switchyard_libsy/algorithms/

llm_class.rs

// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
// SPDX-License-Identifier: Apache-2.0

//! Judge-backed capability, escalation, and custom-policy routing.

use std::collections::HashSet;
use std::sync::Arc;

use async_trait::async_trait;
use serde::{Deserialize, Deserializer};
use serde_json::Value;
use switchyard_protocol::{Category, ContentBlock, Message, Role};

mod decision;
use decision::DecisionClassifier;
pub use decision::DecisionJudgeConfig;

use super::escalation;
use super::fall_through::FallThrough;
use super::util::DEFAULT_JUDGE_MAX_OUTPUT_TOKENS;
use super::util::affinity::{AffinityRouter, ClassifyTrigger, is_user_turn};
use super::util::classifier_contract::{
    ClassifierContract, ClassifierContractConfig, ClassifierResponseFormat,
};
use super::util::escalation::EscalationJudgeConfig;
use super::util::llm_judge::{
    ClassifierInput, JsonSchemaDecoder, JudgeClassifier, JudgePolicy, JudgeRuntimeConfig,
    SerdeDecoder, StructuredJudge,
};
use super::util::target_selector::TargetSelectorPolicy;
use crate::core::algorithm::{Algorithm, Driver};
use crate::core::classifier::{Classification, Classifier, Score};
use crate::core::state::State;
use crate::{LibsyError, Result};
use switchyard_protocol::{Request, Response};

const PROMPT_TEMPLATE: &str = include_str!("../prompts/capability-classifier/prompt.md");
const SCHEMA_TEMPLATE: &str = include_str!("../prompts/capability-classifier/schema.json");
/// Telemetry label for this algorithm's spans, metrics, and logs.
const ALGORITHM_NAME: &str = "llm_task_classifier";

/// Restates the task after the conversation the judge is asked to route.
///
/// The rubric leads the request, which works while the payload is a single short
/// message. Once `recent_turn_window` includes real conversation, those instructions
/// sit far from the generation point and the judge sometimes *answers* the
/// conversation instead of classifying it — the reply then fails to parse, the
/// verdict is unavailable, and the turn silently falls back. Repeating the task at
/// the end is what reliably prevents that.
const TRAILING_ROUTING_INSTRUCTION: &str =
    "Route the conversation above. Output ONLY the routing JSON object, nothing else.";

#[derive(Deserialize)]
#[serde(deny_unknown_fields)]
struct TaskClassifierVerdict {
    crux: String,
    primary_rule: String,
    capability_boundary: String,
    p_solve: f64,
}

impl TaskClassifierVerdict {
    /// Rejects malformed or internally inconsistent verdicts before policy evaluation.
    fn is_valid(&self) -> bool {
        (0.0..=1.0).contains(&self.p_solve)
            && !self.crux.trim().is_empty()
            && matches!(
                (
                    self.primary_rule.as_str(),
                    self.capability_boundary.as_str()
                ),
                ("SUP-1" | "SUP-2" | "SUP-3" | "SUP-4" | "SUP-5", "supported")
                    | ("UNC-1" | "UNC-2", "uncertain")
                    | ("LIM-1" | "LIM-2", "unsupported")
                    | ("none", "unmatched")
            )
    }

    /// Returns the number of threshold steps assigned to this capability boundary.
    ///
    /// `unmatched_steps` comes from the route configuration: a request that matches no
    /// capability rule has no evidence behind its solve probability, so operators can
    /// require more confidence for it than for an uncertain verdict.
    fn boundary_steps(&self, unmatched_steps: u8) -> Option<u8> {
        match self.capability_boundary.as_str() {
            "supported" => Some(0),
            "uncertain" => Some(1),
            "unmatched" => Some(unmatched_steps),
            "unsupported" => Some(2),
            _ => None,
        }
    }
}

/// Keeps the opening task and the last `recent_turn_window` turns after it. A
/// window of `0` keeps the task alone.
///
/// Inbound decoders normalize client system and developer content into
/// `LlmRequest::instructions`, so it never reaches this list.
///
/// Selects by reference and clones only what survives — a coding-agent
/// conversation carries every tool result, so cloning it whole to keep a window
/// would copy the transcript on each judged turn.
fn trim_messages(messages: &[Message], recent_turn_window: usize) -> Vec<Message> {
    let is_instruction = |message: &Message| matches!(message.role, Role::System | Role::Developer);
    let mut kept: Vec<&Message> = messages.iter().filter(|m| is_instruction(m)).collect();
    let Some(task) = messages.iter().position(is_user_turn) else {
        return kept.into_iter().cloned().collect();
    };
    kept.push(&messages[task]);

    let tail: Vec<&Message> = messages[task + 1..]
        .iter()
        .filter(|m| !is_instruction(m))
        .collect();
    kept.extend(&tail[window_start(&tail, recent_turn_window)..]);
    kept.into_iter().cloned().collect()
}

/// The first index of the trailing window.
///
/// Counting messages alone can start the window between an assistant tool call and the
/// result answering it, leaving the judge a result whose call id was never introduced. The
/// start therefore moves back to the nearest one that keeps every tool pair whole.
///
/// One newest-to-oldest pass carries the ids still waiting for a call. Direction is what
/// makes it correct: ids repeat across a conversation, and in this order a call is only
/// ever seen after the results it could answer, so a later call — already passed — clears
/// nothing. A result whose call sits before the opening task, which trimming never reaches,
/// keeps the set non-empty to the end and falls back to the counted start, so an unpairable
/// result costs one pass and cannot widen the window to the whole conversation.
fn window_start(tail: &[&Message], recent_turn_window: usize) -> usize {
    let counted = tail.len().saturating_sub(recent_turn_window);
    // An empty window holds no result to pair, and the loop below never visits its start.
    if counted == tail.len() {
        return counted;
    }
    let mut unpaired: HashSet<&str> = HashSet::new();
    for (start, message) in tail.iter().enumerate().rev() {
        // Blocks reverse too, so a call answers a result only when it precedes it inside
        // one message as well as across messages.
        for block in message.content.iter().rev() {
            match block {
                ContentBlock::ToolResult(result) => {
                    unpaired.insert(result.tool_call_id.as_str());
                }
                ContentBlock::ToolCall(call) => {
                    unpaired.remove(call.id.as_str());
                }
                _ => {}
            }
        }
        if start <= counted && unpaired.is_empty() {
            return start;
        }
    }
    counted
}

/// Keeps the opening task and the latest user follow-up when they differ.
fn task_messages(messages: &[Message]) -> Vec<Message> {
    // Decoders also use the user role for tool results. Select ordinary user content
    // first, so a tool result cannot replace the opening task or latest follow-up.
    let is_task_content = |block: &ContentBlock| {
        !matches!(
            block,
            ContentBlock::ToolCall(_)
                | ContentBlock::ToolResult(_)
                | ContentBlock::Reasoning { .. }
        )
    };
    let mut user_messages = messages
        .iter()
        .filter(|message| is_user_turn(message) && message.content.iter().any(is_task_content));
    let Some(opening_task) = user_messages.next() else {
        return Vec::new();
    };
    [Some(opening_task), user_messages.next_back()]
        .into_iter()
        .flatten()
        .map(|message| Message {
            role: Role::User,
            content: message
                .content
                .iter()
                .filter(|block| is_task_content(block))
                .cloned()
                .collect(),
        })
        .collect()
}

/// Selects the task messages shown to capability and custom-schema classifiers.
struct TaskInput {
    recent_turn_window: Option<usize>,
}

impl TaskInput {
    /// Selects conversation content without adding judge instructions.
    fn messages(&self, request: &Request) -> Vec<Message> {
        // The default preserves the whole-task anchor and latest user update. A
        // configured window widens that to the surrounding conversation.
        let mut messages = match self.recent_turn_window {
            Some(window) => trim_messages(&request.llm_request.messages, window),
            None => task_messages(&request.llm_request.messages),
        };
        // Reasoning is provider-private and not required to classify the task. Some
        // upstreams also reject an unsigned reasoning item replayed without the
        // opaque state it was issued with, so it cannot travel through a windowed
        // classifier request as ordinary history.
        for message in &mut messages {
            message
                .content
                .retain(|block| !matches!(block, ContentBlock::Reasoning { .. }));
        }
        messages.retain(|message| !message.content.is_empty());
        messages
    }
}

impl ClassifierInput for TaskInput {
    fn build_messages(&self, _state: &State, request: &Request) -> Vec<Message> {
        let mut messages = self.messages(request);
        // Only the windowed path carries assistant turns and tool traffic for the judge
        // to be distracted by. The default path is user task messages only — the anchor
        // and the latest follow-up — so there is nothing there to outrank.
        if self.recent_turn_window.is_some() {
            messages.push(Message::text(
                Role::User,
                TRAILING_ROUTING_INSTRUCTION.to_string(),
            ));
        }
        messages
    }
}

struct TaskClassifierPolicy {
    base_threshold: f64,
    threshold_step: f64,
    unmatched_steps: u8,
}

impl TaskClassifierPolicy {
    fn new(config: &LlmCapabilityConfig) -> Self {
        Self {
            base_threshold: config.base_threshold,
            threshold_step: config.threshold_step,
            unmatched_steps: config.unmatched_steps,
        }
    }

    /// Returns the required solve probability for one validated verdict.
    fn threshold(&self, verdict: &TaskClassifierVerdict) -> Option<f64> {
        let steps = verdict.boundary_steps(self.unmatched_steps)?;
        Some(self.base_threshold + f64::from(steps) * self.threshold_step)
    }
}

impl JudgePolicy for TaskClassifierPolicy {
    type Verdict = TaskClassifierVerdict;

    fn to_classification(
        &self,
        verdict: Option<&Self::Verdict>,
        driver: &Driver,
    ) -> Result<Classification> {
        // Judge output is untrusted. An absent, invalid, or inconsistent verdict is
        // ambiguous so the surrounding router applies its configured fallback.
        let Some(verdict) = verdict.filter(|verdict| verdict.is_valid()) else {
            return Ok(Classification::Ambiguous(vec![]));
        };
        // A usable verdict below the capability threshold is still a decision: the judge
        // does not trust the efficient tier with this task.
        let Some(threshold) = self.threshold(verdict) else {
            return Ok(Classification::Ambiguous(vec![]));
        };
        let category = if verdict.p_solve >= threshold
            || (threshold - verdict.p_solve).abs() <= f64::EPSILON
        {
            Category::Efficient
        } else {
            Category::Capable
        };
        // The chosen category may have no models configured. That is ambiguous, not an
        // error, so the surrounding router applies its configured fallback.
        let Some(target) = driver.models_for(&category).first().cloned() else {
            return Ok(Classification::Ambiguous(vec![]));
        };
        Ok(Classification::Scores(vec![Score {
            target,
            confidence: 1.0,
            category: Some(category),
        }]))
    }
}

/// Maps valid verdicts to scores, invalid verdicts to a reason, and leaves absent verdicts alone.
fn capability_evidence(
    policy: &TaskClassifierPolicy,
    verdict: Option<&TaskClassifierVerdict>,
) -> Option<Value> {
    let verdict = verdict?;
    let Some(threshold) = verdict
        .is_valid()
        .then(|| policy.threshold(verdict))
        .flatten()
    else {
        return Some(serde_json::json!({
            "source": "fail_open",
            "reason_code": "invalid_verdict",
        }));
    };
    Some(serde_json::json!({
        "source": "llm-classifier",
        "score": verdict.p_solve,
        "threshold": threshold,
    }))
}

/// Settings shared by capability judges and their surrounding route.
#[derive(Clone, Debug)]
pub struct TaskClassifierConfig {
    /// The judge's prediction contract and routing policy.
    pub judge: CapabilityJudgeConfig,
    /// Routes to the capable tier on judge client failures and deadlines. Defaults to true.
    pub fail_open: bool,
    /// How often the classifier re-decides this session's target.
    pub classify_trigger: ClassifyTrigger,
    /// Uses the first user message as the SessionKey for sticky routing when session metadata is unavailable.
    pub message_hash_fallback: bool,
    /// `None` judges the opening task and latest user follow-up. `Some(n)` also
    /// includes the surrounding conversation using the last `n` turns.
    pub recent_turn_window: Option<usize>,
}

/// Selects how a capability judge obtains and interprets its prediction.
#[derive(Clone, Debug)]
pub enum CapabilityJudgeConfig {
    /// A structured LLM verdict with a solve probability and capability boundary.
    Llm(LlmCapabilityConfig),
    /// A Choice score for the capable candidate succeeding where the efficient one fails.
    Decision(DecisionJudgeConfig),
}

impl Default for CapabilityJudgeConfig {
    fn default() -> Self {
        Self::Llm(LlmCapabilityConfig::default())
    }
}

/// Prompt, output, and solve-probability settings for an LLM capability judge.
#[derive(Clone, Debug)]
pub struct LlmCapabilityConfig {
    /// Lowest solve probability that routes a supported task to the efficient target.
    pub base_threshold: f64,
    /// Amount added per capability-boundary step: zero for supported, one for
    /// uncertain, `unmatched_steps` for unmatched, and two for unsupported.
    pub threshold_step: f64,
    /// Threshold steps applied to an unmatched verdict (no capability rule applies).
    ///
    /// Defaults to 1, the same as an uncertain verdict. 2 treats it like unsupported.
    pub unmatched_steps: u8,
    /// Prompt and verdict contract settings for the classifier judge.
    pub contract: ClassifierContractConfig,
    /// Maximum completion tokens available to the classifier verdict.
    pub max_output_tokens: u64,
}

impl Default for LlmCapabilityConfig {
    fn default() -> Self {
        Self {
            base_threshold: 0.0,
            threshold_step: 0.0,
            unmatched_steps: DEFAULT_UNMATCHED_STEPS,
            contract: ClassifierContractConfig::default(),
            max_output_tokens: DEFAULT_JUDGE_MAX_OUTPUT_TOKENS,
        }
    }
}

/// Flat serialized shape that maps prompt settings into the runtime contract.
#[derive(Deserialize)]
#[serde(deny_unknown_fields)]
struct TaskClassifierConfigWire {
    base_threshold: f64,
    #[serde(default = "default_fail_open")]
    fail_open: bool,
    #[serde(default)]
    threshold_step: f64,
    #[serde(default = "default_unmatched_steps")]
    unmatched_steps: u8,
    #[serde(default)]
    classify_trigger: ClassifyTrigger,
    #[serde(default)]
    message_hash_fallback: bool,
    #[serde(default)]
    recent_turn_window: Option<usize>,
    #[serde(default)]
    prompt: Option<String>,
    #[serde(default)]
    response_format_type: ClassifierResponseFormat,
    #[serde(default = "default_judge_max_output_tokens")]
    max_output_tokens: u64,
}

impl<'de> Deserialize<'de> for TaskClassifierConfig {
    fn deserialize<D>(deserializer: D) -> std::result::Result<Self, D::Error>
    where
        D: Deserializer<'de>,
    {
        let wire = TaskClassifierConfigWire::deserialize(deserializer)?;
        let mut contract = ClassifierContractConfig::default();
        if let Some(prompt) = wire.prompt {
            contract = contract.with_prompt(prompt);
        }
        contract = contract.with_response_format_type(wire.response_format_type);
        Ok(Self {
            judge: CapabilityJudgeConfig::Llm(LlmCapabilityConfig {
                base_threshold: wire.base_threshold,
                threshold_step: wire.threshold_step,
                unmatched_steps: wire.unmatched_steps,
                contract,
                max_output_tokens: wire.max_output_tokens,
            }),
            fail_open: wire.fail_open,
            classify_trigger: wire.classify_trigger,
            message_hash_fallback: wire.message_hash_fallback,
            recent_turn_window: wire.recent_turn_window,
        })
    }
}

const fn default_fail_open() -> bool {
    true
}

/// Unmatched verdicts share the uncertain step unless the route says otherwise.
pub const DEFAULT_UNMATCHED_STEPS: u8 = 1;

const fn default_unmatched_steps() -> u8 {
    DEFAULT_UNMATCHED_STEPS
}

const fn default_judge_max_output_tokens() -> u64 {
    DEFAULT_JUDGE_MAX_OUTPUT_TOKENS
}

impl Default for TaskClassifierConfig {
    fn default() -> Self {
        Self {
            judge: CapabilityJudgeConfig::default(),
            fail_open: default_fail_open(),
            classify_trigger: ClassifyTrigger::default(),
            message_hash_fallback: false,
            recent_turn_window: None,
        }
    }
}

impl LlmCapabilityConfig {
    fn validate(&self) -> Result<()> {
        if !(0.0..=1.0).contains(&self.base_threshold) {
            return Err(LibsyError::AlgorithmError {
                message: format!(
                    "base_threshold must be between 0 and 1, got {}",
                    self.base_threshold
                ),
            });
        }
        if !self.threshold_step.is_finite() || self.threshold_step < 0.0 {
            return Err(LibsyError::AlgorithmError {
                message: format!(
                    "threshold_step must be finite and greater than or equal to 0, got {}",
                    self.threshold_step
                ),
            });
        }
        if self.unmatched_steps > 2 {
            return Err(LibsyError::AlgorithmError {
                message: format!(
                    "unmatched_steps must be 0, 1, or 2, got {}",
                    self.unmatched_steps
                ),
            });
        }
        let unsupported_threshold = self.base_threshold + 2.0 * self.threshold_step;
        if unsupported_threshold > 1.0 && unsupported_threshold - 1.0 > f64::EPSILON {
            return Err(LibsyError::AlgorithmError {
                message: format!(
                    "base_threshold + 2 * threshold_step must be at most 1, got {unsupported_threshold}"
                ),
            });
        }
        if self.max_output_tokens == 0 {
            return Err(LibsyError::AlgorithmError {
                message: "max_output_tokens must be at least 1".to_string(),
            });
        }
        Ok(())
    }
}

impl TaskClassifierConfig {
    fn validate(&self) -> Result<()> {
        match &self.judge {
            CapabilityJudgeConfig::Llm(config) => config.validate()?,
            CapabilityJudgeConfig::Decision(config) => config.validate()?,
        }
        // Only `every_request` is rejected: it retains no target, so a fallback identity has nothing to key on. Both retaining triggers can key the retained target on a message hash when the caller sends no session id.
        if self.message_hash_fallback && self.classify_trigger == ClassifyTrigger::EveryRequest {
            return Err(LibsyError::AlgorithmError {
                message:
                    "message_hash_fallback requires classify_trigger = new_session or user_turn"
                        .to_string(),
            });
        }
        Ok(())
    }
}

/// Policy that maps a custom classifier verdict to a routing target.
#[derive(Clone, Debug)]
pub enum CustomClassifierPolicy {
    /// Resolves a JSON Pointer and treats its string value as a model category.
    TargetSelector {
        /// JSON Pointer evaluated against each schema-validated verdict.
        selector: String,
    },
}

impl CustomClassifierPolicy {
    /// Creates a policy that selects a model category through a JSON Pointer.
    pub fn target_selector(selector: impl Into<String>) -> Self {
        Self::TargetSelector {
            selector: selector.into(),
        }
    }
}

/// Settings for a classifier whose JSON Schema and target-selection policy are user supplied.
#[derive(Clone, Debug)]
pub struct CustomClassifierConfig {
    /// System prompt sent to the classifier judge.
    pub prompt: String,
    /// Inner JSON Schema placed inside the provider's structured-output wrapper.
    pub response_schema: Value,
    /// Deterministic policy applied after the verdict passes schema validation.
    pub policy: CustomClassifierPolicy,
    /// How often the classifier re-decides this session's target.
    pub classify_trigger: ClassifyTrigger,
    /// Uses the first user message when session metadata is unavailable.
    pub message_hash_fallback: bool,
    /// Trailing conversation turns shown to the classifier judge.
    pub recent_turn_window: Option<usize>,
    /// Maximum completion tokens available to the classifier verdict.
    pub max_output_tokens: u64,
}

impl CustomClassifierConfig {
    /// Creates a custom-schema classifier contract with conservative runtime defaults.
    pub fn new(
        prompt: impl Into<String>,
        response_schema: Value,
        policy: CustomClassifierPolicy,
    ) -> Self {
        Self {
            prompt: prompt.into(),
            response_schema,
            policy,
            classify_trigger: ClassifyTrigger::default(),
            message_hash_fallback: false,
            recent_turn_window: None,
            max_output_tokens: DEFAULT_JUDGE_MAX_OUTPUT_TOKENS,
        }
    }

    fn validate(&self) -> Result<()> {
        if self.max_output_tokens == 0 {
            return Err(LibsyError::AlgorithmError {
                message: "max_output_tokens must be at least 1".to_string(),
            });
        }
        // Only `every_request` is rejected: it retains no target, so a fallback identity has nothing to key on. Both retaining triggers can key the retained target on a message hash when the caller sends no session id.
        if self.message_hash_fallback && self.classify_trigger == ClassifyTrigger::EveryRequest {
            return Err(LibsyError::AlgorithmError {
                message:
                    "message_hash_fallback requires classify_trigger = new_session or user_turn"
                        .to_string(),
            });
        }
        Ok(())
    }
}

enum CustomPolicyRuntime {
    TargetSelector(TargetSelectorPolicy),
}

impl JudgePolicy for CustomPolicyRuntime {
    type Verdict = Value;

    fn to_classification(
        &self,
        verdict: Option<&Self::Verdict>,
        driver: &Driver,
    ) -> Result<Classification> {
        match self {
            Self::TargetSelector(policy) => policy.to_classification(verdict, driver),
        }
    }
}

/// Builds the affinity router a trigger calls for, if any.
fn affinity_router(
    trigger: ClassifyTrigger,
    message_hash_fallback: bool,
) -> Option<Arc<AffinityRouter>> {
    let router = match trigger {
        ClassifyTrigger::EveryRequest => return None,
        ClassifyTrigger::NewSession => AffinityRouter::new(),
        ClassifyTrigger::UserTurn => AffinityRouter::new().with_release_on_user_turn(),
    };
    let router = if message_hash_fallback {
        router.with_message_hash_fallback()
    } else {
        router
    };
    Some(Arc::new(router))
}

/// Routes requests through a capability, escalation, or custom classifier mode.
pub struct LlmTaskClassifier {
    route: FallThrough,
    /// Classifier used when this router is embedded in another cascade.
    inner: Arc<dyn Classifier>,
}

struct ClassifierRouteConfig {
    default_target: Category,
    classify_trigger: ClassifyTrigger,
    message_hash_fallback: bool,
}

/// Terminal classifier for a cascade whose classifiers may all abstain.
/// Closes a cascade with the first runtime model in `category`.
pub struct DefaultCategoryClassifier(pub Category);

#[async_trait]
impl Classifier for DefaultCategoryClassifier {
    async fn score(
        &self,
        _state: &mut State,
        _request: &mut Request,
        driver: &Driver,
    ) -> Result<(Classification, Option<Response>)> {
        let target = driver.first_model_for(&self.0)?;
        driver.set_evidence_if_empty(serde_json::json!({"source": "fall_open"}));
        Ok((
            Classification::Scores(vec![Score {
                target: target.clone(),
                confidence: 0.0,
                category: Some(self.0.clone()),
            }]),
            None,
        ))
    }
}

/// Complete construction settings for one LLM classifier mode.
#[derive(Clone)]
#[non_exhaustive]
pub enum LlmClassifierConfig {
    /// Routes between efficient and capable targets from a task-level verdict.
    Capability {
        /// Capability classifier settings.
        config: TaskClassifierConfig,
    },
    /// Judges efficient responses and escalates after a confirmed streak.
    Escalation {
        /// Prompt and verdict contract settings for the escalation judge.
        contract: ClassifierContractConfig,
        /// Escalation policy settings.
        config: EscalationJudgeConfig,
        /// Maximum completion tokens available to the escalation verdict.
        max_output_tokens: u64,
    },
    /// Routes among model categories using a user-supplied schema and policy.
    Custom {
        /// Category selected when the judge does not produce a usable verdict.
        default_target: Category,
        /// Custom classifier settings.
        config: CustomClassifierConfig,
    },
}

impl LlmTaskClassifier {
    /// Builds the classifier mode described by `config`.
    ///
    /// # Errors
    ///
    /// Returns an error when the selected mode's targets, contract, policy, or runtime
    /// settings are invalid.
    pub fn new(config: LlmClassifierConfig) -> Result<Self> {
        match config {
            LlmClassifierConfig::Capability { config } => Self::build_capability(config),
            LlmClassifierConfig::Escalation {
                contract,
                config,
                max_output_tokens,
            } => Self::build_escalation(contract, config, max_output_tokens),
            LlmClassifierConfig::Custom {
                default_target,
                config,
            } => Self::build_custom(default_target, config),
        }
    }

    fn build_capability(config: TaskClassifierConfig) -> Result<Self> {
        config.validate()?;
        let classify_trigger = config.classify_trigger;
        let message_hash_fallback = config.message_hash_fallback;
        let input = TaskInput {
            recent_turn_window: config.recent_turn_window,
        };
        let classifier: Arc<dyn Classifier> = match config.judge {
            CapabilityJudgeConfig::Llm(judge) => Arc::new(
                JudgeClassifier::new(
                    StructuredJudge::new(
                        input,
                        Self::load_capability_contract(&judge.contract)?,
                        SerdeDecoder::new(),
                        JudgeRuntimeConfig::new(judge.max_output_tokens)?,
                    ),
                    TaskClassifierPolicy::new(&judge),
                )
                .with_error_recovery(config.fail_open)
                .with_evidence(capability_evidence),
            ),
            CapabilityJudgeConfig::Decision(judge) => {
                Arc::new(DecisionClassifier::new(judge, input, config.fail_open)?)
            }
        };
        Self::from_classifier(
            classifier,
            ClassifierRouteConfig {
                default_target: Category::Capable,
                classify_trigger,
                message_hash_fallback,
            },
        )
    }

    fn build_custom(default_target: Category, config: CustomClassifierConfig) -> Result<Self> {
        Self::from_classifier(
            Self::custom_classifier(&config, false)?,
            ClassifierRouteConfig {
                default_target,
                classify_trigger: config.classify_trigger,
                message_hash_fallback: config.message_hash_fallback,
            },
        )
    }

    /// The judge face alone, for composite's own trigger and retained tier.
    pub(crate) fn custom_classifier(
        config: &CustomClassifierConfig,
        fail_open: bool,
    ) -> Result<Arc<dyn Classifier>> {
        config.validate()?;
        let contract =
            ClassifierContract::from_inner_schema(&config.prompt, config.response_schema.clone())?;
        let policy = match &config.policy {
            CustomClassifierPolicy::TargetSelector { selector } => {
                CustomPolicyRuntime::TargetSelector(TargetSelectorPolicy::new(selector)?)
            }
        };
        let classifier = JudgeClassifier::new(
            StructuredJudge::new(
                TaskInput {
                    recent_turn_window: config.recent_turn_window,
                },
                contract,
                JsonSchemaDecoder::new(),
                JudgeRuntimeConfig::new(config.max_output_tokens)?,
            ),
            policy,
        )
        .with_error_recovery(fail_open);
        let classifier = if fail_open {
            classifier.with_evidence(|_, verdict| {
                verdict.map(|_| serde_json::json!({"source": "llm-classifier"}))
            })
        } else {
            classifier
        };
        Ok(Arc::new(classifier))
    }

    fn build_escalation(
        contract_config: ClassifierContractConfig,
        config: EscalationJudgeConfig,
        max_output_tokens: u64,
    ) -> Result<Self> {
        let inner = escalation::build_classifier(contract_config, config, max_output_tokens)?;
        Ok(Self {
            route: FallThrough::new_with_state()
                .with_name(ALGORITHM_NAME)
                .with_classifier(Arc::clone(&inner)),
            inner,
        })
    }

    /// Loads the packaged capability-classifier contract.
    fn load_capability_contract(config: &ClassifierContractConfig) -> Result<ClassifierContract> {
        ClassifierContract::from_config(config, PROMPT_TEMPLATE, SCHEMA_TEMPLATE)
    }

    /// Keeps affinity and fallback ordering identical across judge-backed modes.
    fn from_classifier(inner: Arc<dyn Classifier>, config: ClassifierRouteConfig) -> Result<Self> {
        // Only `every_request` is rejected: it retains no target, so a fallback identity has nothing to key on. Both retaining triggers can key the retained target on a message hash when the caller sends no session id.
        if config.message_hash_fallback && config.classify_trigger == ClassifyTrigger::EveryRequest
        {
            return Err(LibsyError::AlgorithmError {
                message:
                    "message_hash_fallback requires classify_trigger = new_session or user_turn"
                        .to_string(),
            });
        }
        // Affinity comes first so a retained assignment short-circuits the judge call.
        let mut route = FallThrough::new_with_state().with_name(ALGORITHM_NAME);
        if let Some(affinity) =
            affinity_router(config.classify_trigger, config.message_hash_fallback).as_ref()
        {
            // Both roles must share one `Arc` so the classifier reads what the processor wrote.
            route = route
                .with_processor(affinity.clone())
                .with_classifier(affinity.clone());
        }
        let fallback = DefaultCategoryClassifier(config.default_target);
        Ok(Self {
            route: route
                .with_classifier(inner.clone())
                .with_classifier(Arc::new(fallback)),
            inner,
        })
    }
}

#[async_trait]
impl Classifier for LlmTaskClassifier {
    async fn score(
        &self,
        state: &mut State,
        request: &mut Request,
        driver: &Driver,
    ) -> Result<(Classification, Option<Response>)> {
        self.inner.score(state, request, driver).await
    }
}

#[async_trait]
impl Algorithm for LlmTaskClassifier {
    fn name(&self) -> &str {
        "llm_task_classifier"
    }

    async fn route(
        self: Arc<Self>,
        driver: Driver,
        request: Request,
    ) -> Result<crate::RoutingOutcome> {
        self.route.execute(driver, request).await
    }
}

#[cfg(test)]
mod tests {
    use std::collections::HashMap;
    use std::sync::Arc;

    use parking_lot::Mutex;
    use serde_json::Value;

    use super::*;
    use switchyard_protocol::{
        ContentBlock, InstructionBlock, LlmClientError, LlmRequest, Metadata, ModelId, ToolCall,
        ToolResult, completion_text, text_request, text_response,
    };

    use crate::algorithms::util::llm_judge::Judge;
    use crate::core::testing::{Serve, test_drive_with_models};
    use switchyard_protocol::{LlmResponse, Response};

    const TEST_THRESHOLD: f64 = 0.5;

    type CapabilityJudge = StructuredJudge<TaskInput, SerdeDecoder<TaskClassifierVerdict>>;

    fn llm_config(base_threshold: f64) -> LlmCapabilityConfig {
        LlmCapabilityConfig {
            base_threshold,
            ..LlmCapabilityConfig::default()
        }
    }

    fn test_config(base_threshold: f64) -> TaskClassifierConfig {
        TaskClassifierConfig {
            judge: CapabilityJudgeConfig::Llm(llm_config(base_threshold)),
            ..TaskClassifierConfig::default()
        }
    }

    fn policy() -> TaskClassifierPolicy {
        TaskClassifierPolicy::new(&llm_config(TEST_THRESHOLD))
    }

    fn runtime_models() -> HashMap<Category, Vec<ModelId>> {
        [
            (Category::Judge, vec![ModelId::from("judge")]),
            (Category::Efficient, vec![ModelId::from("efficient")]),
            (Category::Capable, vec![ModelId::from("capable")]),
            (
                Category::Any,
                vec![ModelId::from("efficient"), ModelId::from("capable")],
            ),
        ]
        .into()
    }

    fn policy_driver() -> Driver {
        Driver::new("test", Arc::new(runtime_models().into())).0
    }

    fn verdict(
        p_solve: f64,
        capability_boundary: &str,
        primary_rule: &str,
    ) -> TaskClassifierVerdict {
        TaskClassifierVerdict {
            crux: "test crux".to_string(),
            primary_rule: primary_rule.to_string(),
            capability_boundary: capability_boundary.to_string(),
            p_solve,
        }
    }

    fn selected(
        policy: &TaskClassifierPolicy,
        verdict: Option<&TaskClassifierVerdict>,
    ) -> Result<ModelId> {
        policy
            .to_classification(verdict, &policy_driver())?
            .argmax(false)?
            .map(|score| score.target)
            .ok_or_else(|| LibsyError::AlgorithmError {
                message: "policy abstained".to_string(),
            })
    }

    /// Records what each target received; answers the judge with a supported verdict and
    /// every other target with a plain completion.
    #[derive(Default)]
    struct Recorder {
        calls: Mutex<Vec<String>>,
        call_roles: Mutex<Vec<(String, bool)>>,
        judge_max_output_tokens: Mutex<Vec<Option<u64>>>,
        judge_system_prompts: Mutex<Vec<String>>,
    }

    impl Recorder {
        fn calls(&self) -> Vec<String> {
            self.calls.lock().clone()
        }

        fn call_roles(&self) -> Vec<(String, bool)> {
            self.call_roles.lock().clone()
        }

        fn judge_max_output_tokens(&self) -> Vec<Option<u64>> {
            self.judge_max_output_tokens.lock().clone()
        }

        fn judge_system_prompts(&self) -> Vec<String> {
            self.judge_system_prompts.lock().clone()
        }

        fn serve(self: &Arc<Self>) -> impl Serve {
            let recorder = Arc::clone(self);
            move |model: ModelId, request: Request| {
                let recorder = Arc::clone(&recorder);
                async move {
                    let model = model.to_string();
                    recorder.calls.lock().push(model.clone());
                    recorder
                        .call_roles
                        .lock()
                        .push((model.clone(), model != "judge"));
                    let completion = if model == "judge" {
                        recorder
                            .judge_max_output_tokens
                            .lock()
                            .push(request.llm_request.output.max_output_tokens);
                        recorder.judge_system_prompts.lock().extend(
                            request
                                .llm_request
                                .instructions
                                .first()
                                .and_then(|instruction| {
                                    instruction.content.iter().find_map(|b| {
                                        if let ContentBlock::Text { text } = b {
                                            Some(text.clone())
                                        } else {
                                            None
                                        }
                                    })
                                }),
                        );
                        r#"{"crux":"bounded task","primary_rule":"SUP-1","capability_boundary":"supported","p_solve":0.9}"#.to_string()
                    } else {
                        format!("answer from {model}")
                    };
                    Ok(Response {
                        llm_response: LlmResponse::Agg(text_response(None, completion)),
                        metadata: request.metadata,
                        upstream_headers: http::HeaderMap::new(),
                    })
                }
            }
        }
    }

    /// The judge times out; every other target answers normally.
    fn unreachable_judge() -> impl Serve {
        |model: ModelId, request: Request| async move {
            let model = model.to_string();
            if model == "judge" {
                return Err(LlmClientError::Timeout {
                    source: Box::new(std::io::Error::other("judge unreachable")),
                });
            }
            Ok(Response {
                llm_response: LlmResponse::Agg(text_response(None, format!("answer from {model}"))),
                metadata: request.metadata,
                upstream_headers: http::HeaderMap::new(),
            })
        }
    }

    fn router() -> Result<Arc<LlmTaskClassifier>> {
        Ok(Arc::new(LlmTaskClassifier::new(
            LlmClassifierConfig::Capability {
                config: test_config(TEST_THRESHOLD),
            },
        )?))
    }

    fn classify_request() -> Request {
        Request {
            llm_request: text_request(Some("auto".to_string()), "classify this task"),
            raw_request: None,
            metadata: None,
        }
    }

    fn classify_session_request() -> Request {
        Request {
            metadata: Some(Metadata {
                session_id: Some("session-1".to_string()),
                ..Metadata::default()
            }),
            ..classify_request()
        }
    }

    fn classify_follow_up_request() -> Request {
        let mut request = classify_request();
        request
            .llm_request
            .messages
            .push(Message::text(Role::Assistant, "I will add the test."));
        request.llm_request.messages.push(Message::text(
            Role::User,
            "Now run the test suite and report the result.",
        ));
        request
    }

    #[tokio::test]
    async fn an_unreachable_judge_routes_capable_instead_of_failing_the_request() -> Result<()> {
        let router = router()?;

        let (selected_model, response) = test_drive_with_models(
            router,
            classify_request(),
            runtime_models(),
            unreachable_judge(),
        )
        .await?;

        assert_eq!(selected_model, "capable");
        assert_eq!(
            response.llm_response.as_agg().map(completion_text),
            Some("answer from capable".to_string())
        );
        Ok(())
    }

    #[tokio::test]
    async fn classifier_judges_each_request_without_affinity() -> Result<()> {
        let recorder = Arc::new(Recorder::default());
        let router = router()?;
        let request = classify_request();
        let models = runtime_models();

        test_drive_with_models(
            router.clone(),
            request.clone(),
            models.clone(),
            recorder.serve(),
        )
        .await?;
        test_drive_with_models(router, request, models, recorder.serve()).await?;

        assert_eq!(
            recorder.calls(),
            vec!["judge", "efficient", "judge", "efficient"]
        );
        assert_eq!(
            recorder.call_roles(),
            vec![
                ("judge".to_string(), false),
                ("efficient".to_string(), true),
                ("judge".to_string(), false),
                ("efficient".to_string(), true),
            ]
        );
        Ok(())
    }

    #[tokio::test]
    async fn classifier_config_sets_the_judge_completion_cap() -> Result<()> {
        let recorder = Arc::new(Recorder::default());
        let router = Arc::new(LlmTaskClassifier::new(LlmClassifierConfig::Capability {
            config: TaskClassifierConfig {
                judge: CapabilityJudgeConfig::Llm(LlmCapabilityConfig {
                    max_output_tokens: 512,
                    ..llm_config(TEST_THRESHOLD)
                }),
                ..test_config(TEST_THRESHOLD)
            },
        })?);

        test_drive_with_models(
            router,
            classify_request(),
            runtime_models(),
            recorder.serve(),
        )
        .await?;

        assert_eq!(recorder.judge_max_output_tokens(), vec![Some(512)]);
        Ok(())
    }

    #[tokio::test]
    async fn classifier_config_overrides_the_packaged_prompt() -> Result<()> {
        let recorder = Arc::new(Recorder::default());
        let router = Arc::new(LlmTaskClassifier::new(LlmClassifierConfig::Capability {
            config: TaskClassifierConfig {
                judge: CapabilityJudgeConfig::Llm(LlmCapabilityConfig {
                    contract: ClassifierContractConfig::default()
                        .with_prompt("Custom capability rubric."),
                    ..llm_config(TEST_THRESHOLD)
                }),
                ..test_config(TEST_THRESHOLD)
            },
        })?);

        test_drive_with_models(
            router,
            classify_request(),
            runtime_models(),
            recorder.serve(),
        )
        .await?;

        let prompts = recorder.judge_system_prompts();
        assert_eq!(prompts.len(), 1);
        assert_eq!(prompts[0], "Custom capability rubric.");
        Ok(())
    }

    #[tokio::test]
    async fn classifier_config_enables_new_session_trigger() -> Result<()> {
        let recorder = Arc::new(Recorder::default());
        let router = Arc::new(LlmTaskClassifier::new(LlmClassifierConfig::Capability {
            config: TaskClassifierConfig {
                classify_trigger: ClassifyTrigger::NewSession,
                ..test_config(TEST_THRESHOLD)
            },
        })?);

        let request = classify_session_request();
        let models = runtime_models();
        test_drive_with_models(
            router.clone(),
            request.clone(),
            models.clone(),
            recorder.serve(),
        )
        .await?;
        test_drive_with_models(router, request, models, recorder.serve()).await?;

        assert_eq!(recorder.calls(), vec!["judge", "efficient", "efficient"]);
        Ok(())
    }

    #[tokio::test]
    async fn classifier_config_reuses_message_hash_affinity_for_a_follow_up() -> Result<()> {
        let recorder = Arc::new(Recorder::default());
        let router = Arc::new(LlmTaskClassifier::new(LlmClassifierConfig::Capability {
            config: TaskClassifierConfig {
                classify_trigger: ClassifyTrigger::NewSession,
                message_hash_fallback: true,
                recent_turn_window: None,
                ..test_config(TEST_THRESHOLD)
            },
        })?);

        let models = runtime_models();
        test_drive_with_models(
            router.clone(),
            classify_request(),
            models.clone(),
            recorder.serve(),
        )
        .await?;
        test_drive_with_models(
            router,
            classify_follow_up_request(),
            models,
            recorder.serve(),
        )
        .await?;

        assert_eq!(recorder.calls(), vec!["judge", "efficient", "efficient"]);
        Ok(())
    }

    #[tokio::test]
    async fn one_classifier_uses_each_requests_runtime_models() -> Result<()> {
        let router = Arc::new(LlmTaskClassifier::new(LlmClassifierConfig::Capability {
            config: TaskClassifierConfig {
                classify_trigger: ClassifyTrigger::NewSession,
                ..test_config(TEST_THRESHOLD)
            },
        })?);
        let calls = Arc::new(Mutex::new(Vec::new()));
        let serve = |calls: Arc<Mutex<Vec<String>>>| {
            move |model: ModelId, _request: Request| {
                let calls = Arc::clone(&calls);
                async move {
                    calls.lock().push(model.to_string());
                    let text = if model.as_str().starts_with("judge-") {
                        r#"{"crux":"bounded task","primary_rule":"SUP-1","capability_boundary":"supported","p_solve":0.9}"#.to_string()
                    } else {
                        model.to_string()
                    };
                    Ok(Response {
                        llm_response: LlmResponse::Agg(text_response(None, text)),
                        metadata: None,
                        upstream_headers: Default::default(),
                    })
                }
            }
        };
        let models = |suffix: &str| -> HashMap<Category, Vec<ModelId>> {
            [
                (
                    Category::Judge,
                    vec![ModelId::from(format!("judge-{suffix}"))],
                ),
                (
                    Category::Efficient,
                    vec![ModelId::from(format!("efficient-{suffix}"))],
                ),
                (
                    Category::Capable,
                    vec![ModelId::from(format!("capable-{suffix}"))],
                ),
                (
                    Category::Any,
                    vec![
                        ModelId::from(format!("efficient-{suffix}")),
                        ModelId::from(format!("capable-{suffix}")),
                    ],
                ),
            ]
            .into()
        };
        let request = classify_session_request();

        let (first, _) = test_drive_with_models(
            router.clone(),
            request.clone(),
            models("a"),
            serve(Arc::clone(&calls)),
        )
        .await?;
        let (second, _) =
            test_drive_with_models(router, request, models("b"), serve(Arc::clone(&calls))).await?;

        assert_eq!(first, "efficient-a");
        assert_eq!(second, "efficient-b");
        assert_eq!(
            &*calls.lock(),
            &["judge-a", "efficient-a", "judge-b", "efficient-b"]
        );
        Ok(())
    }

    #[tokio::test]
    async fn decision_judge_routes_on_relative_advantage() -> Result<()> {
        use crate::{Call, RuntimeModels, drive};
        use serde_json::json;
        use std::collections::BTreeMap;
        use std::sync::atomic::{AtomicUsize, Ordering};
        use switchyard_protocol::{
            BooleanEstimate, DecisionAnswer, DecisionResponse, DecisionValue, Probability,
            ProviderConfidence,
        };

        let settings = DecisionJudgeConfig {
            cutoff: 0.4,
            instructions: None,
            criteria: None,
            candidates: BTreeMap::from([
                ("a".into(), "capable".into()),
                ("b".into(), "efficient".into()),
                ("c".into(), "third".into()),
            ]),
            evidence: json!({
                "candidate_descriptions": {"a": "Profile A", "b": "Profile B", "c": "Profile C"},
                "reference_cases": [{"task": "Independent example", "observed_solved": {"a": true, "b": null, "c": false}}],
                "summaries": [],
            }),
        };
        let mut request = classify_session_request();
        request.llm_request.messages.push(Message {
            role: Role::Assistant,
            content: vec![
                ContentBlock::Reasoning {
                    text: "private reasoning".into(),
                    signature: None,
                    details: vec![],
                },
                ContentBlock::Text {
                    text: "visible progress".into(),
                },
            ],
        });
        request
            .llm_request
            .messages
            .push(Message::text(Role::User, "latest follow-up"));
        let original_messages = request.llm_request.messages.clone();
        let expected_task = json!([
            original_messages[0].clone(),
            Message::text(Role::Assistant, "visible progress"),
            Message::text(Role::User, "latest follow-up"),
        ]);

        for (name, score, fail_open, expected) in [
            ("above", 0.45, true, Some("capable")),
            ("equal", 0.4, true, Some("efficient")),
            ("below", 0.39, false, Some("efficient")),
            ("missing answer", 0.0, true, Some("capable")),
            ("no distribution", 0.0, true, Some("capable")),
            ("wrong type", 0.0, true, Some("capable")),
            ("out of range", 1.1, true, Some("capable")),
            ("nonfinite", f64::NAN, false, Some("capable")),
            ("provider error", 0.0, true, Some("capable")),
            ("provider error", 0.0, false, None),
            ("dropped reply", 0.0, true, Some("capable")),
            ("missing candidate", 0.0, true, None),
        ] {
            let mut config = settings.clone();
            if name == "missing candidate" {
                config.candidates.remove("a");
            }
            if name == "below" {
                config.instructions = Some(
                    json!({"policy": "Judge capable-only success using the supplied evidence."}),
                );
            }
            let override_instructions = config.instructions.clone();
            let router: Arc<dyn Algorithm> =
                Arc::new(LlmTaskClassifier::new(LlmClassifierConfig::Capability {
                    config: TaskClassifierConfig {
                        judge: CapabilityJudgeConfig::Decision(config),
                        fail_open,
                        recent_turn_window: None,
                        classify_trigger: ClassifyTrigger::NewSession,
                        ..TaskClassifierConfig::default()
                    },
                })?);
            let calls = AtomicUsize::new(0);
            let serve = |call| {
                let calls = &calls;
                let expected_task = &expected_task;
                let settings = &settings;
                let override_instructions = &override_instructions;
                async move {
                    let Call::Decision(call) = call else {
                        panic!("judge must use the decision step")
                    };
                    calls.fetch_add(1, Ordering::SeqCst);
                    assert_eq!(call.model, "judge");
                    assert_eq!(call.request.model, Some("judge".into()));
                    assert_eq!(
                        call.request.context,
                        json!({
                            "task": expected_task,
                            "session": "session-1",
                            "candidates": ["a", "b", "c"],
                            "comparison": {"capable": "a", "efficient": "b"},
                            "evidence": settings.evidence,
                        })
                    );
                    assert_eq!(call.request.questions.len(), 1);
                    let question = &call.request.questions["route"];
                    let switchyard_protocol::DecisionKind::Choice { options } = &question.kind
                    else {
                        panic!("expected a choice")
                    };
                    assert_eq!(
                        options
                            .iter()
                            .map(|option| option.id.as_str())
                            .collect::<Vec<_>>(),
                        ["advantage", "no_advantage"]
                    );
                    if let Some(instructions) = override_instructions {
                        assert_eq!(&question.instructions, instructions);
                    } else {
                        for key in ["setting", "evidence", "comparison", "boundary", "policy"] {
                            assert!(question.instructions[key].as_str().is_some());
                        }
                    }
                    if name == "provider error" {
                        return call
                            .respond(std::future::ready(Err(LibsyError::client_call(
                                "judge",
                                LlmClientError::General("private provider body".into()),
                            ))))
                            .await;
                    }
                    if name == "dropped reply" {
                        drop(call);
                        return Ok(());
                    }
                    let value = if name == "wrong type" {
                        DecisionValue::Boolean(BooleanEstimate::Value(true))
                    } else {
                        DecisionValue::Choice {
                            selected: "no_advantage".into(),
                            probabilities: (name != "no distribution").then(|| {
                                BTreeMap::from([
                                    ("advantage".into(), Probability(score)),
                                    ("no_advantage".into(), Probability(1.0 - score)),
                                ])
                            }),
                        }
                    };
                    let answers = if name == "missing answer" {
                        BTreeMap::new()
                    } else {
                        BTreeMap::from([(
                            "route".into(),
                            DecisionAnswer {
                                value,
                                provider_confidence: Some(ProviderConfidence(0.99)),
                            },
                        )])
                    };
                    call.respond(std::future::ready(Ok(DecisionResponse {
                        id: None,
                        model: Some("provider-judge".into()),
                        answers,
                        usage: Default::default(),
                    })))
                    .await
                }
            };
            let models = Arc::new(RuntimeModels::new(runtime_models()));
            let result = drive(router.clone(), request.clone(), models.clone(), &serve).await;
            assert_eq!(
                calls.load(Ordering::SeqCst),
                usize::from(name != "missing candidate"),
                "{name}"
            );
            let Some(expected) = expected else {
                if name == "missing candidate" {
                    assert!(
                        matches!(result, Err(LibsyError::AlgorithmError { message }) if message.contains("candidate is missing"))
                    );
                } else {
                    assert!(
                        matches!(result, Err(LibsyError::ClientCall { .. })),
                        "{name}"
                    );
                }
                continue;
            };
            let outcome = result?;
            assert_eq!(outcome.selected_model_id()?, expected, "{name}");
            assert!(outcome.response.is_none());
            assert_eq!(outcome.request.llm_request.messages, original_messages);
            let evidence = outcome
                .metadata
                .and_then(|metadata| metadata.evidence)
                .expect("routing evidence");
            if matches!(name, "above" | "equal" | "below") {
                assert_eq!(evidence["source"], "decision_classifier");
                assert_eq!(evidence["verdict"], "relative_advantage");
                assert_eq!(evidence["threshold"], settings.cutoff);
                assert_eq!(evidence["score"], score);
            } else {
                let reason = match name {
                    "provider error" => "client_error",
                    "dropped reply" => "call_error",
                    _ => "invalid_verdict",
                };
                assert_eq!(
                    evidence,
                    json!({"source": "fail_open", "reason_code": reason})
                );
            }
            let retained = drive(router, request.clone(), models, &serve).await?;
            assert_eq!(retained.selected_model_id()?, expected);
            assert_eq!(
                calls.load(Ordering::SeqCst),
                1,
                "affinity should skip the judge: {name}"
            );
        }
        for cutoff in [-0.1, 1.1, f64::NAN] {
            let mut config = settings.clone();
            config.cutoff = cutoff;
            assert!(
                LlmTaskClassifier::new(LlmClassifierConfig::Capability {
                    config: TaskClassifierConfig {
                        judge: CapabilityJudgeConfig::Decision(config),
                        ..TaskClassifierConfig::default()
                    },
                })
                .is_err()
            );
        }
        let mut duplicate = settings.clone();
        duplicate
            .candidates
            .insert("duplicate-a".into(), "capable".into());
        assert!(duplicate.validate().is_err());
        let mut custom = settings.clone();
        custom.candidates.remove("c");
        custom.criteria = Some(BTreeMap::from([
            ("a".into(), json!("Start capable")),
            ("b".into(), json!("Start efficient")),
        ]));
        assert!(custom.validate().is_err());
        custom.instructions = Some(json!("Choose a profile."));
        assert!(custom.validate().is_ok());
        custom.candidates.remove("b");
        assert!(custom.validate().is_err());
        Ok(())
    }

    #[test]
    fn the_threshold_boundary_is_inclusive() -> Result<()> {
        let policy = policy();
        let at_threshold = verdict(0.5, "supported", "SUP-1");
        let below_threshold = verdict(0.49, "supported", "SUP-1");
        assert_eq!(selected(&policy, Some(&at_threshold))?, "efficient");
        assert_eq!(selected(&policy, Some(&below_threshold))?, "capable");
        Ok(())
    }

    #[test]
    fn the_threshold_moves_the_routing_boundary() -> Result<()> {
        let borderline = verdict(0.5, "supported", "SUP-1");
        let strict = TaskClassifierPolicy::new(&llm_config(0.9));
        let lenient = TaskClassifierPolicy::new(&llm_config(0.1));
        assert_eq!(selected(&strict, Some(&borderline))?, "capable");
        assert_eq!(selected(&lenient, Some(&borderline))?, "efficient");
        Ok(())
    }

    #[test]
    fn classifier_config_rejects_unknown_fields() {
        let error = serde_json::from_value::<TaskClassifierConfig>(serde_json::json!({
            "base_threshold": 0.5,
            "classifier_magic": true,
        }))
        .expect_err("unknown classifier fields must be rejected");

        assert!(
            error
                .to_string()
                .contains("unknown field `classifier_magic`"),
            "{error}"
        );
    }

    #[test]
    fn invalid_classifier_config_is_rejected() -> Result<()> {
        for bad in [1.5, -0.1, f64::NAN, f64::INFINITY] {
            assert!(
                LlmTaskClassifier::new(LlmClassifierConfig::Capability {
                    config: test_config(bad),
                })
                .is_err(),
                "base threshold {bad} should be rejected"
            );
        }
        for config in [
            TaskClassifierConfig {
                judge: CapabilityJudgeConfig::Llm(LlmCapabilityConfig {
                    base_threshold: 0.5,
                    threshold_step: -0.1,
                    ..LlmCapabilityConfig::default()
                }),
                ..TaskClassifierConfig::default()
            },
            TaskClassifierConfig {
                judge: CapabilityJudgeConfig::Llm(LlmCapabilityConfig {
                    base_threshold: 0.8,
                    threshold_step: 0.11,
                    ..LlmCapabilityConfig::default()
                }),
                ..TaskClassifierConfig::default()
            },
            TaskClassifierConfig {
                judge: CapabilityJudgeConfig::Llm(LlmCapabilityConfig {
                    base_threshold: 0.5,
                    ..LlmCapabilityConfig::default()
                }),
                message_hash_fallback: true,
                ..TaskClassifierConfig::default()
            },
            TaskClassifierConfig {
                judge: CapabilityJudgeConfig::Llm(LlmCapabilityConfig {
                    base_threshold: 0.5,
                    max_output_tokens: 0,
                    ..LlmCapabilityConfig::default()
                }),
                ..TaskClassifierConfig::default()
            },
        ] {
            assert!(LlmTaskClassifier::new(LlmClassifierConfig::Capability { config }).is_err());
        }
        for base_threshold in [0.0, 1.0] {
            LlmTaskClassifier::new(LlmClassifierConfig::Capability {
                config: test_config(base_threshold),
            })?;
        }
        Ok(())
    }

    #[test]
    fn message_hash_fallback_accepts_retaining_triggers() -> Result<()> {
        // Only `every_request` is rejected: it retains no target, so a fallback
        // identity has nothing to key on. Both retaining triggers may key the
        // retained target on a message hash when the caller sends no session id.
        for trigger in [ClassifyTrigger::NewSession, ClassifyTrigger::UserTurn] {
            let config = TaskClassifierConfig {
                judge: CapabilityJudgeConfig::Llm(LlmCapabilityConfig {
                    base_threshold: 0.5,
                    ..LlmCapabilityConfig::default()
                }),
                classify_trigger: trigger,
                message_hash_fallback: true,
                ..TaskClassifierConfig::default()
            };
            LlmTaskClassifier::new(LlmClassifierConfig::Capability { config }).map_err(
                |error| LibsyError::AlgorithmError {
                    message: format!("{trigger:?} with message_hash_fallback rejected: {error}"),
                },
            )?;
        }
        let every_request = TaskClassifierConfig {
            judge: CapabilityJudgeConfig::Llm(LlmCapabilityConfig {
                base_threshold: 0.5,
                ..LlmCapabilityConfig::default()
            }),
            classify_trigger: ClassifyTrigger::EveryRequest,
            message_hash_fallback: true,
            ..TaskClassifierConfig::default()
        };
        assert!(
            LlmTaskClassifier::new(LlmClassifierConfig::Capability {
                config: every_request
            })
            .is_err(),
            "every_request with message_hash_fallback should stay rejected"
        );
        Ok(())
    }

    #[test]
    fn an_unusable_verdict_is_ambiguous() -> Result<()> {
        let policy = policy();
        let inconsistent_rule = TaskClassifierVerdict {
            capability_boundary: "uncertain".to_string(),
            ..verdict(1.0, "supported", "SUP-1")
        };
        let empty_crux = TaskClassifierVerdict {
            crux: "  ".to_string(),
            ..verdict(1.0, "supported", "SUP-1")
        };
        let unusable = [
            Some(verdict(1.1, "supported", "SUP-1")),
            Some(inconsistent_rule),
            Some(empty_crux),
            None,
        ];
        for verdict in unusable {
            let classification = policy.to_classification(verdict.as_ref(), &policy_driver())?;
            assert!(matches!(classification, Classification::Ambiguous(_)));
            assert!(classification.argmax(false)?.is_none());
            assert!(classification.argmax(true)?.is_none());
        }
        Ok(())
    }

    #[test]
    fn capability_boundaries_apply_monotonic_threshold_steps() -> Result<()> {
        let policy = TaskClassifierPolicy::new(&LlmCapabilityConfig {
            threshold_step: 0.1,
            ..llm_config(0.4)
        });

        assert_eq!(
            selected(&policy, Some(&verdict(0.4, "supported", "SUP-2")))?,
            "efficient"
        );
        assert_eq!(
            selected(&policy, Some(&verdict(0.49, "uncertain", "UNC-1")))?,
            "capable"
        );
        assert_eq!(
            selected(&policy, Some(&verdict(0.5, "uncertain", "UNC-1")))?,
            "efficient"
        );
        assert_eq!(
            selected(&policy, Some(&verdict(0.5, "unmatched", "none")))?,
            "efficient"
        );
        assert_eq!(
            selected(&policy, Some(&verdict(0.59, "unsupported", "LIM-1")))?,
            "capable"
        );
        assert_eq!(
            selected(&policy, Some(&verdict(0.6, "unsupported", "LIM-1")))?,
            "efficient"
        );
        Ok(())
    }

    #[test]
    fn unmatched_steps_raise_only_the_unmatched_threshold() -> Result<()> {
        let policy = TaskClassifierPolicy::new(&LlmCapabilityConfig {
            threshold_step: 0.1,
            unmatched_steps: 2,
            ..llm_config(0.75)
        });

        // 0.85 clears one step but not two: unmatched now routes to the capable target.
        assert_eq!(
            selected(&policy, Some(&verdict(0.85, "unmatched", "none")))?,
            "capable"
        );
        assert_eq!(
            selected(&policy, Some(&verdict(0.95, "unmatched", "none")))?,
            "efficient"
        );
        // Other boundaries keep their steps.
        assert_eq!(
            selected(&policy, Some(&verdict(0.85, "uncertain", "UNC-1")))?,
            "efficient"
        );
        assert_eq!(
            selected(&policy, Some(&verdict(0.75, "supported", "SUP-2")))?,
            "efficient"
        );
        Ok(())
    }

    #[test]
    fn unmatched_steps_default_to_one_and_reject_values_above_two() {
        let parsed: TaskClassifierConfig =
            serde_json::from_value(serde_json::json!({ "base_threshold": 0.5 }))
                .expect("config without unmatched_steps parses");
        let CapabilityJudgeConfig::Llm(judge) = &parsed.judge else {
            panic!("a flat capability config builds an LLM judge");
        };
        assert_eq!(judge.unmatched_steps, 1);

        let invalid = TaskClassifierConfig {
            judge: CapabilityJudgeConfig::Llm(LlmCapabilityConfig {
                unmatched_steps: 3,
                ..llm_config(0.5)
            }),
            ..TaskClassifierConfig::default()
        };
        assert!(invalid.validate().is_err());
    }

    /// The text of each message a judge with `recent_turn_window` would be sent.
    /// The no-window case is covered by `capability_judge_builds_a_structured_request`.
    fn capability_judge(recent_turn_window: Option<usize>) -> Result<CapabilityJudge> {
        Ok(StructuredJudge::new(
            TaskInput { recent_turn_window },
            LlmTaskClassifier::load_capability_contract(&ClassifierContractConfig::default())?,
            SerdeDecoder::new(),
            JudgeRuntimeConfig::new(DEFAULT_JUDGE_MAX_OUTPUT_TOKENS)?,
        ))
    }

    fn judged_contents(recent_turn_window: usize) -> Result<Vec<String>> {
        let judge = capability_judge(Some(recent_turn_window))?;
        let request = Request {
            llm_request: LlmRequest {
                messages: vec![
                    Message::text(Role::System, "client instructions"),
                    Message::text(
                        Role::User,
                        "<environment_context>setup</environment_context>",
                    ),
                    Message::text(Role::User, "initial task"),
                    Message::text(Role::Assistant, "old response"),
                    Message::text(Role::User, "old follow-up"),
                    Message::text(Role::Assistant, "recent 1"),
                    Message::text(Role::User, "recent 2"),
                ],
                ..LlmRequest::default()
            },
            raw_request: None,
            metadata: None,
        };
        Ok(judge
            .build_request(&State::default(), &request)
            .llm_request
            .messages
            .iter()
            .filter_map(|message| message.text_content("\n"))
            .collect())
    }

    #[test]
    fn a_window_widens_the_judge_to_the_surrounding_conversation() -> Result<()> {
        // Client instructions and the opening task, plus the last two turns.
        let contents = judged_contents(2)?;
        assert!(contents.contains(&"client instructions".to_string()));
        assert!(contents.contains(&"initial task".to_string()));
        assert!(contents.contains(&"recent 1".to_string()));
        assert!(contents.contains(&"recent 2".to_string()));
        assert!(!contents.contains(&"old response".to_string()));
        Ok(())
    }

    #[test]
    fn a_zero_window_keeps_only_the_instructions_and_the_task() -> Result<()> {
        let contents = judged_contents(0)?;
        assert!(contents.contains(&"client instructions".to_string()));
        assert!(contents.contains(&"initial task".to_string()));
        assert!(!contents.contains(&"recent 2".to_string()));
        Ok(())
    }

    fn tool_call(id: &str) -> Message {
        Message {
            role: Role::Assistant,
            content: vec![ContentBlock::ToolCall(ToolCall {
                id: id.to_string(),
                name: "search".to_string(),
                arguments: Value::Null,
            })],
        }
    }

    fn tool_result(id: &str) -> Message {
        Message {
            role: Role::Tool,
            content: vec![ContentBlock::ToolResult(ToolResult {
                tool_call_id: id.to_string(),
                content: vec![ContentBlock::Text {
                    text: "tool output".to_string(),
                }],
                is_error: None,
            })],
        }
    }

    #[test]
    fn default_task_input_keeps_user_content_around_tool_results() {
        let context = Message::text(
            Role::User,
            "<environment_context><current_date>2026-10-31</current_date></environment_context>",
        );
        let mut result = tool_result("call-1");
        result.role = Role::User;
        let mut mixed = result.clone();
        mixed.content.push(ContentBlock::Text {
            text: "latest follow-up".to_string(),
        });
        let input = TaskInput {
            recent_turn_window: None,
        };
        let mut request = Request {
            llm_request: LlmRequest {
                messages: vec![
                    context.clone(),
                    result.clone(),
                    Message::text(Role::User, "initial task"),
                    tool_call("call-1"),
                    mixed,
                    result.clone(),
                    context,
                ],
                ..LlmRequest::default()
            },
            ..Request::default()
        };
        assert_eq!(
            input.build_messages(&State::default(), &request),
            vec![
                Message::text(Role::User, "initial task"),
                Message::text(Role::User, "latest follow-up"),
            ]
        );
        request.llm_request.messages = vec![result];
        assert!(input.build_messages(&State::default(), &request).is_empty());
    }

    /// A count-based window can begin on a tool result, which leaves the call that
    /// introduced its id outside the window and the classifier history invalid.
    #[test]
    fn trimming_keeps_the_call_that_introduced_a_kept_tool_result() {
        let messages = vec![
            Message::text(Role::System, "client instructions"),
            Message::text(Role::User, "initial task"),
            Message::text(Role::Assistant, "old response"),
            tool_call("call-1"),
            tool_result("call-1"),
            Message::text(Role::Assistant, "recent 1"),
            Message::text(Role::User, "recent 2"),
            Message::text(Role::Assistant, "recent 3"),
            Message::text(Role::User, "recent 4"),
        ];

        // The five-message tail begins exactly on the tool result.
        let kept = trim_messages(&messages, 5);

        assert_eq!(
            kept,
            vec![
                Message::text(Role::System, "client instructions"),
                Message::text(Role::User, "initial task"),
                tool_call("call-1"),
                tool_result("call-1"),
                Message::text(Role::Assistant, "recent 1"),
                Message::text(Role::User, "recent 2"),
                Message::text(Role::Assistant, "recent 3"),
                Message::text(Role::User, "recent 4"),
            ]
        );
    }

    /// Ids repeat across a conversation, so a later call must not stand in for the one that
    /// answers an earlier result.
    #[test]
    fn trimming_pairs_a_repeated_id_with_the_call_that_precedes_it() {
        let messages = vec![
            Message::text(Role::System, "client instructions"),
            Message::text(Role::User, "initial task"),
            tool_call("x"),
            tool_result("x"),
            Message::text(Role::Assistant, "later"),
            tool_call("x"),
            tool_result("x"),
        ];

        // The four-message tail begins on the first result, whose own call sits one earlier.
        let kept = trim_messages(&messages, 4);

        assert_eq!(
            kept,
            vec![
                Message::text(Role::System, "client instructions"),
                Message::text(Role::User, "initial task"),
                tool_call("x"),
                tool_result("x"),
                Message::text(Role::Assistant, "later"),
                tool_call("x"),
                tool_result("x"),
            ]
        );
    }

    /// A result whose call precedes the opening task can never be paired, because trimming
    /// never reaches behind the task. The window must not widen hunting for it.
    #[test]
    fn trimming_keeps_the_counted_window_when_a_result_cannot_be_paired() {
        let messages = vec![
            Message::text(Role::System, "client instructions"),
            tool_call("orphan"),
            Message::text(Role::User, "initial task"),
            Message::text(Role::Assistant, "old response"),
            tool_result("orphan"),
            Message::text(Role::Assistant, "recent 1"),
            Message::text(Role::User, "recent 2"),
        ];

        let kept = trim_messages(&messages, 3);

        assert_eq!(
            kept,
            vec![
                Message::text(Role::System, "client instructions"),
                Message::text(Role::User, "initial task"),
                tool_result("orphan"),
                Message::text(Role::Assistant, "recent 1"),
                Message::text(Role::User, "recent 2"),
            ]
        );
    }

    #[test]
    fn a_window_restates_the_routing_instruction_last() -> Result<()> {
        let contents = judged_contents(2)?;

        // Last, not merely present: the instruction only works in final position,
        // after the conversation content it is meant to outrank.
        assert_eq!(
            contents.last().map(String::as_str),
            Some(TRAILING_ROUTING_INSTRUCTION)
        );
        Ok(())
    }

    /// Reasoning is provider-private and some upstreams reject it replayed without the
    /// opaque state it was issued with, so it must not reach a windowed classifier
    /// request. Visible assistant text and complete tool pairs still do, and a turn
    /// whose only content was reasoning is dropped rather than left behind empty.
    #[test]
    fn a_window_drops_reasoning_but_keeps_visible_text_and_tool_pairs() {
        let messages = vec![
            Message::text(Role::System, "client instructions"),
            Message::text(Role::User, "initial task"),
            Message {
                role: Role::Assistant,
                content: vec![
                    ContentBlock::Reasoning {
                        text: "private chain of thought".to_string(),
                        signature: None,
                        details: Vec::new(),
                    },
                    ContentBlock::Text {
                        text: "visible answer".to_string(),
                    },
                ],
            },
            tool_call("call-1"),
            tool_result("call-1"),
            Message {
                role: Role::Assistant,
                content: vec![ContentBlock::Reasoning {
                    text: "reasoning-only turn".to_string(),
                    signature: None,
                    details: Vec::new(),
                }],
            },
            Message::text(Role::User, "follow-up"),
        ];
        let request = Request {
            llm_request: LlmRequest {
                messages,
                ..LlmRequest::default()
            },
            raw_request: None,
            metadata: None,
        };

        let built = TaskInput {
            recent_turn_window: Some(10),
        }
        .build_messages(&State::default(), &request);

        assert!(
            !built
                .iter()
                .flat_map(|message| &message.content)
                .any(|block| matches!(block, ContentBlock::Reasoning { .. })),
            "{built:?}"
        );
        assert!(
            built
                .iter()
                .any(|message| message.text_content("\n").as_deref() == Some("visible answer"))
        );
        assert!(built.contains(&tool_call("call-1")));
        assert!(built.contains(&tool_result("call-1")));
        // Six of the seven fixtures survive — the reasoning-only turn is gone entirely
        // rather than left behind empty — plus the trailing routing instruction.
        assert_eq!(built.len(), 7);
        assert!(built.iter().all(|message| !message.content.is_empty()));
    }

    #[test]
    fn the_default_path_is_left_unchanged() -> Result<()> {
        // No window means no conversation to be distracted by, so the default
        // request shape stays exactly as it was.
        let judge = capability_judge(None)?;
        let request = Request {
            llm_request: LlmRequest {
                messages: vec![Message::text(Role::User, "the task")],
                ..LlmRequest::default()
            },
            raw_request: None,
            metadata: None,
        };

        let built = judge.build_request(&State::default(), &request);

        // The task message alone; the rubric reaches the judge as an instruction block
        // rather than a message, so nothing was dropped by it not being counted here.
        assert_eq!(built.llm_request.messages.len(), 1);
        assert!(!built.llm_request.instructions.is_empty());
        assert!(
            !built
                .llm_request
                .messages
                .iter()
                .filter_map(|message| message.text_content("\n"))
                .any(|text| text.contains(TRAILING_ROUTING_INSTRUCTION))
        );
        Ok(())
    }

    #[test]
    fn capability_judge_builds_a_structured_request() -> Result<()> {
        let judge = capability_judge(None)?;
        let request = Request {
            llm_request: LlmRequest {
                model: Some("inbound".to_string()),
                messages: vec![
                    Message::text(Role::System, "client instructions"),
                    Message::text(Role::Developer, "client developer instructions"),
                    Message::text(Role::User, "initial task"),
                    Message::text(Role::Assistant, "old response"),
                    Message::text(Role::User, "old follow-up"),
                    Message::text(Role::Assistant, "recent 1"),
                    Message::text(Role::User, "recent 2"),
                    Message::text(Role::Assistant, "recent 3"),
                    Message::text(Role::User, "recent 4"),
                    Message::text(Role::Assistant, "recent 5"),
                ],
                ..LlmRequest::default()
            },
            raw_request: None,
            metadata: None,
        };
        let judge_request = judge.build_request(&State::default(), &request);

        assert_eq!(judge_request.llm_request.model, request.llm_request.model);
        assert_eq!(judge_request.llm_request.instructions.len(), 1);
        assert_eq!(judge_request.llm_request.instructions[0].role, Role::System);
        assert_eq!(
            judge_request.llm_request.instructions[0].content,
            InstructionBlock {
                role: Role::System,
                content: Message::text(Role::System, judge.contract().system_prompt()).content,
            }
            .content,
        );
        assert_eq!(judge_request.llm_request.messages.len(), 2);
        let contents = judge_request
            .llm_request
            .messages
            .iter()
            .filter_map(|message| message.text_content("\n"))
            .collect::<Vec<_>>();
        assert!(contents.contains(&"recent 4".to_string()));
        assert!(contents.contains(&"initial task".to_string()));
        assert!(!contents.contains(&"recent 5".to_string()));
        assert!(!contents.contains(&"client instructions".to_string()));
        assert_eq!(
            judge_request.llm_request.output.response_format,
            Some(judge.contract().response_format().clone())
        );
        assert_eq!(
            judge_request.llm_request.output.max_output_tokens,
            Some(DEFAULT_JUDGE_MAX_OUTPUT_TOKENS)
        );
        Ok(())
    }

    fn sample_value(spec: &Value) -> Value {
        if let Some(first) = spec
            .get("enum")
            .and_then(Value::as_array)
            .and_then(|values| values.first())
        {
            return first.clone();
        }
        match spec.get("type").and_then(Value::as_str) {
            Some("number") => serde_json::json!(0.5),
            Some("boolean") => serde_json::json!(false),
            _ => serde_json::json!("sample"),
        }
    }

    fn schema_shaped_verdict(schema: &Value) -> Result<String> {
        let properties = schema
            .pointer("/json_schema/schema/properties")
            .and_then(Value::as_object)
            .ok_or_else(|| LibsyError::AlgorithmError {
                message: "packaged schema declares no properties".to_string(),
            })?;
        Ok(Value::Object(
            properties
                .iter()
                .map(|(name, spec)| (name.clone(), sample_value(spec)))
                .collect(),
        )
        .to_string())
    }

    /// Built from the schema so a property added there fails here rather than silently
    /// rejecting every production verdict.
    #[test]
    fn every_schema_property_round_trips_through_the_judge_parser() -> Result<()> {
        let contract =
            LlmTaskClassifier::load_capability_contract(&ClassifierContractConfig::default())?;
        let schema = contract.response_format();
        let reply = schema_shaped_verdict(schema)?;
        let judge: CapabilityJudge = StructuredJudge::new(
            TaskInput {
                recent_turn_window: None,
            },
            contract,
            SerdeDecoder::new(),
            JudgeRuntimeConfig::new(DEFAULT_JUDGE_MAX_OUTPUT_TOKENS)?,
        );

        let verdict = judge.parse(&text_response(None, reply))?;

        assert!(verdict.is_valid());
        assert!((0.0..=1.0).contains(&verdict.p_solve));
        Ok(())
    }

    #[test]
    fn packaged_prompt_keeps_the_schema_in_the_structured_request() -> Result<()> {
        let contract =
            LlmTaskClassifier::load_capability_contract(&ClassifierContractConfig::default())?;
        let prompt = contract.system_prompt();
        let schema_name = contract
            .response_format()
            .pointer("/json_schema/name")
            .and_then(Value::as_str)
            .ok_or_else(|| LibsyError::AlgorithmError {
                message: "packaged response schema has no name".to_string(),
            })?;
        assert_eq!(schema_name, "CapabilityClassifierDecision");
        assert!(prompt.contains("SUP-1 [supported]"));
        assert!(prompt.contains("SUP-5 [supported]"));
        assert!(!prompt.contains("{{RESPONSE_SCHEMA}}"));
        assert!(!prompt.contains("\"type\": \"object\""));
        assert!(!prompt.contains("\"json_schema\""));
        assert!(!prompt.contains(schema_name));
        let rule_values = contract
            .response_format()
            .pointer("/json_schema/schema/properties/primary_rule/enum")
            .and_then(Value::as_array)
            .ok_or_else(|| LibsyError::AlgorithmError {
                message: "rendered response schema has no primary rule enum".to_string(),
            })?;
        assert!(
            rule_values
                .iter()
                .any(|value| value.as_str() == Some("SUP-1"))
        );
        assert!(
            rule_values
                .iter()
                .any(|value| value.as_str() == Some("none"))
        );
        Ok(())
    }
}