Monitoring & Continuous Improvement Agent

@tool(
    name="kpi_trend_and_alerts",
    description="Compute simple trend(first→last) for KPIs and raise alerts on threshold breaches.",
    show_result=True,
)
def kpi_trend_and_alerts(
    kpi_series: Dict[str, List[float]],
    alert_thresholds: Dict[str, float]
) -> Dict[str, Any]:
    """
    Trend KPIs and compare last value to threshold.

    Args:
        kpi_series: {kpi_name: [v1, v2, ..., vn]}
        alert_thresholds: {kpi_name: threshold_max_allowed} (alert if last > threshold)

    Returns:
        Dict with trend per KPI and alert flags.
    """
    output: Dict[str, Any] = {"kpis": {}}
    for name, series in kpi_series.items():
        if not series:
            output["kpis"][name] = {"error": "Empty series."}
            continue
        first, last = series[0], series[-1]
        trend_abs = last - first
        trend_pct = (trend_abs / first) if first != 0 else 0.0
        threshold = alert_thresholds.get(name, None)
        alert = (threshold is not None) and (last > threshold)
        output["kpis"][name] = {
            "first": round(first, 2),
            "last": round(last, 2),
            "trend_abs": round(trend_abs, 2),
            "trend_pct": round(trend_pct, 4),
            "threshold": threshold,
            "alert": alert
        }
    return output

@tool(
    name="detect_model_drift_simple",
    description="Compare baseline vs current model metrics; flag drift if relative change exceeds threshold.",
    show_result=True,
)
def detect_model_drift_simple(
    baseline_metrics: Dict[str, float],
    current_metrics: Dict[str, float],
    drift_threshold: float = 0.1
) -> Dict[str, Any]:
    """
    Simple drift detector on scalar metrics(e.g., MAE, AUC).

    Args:
        baseline_metrics: {metric: value}
        current_metrics: {metric: value}
        drift_threshold: relative change(e.g., 0.1 for 10%)

    Returns:
        Dict with per-metric drift pct and alerts.
    """
    results: Dict[str, Dict[str, Any]] = {}
    for m, base in baseline_metrics.items():
        cur = current_metrics.get(m, base)
        if base == 0:
            drift = 0.0 if cur == 0 else float("inf")
        else:
            drift = (cur - base) / base
        results[m] = {
            "baseline": round(base, 6),
            "current": round(cur, 6),
            "drift_pct": (None if drift == float("inf") else round(drift, 6)),
            "alert": (abs(drift) > drift_threshold) if drift != float("inf") else True
        }
    return {
        "drift_threshold": drift_threshold,
        "metrics": results
    }

@tool(
    name="build_dashboard_snapshot",
    description="Assemble a dashboard snapshot from named sections(KPIs, Risks, Actions).",
    show_result=True,
)
def build_dashboard_snapshot(
    sections: Dict[str, Dict[str, Any]]
) -> Dict[str, Any]:
    """
    Package a simple dashboard payload for UI rendering/logging.

    Args:
        sections: {"KPIs": {...}, "Risks": {...}, "Actions": {...}, ...}

    Returns:
        Dict with normalized keys and counts for each section.
    """
    normalized = {}
    counts = {}
    for sec, payload in sections.items():
        normalized[sec] = payload
        counts[sec] = (len(payload) if isinstance(payload, dict) else 1)
    return {
        "snapshot": normalized,
        "section_counts": counts,
        "generated_at": datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S UTC")
    }