backend-rust/src/api/ops.rs

use axum::{extract::State, http::StatusCode, Json};
use serde::{Deserialize, Serialize};
use sqlx::Row;
use uuid::Uuid;

use crate::app_state::AppState;

use super::system::{
    build_author, build_platform_manifest, CloudCapabilityManifest, DiagnosticsManifest,
    OperatorOsManifest, PlatformContractsManifest, PluginPlatformManifest,
    ProcessRuntimeManifest, RoboticsCapabilityManifest, SelfImprovementManifest,
    TranslationLayerManifest,
};

const LATENCY_DEGRADED_THRESHOLD_MS: i64 = 1_500;
const LATENCY_WATCH_THRESHOLD_MS: i64 = 900;
const HEALTH_SCORE_DEGRADED: f64 = 42.0;
const HEALTH_SCORE_WATCH: f64 = 68.0;
const HEALTH_SCORE_TOOL_MEMORY_WATCH: f64 = 69.0;
const HEALTH_SCORE_HEALTHY: f64 = 88.0;
const HEALTH_SCORE_HEALTHY_ALT: f64 = 87.0;

#[derive(Serialize, Clone)]
pub struct LeaderboardEntry {
    pub branch: String,
    pub evaluations: i64,
    pub avg_outcome_score: Option<f64>,
    pub avg_latency_ms: Option<i64>,
    pub failed_evaluations: i64,
    pub source: &'static str,
}

#[derive(Serialize, Clone)]
pub struct ImprovementRecommendationRecord {
    pub id: String,
    pub agent_session_id: String,
    pub source_task_id: Option<String>,
    pub target: String,
    pub title: String,
    pub summary: String,
    pub confidence_score: f64,
    pub created_at: String,
}

#[derive(Serialize, Clone)]
pub struct StatusMetric {
    pub status: String,
    pub count: i64,
}

#[derive(Serialize, Clone)]
pub struct SelfImprovementConsole {
    pub manifest: SelfImprovementManifest,
    pub total_evaluations: i64,
    pub automated_recommendations: i64,
    pub leaderboard: Vec<LeaderboardEntry>,
    pub recommendations: Vec<ImprovementRecommendationRecord>,
}

#[derive(Serialize, Clone)]
pub struct ProcessRuntimeConsole {
    pub manifest: ProcessRuntimeManifest,
    pub active_agent_sessions: i64,
    pub running_agent_tasks: i64,
    pub tool_calls_24h: i64,
    pub avg_tool_latency_ms: Option<i64>,
    pub gpu_jobs_by_status: Vec<StatusMetric>,
}

#[derive(Serialize, Clone)]
pub struct PluginPlatformConsole {
    pub manifest: PluginPlatformManifest,
    pub listed_metadata_fields: usize,
}

#[derive(Serialize, Clone)]
pub struct TranslationLayerConsole {
    pub manifest: TranslationLayerManifest,
    pub supported_planes: usize,
}

#[derive(Serialize, Clone)]
pub struct HealthSignalStatus {
    pub signal: String,
    pub status: String,
    pub summary: String,
    pub score: Option<f64>,
    pub source: &'static str,
}

#[derive(Serialize, Clone)]
pub struct DiagnosticTimelineEventRecord {
    pub id: String,
    pub incident_id: String,
    pub event_type: String,
    pub summary: String,
    pub created_at: String,
}

#[derive(Serialize, Clone)]
pub struct DiagnosticIncidentRecord {
    pub id: String,
    pub incident_key: String,
    pub title: String,
    pub status: String,
    pub severity: String,
    pub affected_scope: String,
    pub root_cause_summary: Option<String>,
    pub recovery_action: Option<String>,
    pub health_score: Option<f64>,
    pub opened_at: String,
    pub resolved_at: Option<String>,
}

#[derive(Serialize, Clone)]
pub struct GuardrailActionRecord {
    pub id: String,
    pub incident_id: Option<String>,
    pub action_type: String,
    pub trigger_signal: String,
    pub status: String,
    pub scope: String,
    pub summary: String,
    pub created_at: String,
}

#[derive(Serialize, Clone)]
pub struct DiagnosticsConsole {
    pub manifest: DiagnosticsManifest,
    pub health_score: f64,
    pub open_incidents: i64,
    pub active_guardrails: i64,
    pub degraded_capabilities: i64,
    pub health_signals: Vec<HealthSignalStatus>,
    pub incidents: Vec<DiagnosticIncidentRecord>,
    pub timeline: Vec<DiagnosticTimelineEventRecord>,
    pub guardrail_actions: Vec<GuardrailActionRecord>,
}

#[derive(Serialize, Clone)]
pub struct PlatformFoundationConsole {
    pub manifest: PlatformContractsManifest,
    pub organizations: i64,
    pub teams: i64,
    pub workspaces: i64,
    pub artifacts: i64,
    pub notifications: i64,
}

#[derive(Serialize, Clone)]
pub struct CloudConsole {
    pub manifest: CloudCapabilityManifest,
    pub usage_plans: i64,
    pub billing_accounts: i64,
    pub billing_events: i64,
}

#[derive(Serialize, Clone)]
pub struct RoboticsConsole {
    pub manifest: RoboticsCapabilityManifest,
    pub fleet_assets: i64,
}

#[derive(Serialize, Clone)]
pub struct OperatorOsConsole {
    pub manifest: OperatorOsManifest,
    pub active_centers: usize,
    pub notification_backlog: i64,
}

#[derive(Serialize, Clone)]
pub struct OperationsConsoleResponse {
    pub summary: String,
    pub generated_at: String,
    pub self_improvement: SelfImprovementConsole,
    pub process_runtime: ProcessRuntimeConsole,
    pub plugin_platform: PluginPlatformConsole,
    pub translation_layer: TranslationLayerConsole,
    pub diagnostics: DiagnosticsConsole,
    pub platform_foundation: PlatformFoundationConsole,
    pub cloud: CloudConsole,
    pub robotics: RoboticsConsole,
    pub operator_os: OperatorOsConsole,
}

#[derive(Debug, Deserialize)]
pub struct SubmitTaskOutcomeRequest {
    pub agent_session_id: String,
    pub task_id: String,
    pub agent_branch: String,
    pub evaluator: String,
    pub suite_name: String,
    pub outcome_score: f64,
    pub latency_ms: Option<i32>,
    pub token_efficiency_score: Option<f64>,
    pub regression_status: Option<String>,
    pub feedback: Option<String>,
    pub evidence: Option<serde_json::Value>,
    pub signals: Option<OutcomeSignals>,
}

#[derive(Debug, Deserialize, Serialize, Clone, Default)]
pub struct OutcomeSignals {
    pub policy_violations: Option<u32>,
    pub tool_failures: Option<u32>,
    pub memory_misses: Option<u32>,
    pub prompt_clarity_issues: Option<u32>,
}

#[derive(Debug, Serialize)]
pub struct SubmitTaskOutcomeResponse {
    pub evaluation_id: String,
    pub outcome: String,
    pub recommendation_count: usize,
}

#[derive(Debug, Clone)]
struct GeneratedRecommendation {
    target: &'static str,
    title: String,
    summary: String,
    confidence_score: f64,
}

pub async fn operations_console(
    State(state): State<AppState>,
) -> Result<Json<OperationsConsoleResponse>, (StatusCode, Json<serde_json::Value>)> {
    let manifest = build_platform_manifest(build_author(&state));
    let postgres = state.postgres.as_ref();

    let total_evaluations = count_rows(
        postgres,
        "SELECT COUNT(*) AS count FROM agent_task_evaluations",
    )
    .await?;
    let automated_recommendations = count_rows(
        postgres,
        "SELECT COUNT(*) AS count FROM failed_session_recommendations",
    )
    .await?;
    let active_agent_sessions = count_rows(
        postgres,
        "SELECT COUNT(*) AS count FROM agent_sessions WHERE status = 'running'",
    )
    .await?;
    let running_agent_tasks = count_rows(
        postgres,
        "SELECT COUNT(*) AS count FROM agent_tasks WHERE status = 'running'",
    )
    .await?;
    let tool_calls_24h = count_rows(
        postgres,
        "SELECT COUNT(*) AS count FROM agent_tool_calls WHERE created_at >= NOW() - INTERVAL '24 hours'",
    )
    .await?;
    let avg_tool_latency_ms = optional_avg_latency(
        postgres,
        "SELECT AVG(latency_ms)::BIGINT AS avg_latency_ms FROM agent_tool_calls WHERE latency_ms IS NOT NULL AND created_at >= NOW() - INTERVAL '24 hours'",
    )
    .await?;
    let organizations = count_rows(postgres, "SELECT COUNT(*) AS count FROM organizations").await?;
    let teams = count_rows(postgres, "SELECT COUNT(*) AS count FROM teams").await?;
    let workspaces = count_rows(postgres, "SELECT COUNT(*) AS count FROM workspaces").await?;
    let artifacts =
        count_rows(postgres, "SELECT COUNT(*) AS count FROM workspace_artifacts").await?;
    let notifications =
        count_rows(postgres, "SELECT COUNT(*) AS count FROM operator_notifications").await?;
    let usage_plans = count_rows(postgres, "SELECT COUNT(*) AS count FROM usage_plans").await?;
    let billing_accounts =
        count_rows(postgres, "SELECT COUNT(*) AS count FROM billing_accounts").await?;
    let billing_events =
        count_rows(postgres, "SELECT COUNT(*) AS count FROM billing_ledger_events").await?;
    let fleet_assets = count_rows(postgres, "SELECT COUNT(*) AS count FROM fleet_assets").await?;
    let open_incidents = count_rows(
        postgres,
        "SELECT COUNT(*) AS count FROM diagnostic_incidents WHERE status <> 'resolved'",
    )
    .await?;
    let active_guardrails = count_rows(
        postgres,
        "SELECT COUNT(*) AS count FROM diagnostic_guardrail_actions WHERE status IN ('planned', 'triggered')",
    )
    .await?;
    let degraded_capabilities =
        count_rows(postgres, "SELECT COUNT(*) AS count FROM diagnostic_incidents WHERE status = 'degraded'")
            .await?;
    let gpu_jobs_by_status = load_gpu_jobs_by_status(postgres).await?;
    let mut leaderboard = load_leaderboard(postgres).await?;
    if leaderboard.is_empty() {
        leaderboard = manifest
            .branches
            .iter()
            .take(4)
            .map(|branch| LeaderboardEntry {
                branch: branch.branch.to_string(),
                evaluations: 0,
                avg_outcome_score: None,
                avg_latency_ms: None,
                failed_evaluations: 0,
                source: "manifest_seed",
            })
            .collect();
    }
    let recommendations = load_recommendations(postgres).await?;
    let incidents = load_incidents(postgres).await?;
    let timeline = load_incident_timeline(postgres).await?;
    let guardrail_actions = load_guardrail_actions(postgres).await?;
    let health_signals = build_health_signals(
        avg_tool_latency_ms,
        &leaderboard,
        &recommendations,
        active_guardrails,
    );
    let health_score = calculate_health_score(&health_signals);

    Ok(Json(OperationsConsoleResponse {
        summary:
            "Vienota operations console ar diagnostics, platform foundation, evals, runtime telemetry, billing un fleet pārskatu."
                .to_string(),
        generated_at: chrono::Utc::now().to_rfc3339(),
        self_improvement: SelfImprovementConsole {
            manifest: manifest.self_improvement.clone(),
            total_evaluations,
            automated_recommendations,
            leaderboard,
            recommendations,
        },
        process_runtime: ProcessRuntimeConsole {
            manifest: manifest.process_runtime.clone(),
            active_agent_sessions,
            running_agent_tasks,
            tool_calls_24h,
            avg_tool_latency_ms,
            gpu_jobs_by_status,
        },
        plugin_platform: PluginPlatformConsole {
            listed_metadata_fields: manifest.plugin_platform.listing_metadata.len(),
            manifest: manifest.plugin_platform.clone(),
        },
        translation_layer: TranslationLayerConsole {
            supported_planes: manifest.translation_layer.translation_planes.len(),
            manifest: manifest.translation_layer.clone(),
        },
        diagnostics: DiagnosticsConsole {
            manifest: manifest.diagnostics.clone(),
            health_score,
            open_incidents,
            active_guardrails,
            degraded_capabilities,
            health_signals,
            incidents,
            timeline,
            guardrail_actions,
        },
        platform_foundation: PlatformFoundationConsole {
            manifest: manifest.platform_contracts.clone(),
            organizations,
            teams,
            workspaces,
            artifacts,
            notifications,
        },
        cloud: CloudConsole {
            manifest: manifest.cloud.clone(),
            usage_plans,
            billing_accounts,
            billing_events,
        },
        robotics: RoboticsConsole {
            manifest: manifest.robotics.clone(),
            fleet_assets,
        },
        operator_os: OperatorOsConsole {
            active_centers: manifest.operator_os.centers.len(),
            notification_backlog: notifications,
            manifest: manifest.operator_os.clone(),
        },
    }))
}

pub async fn submit_task_outcome(
    State(state): State<AppState>,
    Json(req): Json<SubmitTaskOutcomeRequest>,
) -> Result<Json<SubmitTaskOutcomeResponse>, (StatusCode, Json<serde_json::Value>)> {
    validate_submit_request(&req)?;
    let Some(pool) = state.postgres.as_ref() else {
        return Err(error(
            StatusCode::SERVICE_UNAVAILABLE,
            "PostgreSQL nav pieejams eval scoring glabāšanai",
        ));
    };

    let agent_session_id = Uuid::parse_str(req.agent_session_id.trim())
        .map_err(|_| error(StatusCode::BAD_REQUEST, "Nederīgs agent_session_id"))?;
    let task_id = Uuid::parse_str(req.task_id.trim())
        .map_err(|_| error(StatusCode::BAD_REQUEST, "Nederīgs task_id"))?;
    let outcome = classify_outcome(req.outcome_score);
    let regression_status = normalize_agent_task_status(req.regression_status.as_deref())?;
    let feedback = req.feedback.clone().unwrap_or_default();
    let evidence = req
        .evidence
        .clone()
        .unwrap_or_else(|| serde_json::json!({ "signals": req.signals }));

    let evaluation_row = sqlx::query(
        r#"
        INSERT INTO agent_task_evaluations (
            agent_session_id,
            task_id,
            agent_branch,
            evaluator,
            suite_name,
            outcome,
            outcome_score,
            latency_ms,
            token_efficiency_score,
            regression_status,
            feedback,
            evidence_json
        )
        VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10::agent_task_status, $11, $12)
        RETURNING id
        "#,
    )
    .bind(agent_session_id)
    .bind(task_id)
    .bind(req.agent_branch.trim())
    .bind(req.evaluator.trim())
    .bind(req.suite_name.trim())
    .bind(outcome)
    .bind(req.outcome_score)
    .bind(req.latency_ms)
    .bind(req.token_efficiency_score)
    .bind(regression_status.as_deref())
    .bind(feedback)
    .bind(evidence)
    .fetch_one(pool)
    .await
    .map_err(internal_error)?;

    let recommendations = build_recommendations(&req);
    for recommendation in &recommendations {
        sqlx::query(
            r#"
            INSERT INTO failed_session_recommendations (
                agent_session_id,
                source_task_id,
                target,
                title,
                summary,
                confidence_score,
                recommendation_json
            )
            VALUES ($1, $2, $3, $4, $5, $6, $7)
            "#,
        )
        .bind(agent_session_id)
        .bind(task_id)
        .bind(recommendation.target)
        .bind(&recommendation.title)
        .bind(&recommendation.summary)
        .bind(recommendation.confidence_score)
        .bind(serde_json::json!({
            "agent_branch": req.agent_branch.trim(),
            "suite_name": req.suite_name.trim(),
            "evaluator": req.evaluator.trim(),
            "outcome_score": req.outcome_score,
        }))
        .execute(pool)
        .await
        .map_err(internal_error)?;
    }

    Ok(Json(SubmitTaskOutcomeResponse {
        evaluation_id: evaluation_row.get::<Uuid, _>("id").to_string(),
        outcome: outcome.to_string(),
        recommendation_count: recommendations.len(),
    }))
}

async fn count_rows(
    pool: Option<&sqlx::PgPool>,
    query: &str,
) -> Result<i64, (StatusCode, Json<serde_json::Value>)> {
    let Some(pool) = pool else {
        return Ok(0);
    };
    let row = sqlx::query(query).fetch_one(pool).await.map_err(internal_error)?;
    Ok(row.get::<i64, _>("count"))
}

async fn optional_avg_latency(
    pool: Option<&sqlx::PgPool>,
    query: &str,
) -> Result<Option<i64>, (StatusCode, Json<serde_json::Value>)> {
    let Some(pool) = pool else {
        return Ok(None);
    };
    let row = sqlx::query(query).fetch_one(pool).await.map_err(internal_error)?;
    Ok(row.get::<Option<i64>, _>("avg_latency_ms"))
}

async fn load_gpu_jobs_by_status(
    pool: Option<&sqlx::PgPool>,
) -> Result<Vec<StatusMetric>, (StatusCode, Json<serde_json::Value>)> {
    let Some(pool) = pool else {
        return Ok(Vec::new());
    };
    let rows = sqlx::query(
        r#"
        SELECT status::text AS status, COUNT(*) AS count
        FROM vision_gpu_jobs
        GROUP BY status
        ORDER BY status
        "#,
    )
    .fetch_all(pool)
    .await
    .map_err(internal_error)?;

    Ok(rows
        .into_iter()
        .map(|row| StatusMetric {
            status: row.get("status"),
            count: row.get("count"),
        })
        .collect())
}

async fn load_leaderboard(
    pool: Option<&sqlx::PgPool>,
) -> Result<Vec<LeaderboardEntry>, (StatusCode, Json<serde_json::Value>)> {
    let Some(pool) = pool else {
        return Ok(Vec::new());
    };
    let rows = sqlx::query(
        r#"
        SELECT
            agent_branch,
            COUNT(*) AS evaluations,
            AVG(outcome_score)::DOUBLE PRECISION AS avg_outcome_score,
            AVG(latency_ms)::BIGINT AS avg_latency_ms,
            COUNT(*) FILTER (WHERE outcome = 'failed') AS failed_evaluations
        FROM agent_task_evaluations
        GROUP BY agent_branch
        ORDER BY avg_outcome_score DESC NULLS LAST, evaluations DESC
        LIMIT 10
        "#,
    )
    .fetch_all(pool)
    .await
    .map_err(internal_error)?;

    Ok(rows
        .into_iter()
        .map(|row| LeaderboardEntry {
            branch: row.get("agent_branch"),
            evaluations: row.get("evaluations"),
            avg_outcome_score: row.get("avg_outcome_score"),
            avg_latency_ms: row.get("avg_latency_ms"),
            failed_evaluations: row.get("failed_evaluations"),
            source: "live",
        })
        .collect())
}

async fn load_recommendations(
    pool: Option<&sqlx::PgPool>,
) -> Result<Vec<ImprovementRecommendationRecord>, (StatusCode, Json<serde_json::Value>)> {
    let Some(pool) = pool else {
        return Ok(Vec::new());
    };
    let rows = sqlx::query(
        r#"
        SELECT id, agent_session_id, source_task_id, target, title, summary, confidence_score, created_at
        FROM failed_session_recommendations
        ORDER BY created_at DESC, confidence_score DESC
        LIMIT 8
        "#,
    )
    .fetch_all(pool)
    .await
    .map_err(internal_error)?;

    Ok(rows
        .into_iter()
        .map(|row| ImprovementRecommendationRecord {
            id: row.get::<Uuid, _>("id").to_string(),
            agent_session_id: row.get::<Uuid, _>("agent_session_id").to_string(),
            source_task_id: row
                .get::<Option<Uuid>, _>("source_task_id")
                .map(|value| value.to_string()),
            target: row.get("target"),
            title: row.get("title"),
            summary: row.get("summary"),
            confidence_score: row.get("confidence_score"),
            created_at: row.get::<chrono::DateTime<chrono::Utc>, _>("created_at").to_rfc3339(),
        })
        .collect())
}

async fn load_incidents(
    pool: Option<&sqlx::PgPool>,
) -> Result<Vec<DiagnosticIncidentRecord>, (StatusCode, Json<serde_json::Value>)> {
    let Some(pool) = pool else {
        return Ok(vec![DiagnosticIncidentRecord {
            id: Uuid::nil().to_string(),
            incident_key: "manifest-seed-platform-foundation".to_string(),
            title: "Platform foundation seeded for diagnostics center".to_string(),
            status: "monitoring".to_string(),
            severity: "info".to_string(),
            affected_scope: "platform.foundation".to_string(),
            root_cause_summary: Some(
                "Persistence layer vēl tiek sagatavota pilniem live incidentiem.".to_string(),
            ),
            recovery_action: Some(
                "Pieslēgt PostgreSQL un sākt pierakstīt diagnostic_incidents/diagnostic_timeline_events."
                    .to_string(),
            ),
            health_score: Some(82.0),
            opened_at: chrono::Utc::now().to_rfc3339(),
            resolved_at: None,
        }]);
    };
    let rows = sqlx::query(
        r#"
        SELECT id, incident_key, title, status, severity::text AS severity, affected_scope,
               root_cause_summary, recovery_action, health_score, opened_at, resolved_at
        FROM diagnostic_incidents
        ORDER BY opened_at DESC
        LIMIT 6
        "#,
    )
    .fetch_all(pool)
    .await
    .map_err(internal_error)?;

    Ok(rows
        .into_iter()
        .map(|row| DiagnosticIncidentRecord {
            id: row.get::<Uuid, _>("id").to_string(),
            incident_key: row.get("incident_key"),
            title: row.get("title"),
            status: row.get("status"),
            severity: row.get("severity"),
            affected_scope: row.get("affected_scope"),
            root_cause_summary: row.get("root_cause_summary"),
            recovery_action: row.get("recovery_action"),
            health_score: row.get("health_score"),
            opened_at: row.get::<chrono::DateTime<chrono::Utc>, _>("opened_at").to_rfc3339(),
            resolved_at: row
                .get::<Option<chrono::DateTime<chrono::Utc>>, _>("resolved_at")
                .map(|value| value.to_rfc3339()),
        })
        .collect())
}

async fn load_incident_timeline(
    pool: Option<&sqlx::PgPool>,
) -> Result<Vec<DiagnosticTimelineEventRecord>, (StatusCode, Json<serde_json::Value>)> {
    let Some(pool) = pool else {
        return Ok(vec![
            DiagnosticTimelineEventRecord {
                id: Uuid::nil().to_string(),
                incident_id: Uuid::nil().to_string(),
                event_type: "health.observed".to_string(),
                summary: "Modelētais diagnostics timeline gaida pirmos live incidentus.".to_string(),
                created_at: chrono::Utc::now().to_rfc3339(),
            },
            DiagnosticTimelineEventRecord {
                id: Uuid::nil().to_string(),
                incident_id: Uuid::nil().to_string(),
                event_type: "guardrail.ready".to_string(),
                summary: "Fallback, safe-mode un approval escalation guardrails ir manifestēti operatoram."
                    .to_string(),
                created_at: chrono::Utc::now().to_rfc3339(),
            },
        ]);
    };
    let rows = sqlx::query(
        r#"
        SELECT id, incident_id, event_type, summary, created_at
        FROM diagnostic_timeline_events
        ORDER BY created_at DESC
        LIMIT 12
        "#,
    )
    .fetch_all(pool)
    .await
    .map_err(internal_error)?;

    Ok(rows
        .into_iter()
        .map(|row| DiagnosticTimelineEventRecord {
            id: row.get::<Uuid, _>("id").to_string(),
            incident_id: row.get::<Uuid, _>("incident_id").to_string(),
            event_type: row.get("event_type"),
            summary: row.get("summary"),
            created_at: row.get::<chrono::DateTime<chrono::Utc>, _>("created_at").to_rfc3339(),
        })
        .collect())
}

async fn load_guardrail_actions(
    pool: Option<&sqlx::PgPool>,
) -> Result<Vec<GuardrailActionRecord>, (StatusCode, Json<serde_json::Value>)> {
    let Some(pool) = pool else {
        return Ok(vec![GuardrailActionRecord {
            id: Uuid::nil().to_string(),
            incident_id: None,
            action_type: "safe_mode".to_string(),
            trigger_signal: "platform.foundation".to_string(),
            status: "planned".to_string(),
            scope: "operator_os".to_string(),
            summary: "Pie degradācijas jāspēj automātiski samazināt risku un prasīt operator approval."
                .to_string(),
            created_at: chrono::Utc::now().to_rfc3339(),
        }]);
    };
    let rows = sqlx::query(
        r#"
        SELECT id, incident_id, action_type, trigger_signal, status, scope, summary, created_at
        FROM diagnostic_guardrail_actions
        ORDER BY created_at DESC
        LIMIT 8
        "#,
    )
    .fetch_all(pool)
    .await
    .map_err(internal_error)?;

    Ok(rows
        .into_iter()
        .map(|row| GuardrailActionRecord {
            id: row.get::<Uuid, _>("id").to_string(),
            incident_id: row
                .get::<Option<Uuid>, _>("incident_id")
                .map(|value| value.to_string()),
            action_type: row.get("action_type"),
            trigger_signal: row.get("trigger_signal"),
            status: row.get("status"),
            scope: row.get("scope"),
            summary: row.get("summary"),
            created_at: row.get::<chrono::DateTime<chrono::Utc>, _>("created_at").to_rfc3339(),
        })
        .collect())
}

fn build_health_signals(
    avg_tool_latency_ms: Option<i64>,
    leaderboard: &[LeaderboardEntry],
    recommendations: &[ImprovementRecommendationRecord],
    active_guardrails: i64,
) -> Vec<HealthSignalStatus> {
    let average_outcome = if leaderboard.is_empty() {
        None
    } else {
        Some(
            leaderboard
                .iter()
                .filter_map(|entry| entry.avg_outcome_score)
                .sum::<f64>()
                / leaderboard
                    .iter()
                    .filter(|entry| entry.avg_outcome_score.is_some())
                    .count()
                    .max(1) as f64,
        )
    };
    let latency_signal = match avg_tool_latency_ms {
        Some(latency) if latency > LATENCY_DEGRADED_THRESHOLD_MS => {
            ("degraded", Some(HEALTH_SCORE_DEGRADED), "Tool latency pārsniedz drošo robežu.")
        }
        Some(latency) if latency > LATENCY_WATCH_THRESHOLD_MS => (
            "watch",
            Some(HEALTH_SCORE_WATCH),
            "Tool latentums pieaug un vajag throughput limiting.",
        ),
        Some(latency) => (
            "healthy",
            Some(HEALTH_SCORE_HEALTHY),
            if latency > 0 {
                "Tool latentums ir stabils."
            } else {
                "Tool latentuma dati vēl krājas."
            },
        ),
        None => ("observing", None, "Trūkst pietiekamu live latentuma datu."),
    };
    let drift_signal = match average_outcome {
        Some(score) if score < 60.0 => ("degraded", Some(score), "Eval leaderboard rāda kritisku kvalitātes kritumu."),
        Some(score) if score < 78.0 => ("watch", Some(score), "Redzama branch kvalitātes svārstība."),
        Some(score) => ("healthy", Some(score), "Branch kvalitāte ir stabila."),
        None => ("observing", None, "Vēl nav pietiekami outcome benchmarki."),
    };
    let memory_recommendations = recommendations
        .iter()
        .filter(|item| item.target == "memory")
        .count();
    let memory_signal = match memory_recommendations {
        count if count >= 3 => ("degraded", Some(49.0), "Bieži memory miss ieteikumi liecina par retrieval problēmām."),
        1 | 2 => ("watch", Some(71.0), "Ir memory kvalitātes signāli, kurus jāpieskata."),
        _ => ("healthy", Some(86.0), "Nav novērots memory miss klasteris."),
    };
    let tool_signal = match active_guardrails {
        count if count >= 3 => ("degraded", Some(44.0), "Daudzi guardrail triggeri norāda uz tool nestabilitāti."),
        1 | 2 => (
            "watch",
            Some(HEALTH_SCORE_TOOL_MEMORY_WATCH),
            "Daļa rīku darbojas nestabili, guardrails jau reaģē.",
        ),
        _ => ("healthy", Some(HEALTH_SCORE_HEALTHY_ALT), "Tools šobrīd izskatās stabili."),
    };

    vec![
        HealthSignalStatus {
            signal: "model_drift".to_string(),
            status: drift_signal.0.to_string(),
            summary: drift_signal.2.to_string(),
            score: drift_signal.1,
            source: if average_outcome.is_some() { "live" } else { "manifest_seed" },
        },
        HealthSignalStatus {
            signal: "latency_spikes".to_string(),
            status: latency_signal.0.to_string(),
            summary: latency_signal.2.to_string(),
            score: latency_signal.1,
            source: if avg_tool_latency_ms.is_some() { "live" } else { "manifest_seed" },
        },
        HealthSignalStatus {
            signal: "memory_quality".to_string(),
            status: memory_signal.0.to_string(),
            summary: memory_signal.2.to_string(),
            score: memory_signal.1,
            source: if recommendations.is_empty() { "manifest_seed" } else { "live" },
        },
        HealthSignalStatus {
            signal: "tool_instability".to_string(),
            status: tool_signal.0.to_string(),
            summary: tool_signal.2.to_string(),
            score: tool_signal.1,
            source: if active_guardrails == 0 { "manifest_seed" } else { "live" },
        },
    ]
}

fn calculate_health_score(signals: &[HealthSignalStatus]) -> f64 {
    let scores = signals.iter().filter_map(|signal| signal.score).collect::<Vec<_>>();
    if scores.is_empty() {
        75.0
    } else {
        (scores.iter().sum::<f64>() / scores.len() as f64 * 100.0).round() / 100.0
    }
}

fn validate_submit_request(
    req: &SubmitTaskOutcomeRequest,
) -> Result<(), (StatusCode, Json<serde_json::Value>)> {
    if req.agent_branch.trim().is_empty()
        || req.evaluator.trim().is_empty()
        || req.suite_name.trim().is_empty()
    {
        return Err(error(
            StatusCode::BAD_REQUEST,
            "agent_branch, evaluator un suite_name ir obligāti",
        ));
    }
    if !(0.0..=100.0).contains(&req.outcome_score) {
        return Err(error(
            StatusCode::BAD_REQUEST,
            "outcome_score jābūt diapazonā no 0 līdz 100",
        ));
    }
    if let Some(score) = req.token_efficiency_score {
        if !(0.0..=100.0).contains(&score) {
            return Err(error(
                StatusCode::BAD_REQUEST,
                "token_efficiency_score jābūt diapazonā no 0 līdz 100",
            ));
        }
    }
    Ok(())
}

fn classify_outcome(score: f64) -> &'static str {
    if score >= 90.0 {
        "excellent"
    } else if score >= 70.0 {
        "passing"
    } else if score >= 50.0 {
        "needs_review"
    } else {
        "failed"
    }
}

fn normalize_agent_task_status(
    status: Option<&str>,
) -> Result<Option<String>, (StatusCode, Json<serde_json::Value>)> {
    let Some(status) = status.map(str::trim).filter(|value| !value.is_empty()) else {
        return Ok(None);
    };

    match status {
        "queued" | "running" | "completed" | "failed" | "cancelled" => {
            Ok(Some(status.to_string()))
        }
        _ => Err(error(
            StatusCode::BAD_REQUEST,
            "regression_status jābūt queued, running, completed, failed vai cancelled",
        )),
    }
}

fn build_recommendations(req: &SubmitTaskOutcomeRequest) -> Vec<GeneratedRecommendation> {
    let signals = req.signals.as_ref().cloned().unwrap_or_default();
    let mut recommendations = Vec::new();

    if req.outcome_score < 70.0 || signals.prompt_clarity_issues.unwrap_or(0) > 0 {
        recommendations.push(GeneratedRecommendation {
            target: "prompt",
            title: "Prompt contract needs tightening".to_string(),
            summary:
                "Nepieciešams precizēt sistēmas promptu, success criteria and task decomposition instrukcijas."
                    .to_string(),
            confidence_score: 72.0
                + f64::from(signals.prompt_clarity_issues.unwrap_or(0)).min(20.0),
        });
    }
    if signals.policy_violations.unwrap_or(0) > 0 {
        recommendations.push(GeneratedRecommendation {
            target: "policy",
            title: "Policy guardrails should be reinforced".to_string(),
            summary:
                "Neveiksmīgā sesija norāda uz policy pārkāpuma riskiem; vajag stingrākus approval vai refusal noteikumus."
                    .to_string(),
            confidence_score: 80.0
                + f64::from(signals.policy_violations.unwrap_or(0)).min(15.0),
        });
    }
    if signals.tool_failures.unwrap_or(0) > 0 || req.latency_ms.unwrap_or_default() > 2_000 {
        recommendations.push(GeneratedRecommendation {
            target: "tool",
            title: "Tool runtime path should be optimized".to_string(),
            summary:
                "Retry vai liels latentums rāda, ka jāoptimizē tool izvēle, job scheduling vai timeout politika."
                    .to_string(),
            confidence_score: if req.latency_ms.unwrap_or_default() > 2_000 {
                78.0
            } else {
                74.0 + f64::from(signals.tool_failures.unwrap_or(0)).min(18.0)
            },
        });
    }
    if signals.memory_misses.unwrap_or(0) > 0 {
        recommendations.push(GeneratedRecommendation {
            target: "memory",
            title: "Memory retrieval should be improved".to_string(),
            summary:
                "Cross-session vai cross-lingual memory retrieval izskatās nepietiekams konkrētajam uzdevumam."
                    .to_string(),
            confidence_score: 76.0
                + f64::from(signals.memory_misses.unwrap_or(0)).min(18.0),
        });
    }

    if recommendations.is_empty() && req.outcome_score < 50.0 {
        recommendations.push(GeneratedRecommendation {
            target: "prompt",
            title: "Fallback improvement path generated".to_string(),
            summary:
                "Ļoti zems score bez diagnostikas signāliem — sākuma punkts ir prompt un task policy pārskatīšana."
                    .to_string(),
            confidence_score: 68.0,
        });
    }

    recommendations
}

fn internal_error(err: impl std::fmt::Display) -> (StatusCode, Json<serde_json::Value>) {
    error(StatusCode::INTERNAL_SERVER_ERROR, &err.to_string())
}

fn error(status: StatusCode, message: &str) -> (StatusCode, Json<serde_json::Value>) {
    (status, Json(serde_json::json!({ "error": message })))
}

#[cfg(test)]
mod tests {
    use super::{
        build_health_signals, build_recommendations, calculate_health_score, classify_outcome,
        normalize_agent_task_status, OutcomeSignals, SubmitTaskOutcomeRequest,
    };

    #[test]
    fn classifies_outcomes_with_expected_buckets() {
        assert_eq!(classify_outcome(95.0), "excellent");
        assert_eq!(classify_outcome(75.0), "passing");
        assert_eq!(classify_outcome(55.0), "needs_review");
        assert_eq!(classify_outcome(35.0), "failed");
    }

    #[test]
    fn recommendation_builder_maps_failure_signals_to_targets() {
        let recommendations = build_recommendations(&SubmitTaskOutcomeRequest {
            agent_session_id: uuid::Uuid::nil().to_string(),
            task_id: uuid::Uuid::nil().to_string(),
            agent_branch: "planner".to_string(),
            evaluator: "nightly".to_string(),
            suite_name: "task_regression_grid".to_string(),
            outcome_score: 42.0,
            latency_ms: Some(3_100),
            token_efficiency_score: Some(34.0),
            regression_status: Some("failed".to_string()),
            feedback: None,
            evidence: None,
            signals: Some(OutcomeSignals {
                policy_violations: Some(1),
                tool_failures: Some(2),
                memory_misses: Some(1),
                prompt_clarity_issues: Some(1),
            }),
        });

        let targets = recommendations
            .iter()
            .map(|item| item.target)
            .collect::<Vec<_>>();
        assert_eq!(targets, vec!["prompt", "policy", "tool", "memory"]);
    }

    #[test]
    fn regression_status_validation_rejects_unknown_values() {
        assert_eq!(
            normalize_agent_task_status(Some("completed")).unwrap(),
            Some("completed".to_string())
        );
        assert!(normalize_agent_task_status(Some("oops")).is_err());
    }

    #[test]
    fn health_signals_produce_platform_score() {
        let signals = build_health_signals(
            Some(1_200),
            &[super::LeaderboardEntry {
                branch: "planner".to_string(),
                evaluations: 4,
                avg_outcome_score: Some(74.0),
                avg_latency_ms: Some(900),
                failed_evaluations: 1,
                source: "live",
            }],
            &[super::ImprovementRecommendationRecord {
                id: uuid::Uuid::nil().to_string(),
                agent_session_id: uuid::Uuid::nil().to_string(),
                source_task_id: None,
                target: "memory".to_string(),
                title: "Memory drift".to_string(),
                summary: "Need retrieval tuning".to_string(),
                confidence_score: 82.0,
                created_at: "2026-04-04T00:00:00Z".to_string(),
            }],
            1,
        );

        assert_eq!(signals.len(), 4);
        assert!(calculate_health_score(&signals) > 0.0);
    }
}