llm_provider key. 3. Merge and set `enabled: false`. 4. Write to temp file and call:

   pup llm-obs evals create-or-update EVAL_NAME --file /tmp/eval_EVAL_NAME.json

Use unique temp file names when publishing multiple evaluators in parallel (e.g. /tmp/eval_toxicity.json).

Notebooks

The cells file is a JSON array of cell objects:

[{"attributes": {"definition": {"type": "markdown", "text": "## Section\n\nContent."}}, "type": "notebook_cells"}]

• MCP result parsing safety: Before writing any script (Python, jq, etc.) that iterates over or accesses fields in an MCP tool result, inspect the raw structure first — check type(result), top-level keys, and whether the payload is nested inside a content block (e.g. [{'type': 'text', 'text': '<json>'}]). Extract and json.loads() the inner payload if needed before parsing. Never assume MCP results are bare dicts or lists.

--evaluator-style class (advanced — for evaluators that need state or async I/O)

BaseEvaluator subclasses with evaluate(self, context: EvaluatorContext) -> EvaluatorResult. Always return EvaluatorResult — never a bare value. State-bearing evaluators usually have richer reasoning to surface anyway.

Code to emit

from ddtrace.llmobs import BaseEvaluator, EvaluatorContext, EvaluatorResult

class FaithfulnessJudge(BaseEvaluator):
    def __init__(self):
        super().__init__(name="faithfulness")
        # TODO(user): initialize any client or state here

    def evaluate(self, context: EvaluatorContext) -> EvaluatorResult:
        # context exposes: input_data, output_data, expected_output, metadata
        # TODO(user): replace placeholder logic with your faithfulness check
        passed = context.output_data is not None
        return EvaluatorResult(
            value=1.0 if passed else 0.0,
            reasoning="placeholder — replace with your faithfulness rubric",
            assessment="pass" if passed else "fail",
            metadata={"evaluator_version": "v1"},
        )

Rules

• Call super().__init__(name=...) in __init__. The name is the column header in the Datadog Experiments UI.
• evaluate() runs in the experiment's worker pool. Do NOT mutate self from evaluate() (thread safety) — state set in __init__ should be read-only thereafter.
• For async work (e.g., calling an LLM judge over the network), prefer wrapping with asyncio.run(...) inside evaluate() rather than making evaluate itself async. Keeps the experiment runner sync.

When NOT to use this style

If the evaluator is a one-line check (exact_match, length_under_500), use function style — the class boilerplate adds noise. See references/evaluator-styles/function.md.

--evaluator-style function (default — what the notebooks use)

Plain Python functions with the signature (input_data, output_data, expected_output). Always emit at least three: a trivial boolean (returns bool), a richer rule-based one (returns EvaluatorResult), and an LLM-as-Judge surrogate (a RemoteEvaluator reference or a placeholder).

Code to emit

from ddtrace.llmobs import EvaluatorResult

# Trivial check — bare bool is fine here, the result has no extra signal.
def exact_match(input_data, output_data, expected_output) -> bool:
    return output_data == expected_output

# Richer check — use EvaluatorResult so reasoning/assessment surface in the UI.
def response_well_formed(input_data, output_data, expected_output) -> EvaluatorResult:
    if not isinstance(output_data, str):
        return EvaluatorResult(
            value=False,
            reasoning=f"output_data was {type(output_data).__name__}, expected str",
            assessment="fail",
        )
    if len(output_data) > 500:
        return EvaluatorResult(
            value=False,
            reasoning=f"output exceeded 500 chars (was {len(output_data)})",
            assessment="fail",
            metadata={"length": len(output_data)},
        )
    return EvaluatorResult(value=True, assessment="pass")

When to extend

• If the user passed --dataset with a structured expected_output, add a JSON-shape check (also returning EvaluatorResult).
• For LLM-as-Judge surrogates, prefer RemoteEvaluator references (server-side, scalable) over inline LLMJudge calls.

When NOT to use this style

If the evaluator needs persistent state (a model client, a cached lookup, an async I/O resource), use class style instead — BaseEvaluator.__init__ is where you set up state safely. See references/evaluator-styles/class.md.

--evaluator-style remote (LLM-as-Judge running server-side)

RemoteEvaluator instances that point at a judge configured in the Datadog UI. The judge LLM call runs on Datadog's side, not in the user's experiment process — useful when the judge is shared across experiments or has its own quota / model selection.

Code to emit

from ddtrace.llmobs import RemoteEvaluator

# Create the judge in Datadog UI first: Agent Observability → Evaluations → New Evaluator
quality_judge = RemoteEvaluator(eval_name="<name-from-datadog-ui>")

# Optional: customize the payload the judge receives
custom_judge = RemoteEvaluator(
    eval_name="<name>",
    transform_fn=lambda ctx: {
        "question": ctx.input_data.get("question"),
        "answer": ctx.output_data,
        "reference": ctx.expected_output,
    },
)

Setup the user has to do first

The judge must exist in the Datadog UI before the experiment runs. Emit a comment in the generated file telling the user:

# Before running this experiment:
#   1. Open Datadog → Agent Observability → Evaluations → Custom evaluators
#   2. Create an LLM-as-a-Judge evaluator. Note the eval_name you give it.
#   3. Paste that name into RemoteEvaluator(eval_name="...") below.

When to prefer remote over inline LLMJudge

• The same judge is reused across multiple experiments (single source of truth in the UI).
• The judge needs its own model/provider config that the experiment process doesn't have access to.
• The user wants to swap judges without changing experiment code.

For one-off rubrics tied to a single experiment, inline LLMJudge (under the function style) is simpler. See references/evaluator-styles/function.md.

When NOT to use this style

If the user doesn't have a judge configured in Datadog yet and won't set one up, fall back to function style with an LLMJudge placeholder — at least the experiment runs end-to-end.

Provider: Anthropic

Triggered by introspection (Workflow step 2.5) when the call-site function imports anthropic and calls one of:

• anthropic.messages.create
• client.messages.create (where client = anthropic.Anthropic(...))
• Anthropic(...).messages.create

{{PROVIDER_ASSERTS}} substitution

assert os.getenv("ANTHROPIC_API_KEY"), "ANTHROPIC_API_KEY is required for the wired task_fn."

Optional env vars (do NOT assert; document in # TODO comments)

• ANTHROPIC_BASE_URL — only needed when pointing at a proxy or self-hosted relay.

Adapter notes

• anthropic.Anthropic().messages.create(model=..., max_tokens=..., messages=[...]) returns a Message object. Extract text via .content[0].text (note: content is a list of blocks, not a single string).
• If the user's function returns the raw Message, wrap with a .content[0].text extractor in task_fn.
• max_tokens is required — unlike OpenAI, Anthropic raises if it's missing. If the user's function omits it, leave their signature alone; the call will fail at runtime and the user can fix.
• Async (AsyncAnthropic): wrap with asyncio.run(...) inside a sync task_fn.

Common gotchas

• Tool use response format differs from OpenAI — tool_use blocks are interleaved with text blocks in .content. Don't assume .content[0] is text; loop and concatenate text blocks if needed.
• Anthropic models require messages to alternate user/assistant (no two consecutive same-role messages). If the user's function builds messages, trust it; don't second-guess.