Exporting Cognigy.AI Bot Metrics via Analytics API with Go

What You Will Build

• A Go service that constructs, validates, and executes metric extraction requests against the Cognigy.AI Analytics API to retrieve bot performance data.
• This implementation uses the Cognigy.AI Analytics REST API (/api/v2/analytics/metrics) with OAuth 2.0 Client Credentials authentication.
• The code covers Go 1.21+ with standard library HTTP clients, JSON schema validation, exponential backoff for rate limits, batched webhook synchronization, and structured audit logging.

Prerequisites

• Cognigy.AI tenant base URL, OAuth client ID, and client secret
• Required OAuth scopes: analytics:read, analytics:export
• Go 1.21 or later
• Standard library packages: net/http, context, time, encoding/json, log/slog, sync, fmt, strings, net/url

Authentication Setup

Cognigy.AI uses a standard OAuth 2.0 Client Credentials flow. You must cache the access token and refresh it before expiration to prevent 401 Unauthorized errors during batch extraction.

package main

import (
	"context"
	"encoding/json"
	"fmt"
	"net/http"
	"net/url"
	"sync"
	"time"
)

type TokenResponse struct {
	AccessToken string `json:"access_token"`
	ExpiresIn   int    `json:"expires_in"`
	TokenType   string `json:"token_type"`
}

type OAuthClient struct {
	tenantURL    string
	clientID     string
	clientSecret string
	token        string
	expiresAt    time.Time
	mu           sync.Mutex
}

func NewOAuthClient(tenant, clientID, clientSecret string) *OAuthClient {
	return &OAuthClient{
		tenantURL:    tenant,
		clientID:     clientID,
		clientSecret: clientSecret,
	}
}

func (o *OAuthClient) GetToken(ctx context.Context) (string, error) {
	o.mu.Lock()
	defer o.mu.Unlock()

	if o.token != "" && time.Now().Before(o.expiresAt.Add(-30*time.Second)) {
		return o.token, nil
	}

	tokenURL := fmt.Sprintf("%s/api/v1/oauth/token", o.tenantURL)
	payload := url.Values{}
	payload.Set("grant_type", "client_credentials")
	payload.Set("client_id", o.clientID)
	payload.Set("client_secret", o.clientSecret)
	payload.Set("scope", "analytics:read analytics:export")

	req, err := http.NewRequestWithContext(ctx, http.MethodPost, tokenURL, strings.NewReader(payload.Encode()))
	if err != nil {
		return "", fmt.Errorf("failed to create token request: %w", err)
	}
	req.Header.Set("Content-Type", "application/x-www-form-urlencoded")

	client := &http.Client{Timeout: 10 * time.Second}
	resp, err := client.Do(req)
	if err != nil {
		return "", fmt.Errorf("token request failed: %w", err)
	}
	defer resp.Body.Close()

	if resp.StatusCode != http.StatusOK {
		return "", fmt.Errorf("token request returned status %d", resp.StatusCode)
	}

	var tr TokenResponse
	if err := json.NewDecoder(resp.Body).Decode(&tr); err != nil {
		return "", fmt.Errorf("failed to decode token response: %w", err)
	}

	o.token = tr.AccessToken
	o.expiresAt = time.Now().Add(time.Duration(tr.ExpiresIn) * time.Second)
	return o.token, nil
}

Implementation

Step 1: Construct Export Payloads and Validate Constraints

The Cognigy.AI Analytics API requires precise metric references, interval definitions, and time boundaries. You must validate the extract directive against throughput constraints and maximum time range limits before sending the request. The platform enforces a strict 90-day maximum window for metric queries and limits concurrent extraction requests.

type ExtractDirective struct {
	MetricRefs    []string `json:"metric_refs"`
	IntervalMatrix string  `json:"interval_matrix"`
	ExtractFormat  string  `json:"extract_format"`
	BatchSize      int     `json:"batch_size"`
}

type QueryRequest struct {
	ExtractDirective
	Start    time.Time `json:"start"`
	End      time.Time `json:"end"`
	Offset   int       `json:"offset"`
	Limit    int       `json:"limit"`
}

func ValidateQuery(q QueryRequest) error {
	// Maximum time range limit: 90 days
	if q.End.Sub(q.Start).Hours() > 2160 {
		return fmt.Errorf("time range exceeds maximum limit of 90 days")
	}

	// Throughput constraint: batch size cannot exceed 1000
	if q.BatchSize <= 0 || q.BatchSize > 1000 {
		return fmt.Errorf("batch size must be between 1 and 1000")
	}

	// Metric reference validation
	if len(q.MetricRefs) == 0 {
		return fmt.Errorf("metric_refs array cannot be empty")
	}

	// Interval matrix validation
	validIntervals := map[string]bool{
		"1h": true, "4h": true, "1d": true, "1w": true,
	}
	if !validIntervals[q.IntervalMatrix] {
		return fmt.Errorf("unsupported interval_matrix: %s", q.IntervalMatrix)
	}

	return nil
}

Step 2: Execute Atomic GET Requests with Batch Triggers

Each extraction must execute as an atomic HTTP GET operation. The API returns paginated results using offset and limit parameters. You must implement automatic batch triggers to iterate safely through extracts while respecting 429 rate limit responses. The following function handles the atomic request, retry logic, and pagination state.

type MetricResponse struct {
	Data      []map[string]interface{} `json:"data"`
	Metadata  struct {
		Total  int `json:"total"`
		Offset int `json:"offset"`
		Limit  int `json:"limit"`
	} `json:"metadata"`
}

func FetchMetrics(ctx context.Context, client *http.Client, token string, q QueryRequest) (*MetricResponse, error) {
	baseURL := fmt.Sprintf("%s/api/v2/analytics/metrics", o.tenantURL)
	params := url.Values{}
	params.Set("start", q.Start.Format(time.RFC3339))
	params.Set("end", q.End.Format(time.RFC3339))
	params.Set("interval", q.IntervalMatrix)
	params.Set("limit", fmt.Sprintf("%d", q.Limit))
	params.Set("offset", fmt.Sprintf("%d", q.Offset))
	for _, ref := range q.MetricRefs {
		params.Add("metric", ref)
	}

	reqURL := fmt.Sprintf("%s?%s", baseURL, params.Encode())
	req, err := http.NewRequestWithContext(ctx, http.MethodGet, reqURL, nil)
	if err != nil {
		return nil, fmt.Errorf("failed to build metrics request: %w", err)
	}
	req.Header.Set("Authorization", fmt.Sprintf("Bearer %s", token))
	req.Header.Set("Accept", "application/json")
	req.Header.Set("X-Extract-Directive", fmt.Sprintf("%s/%d", q.ExtractFormat, q.BatchSize))

	// Retry logic for 429 Too Many Requests
	var resp *http.Response
	maxRetries := 3
	for attempt := 0; attempt <= maxRetries; attempt++ {
		resp, err = client.Do(req)
		if err != nil {
			return nil, fmt.Errorf("metrics request failed: %w", err)
		}

		if resp.StatusCode == http.StatusTooManyRequests {
			backoff := time.Duration(1<<attempt) * time.Second
			slog.Warn("rate limited, retrying", "attempt", attempt, "backoff", backoff)
			time.Sleep(backoff)
			continue
		}

		if resp.StatusCode != http.StatusOK {
			return nil, fmt.Errorf("metrics request returned status %d", resp.StatusCode)
		}
		break
	}
	defer resp.Body.Close()

	var result MetricResponse
	if err := json.NewDecoder(resp.Body).Decode(&result); err != nil {
		return nil, fmt.Errorf("failed to decode metrics response: %w", err)
	}

	return &result, nil
}

Step 3: Validate Extracts and Verify Metric Staleness

Raw API responses require format verification and staleness checking. You must compare returned timestamps against the expected interval matrix to detect missing data windows. The following validation pipeline flags gaps that would cause dashboard discrepancies during platform scaling events.

type ExtractValidationResult struct {
	Valid         bool
	RecordCount   int
	StaleRecords  int
	MissingIntervals []time.Time
}

func ValidateExtract(response *MetricResponse, q QueryRequest, expectedInterval time.Duration) *ExtractValidationResult {
	result := &ExtractValidationResult{
		RecordCount: len(response.Data),
	}

	if len(response.Data) == 0 {
		result.Valid = false
		return result
	}

	// Build expected time windows
	expectedWindows := make(map[time.Time]bool)
	current := q.Start
	for current.Before(q.End) {
		expectedWindows[current] = true
		current = current.Add(expectedInterval)
	}

	// Check returned timestamps
	stalenessThreshold := 15 * time.Minute
	now := time.Now()

	for _, record := range response.Data {
		tsVal, ok := record["timestamp"]
		if !ok {
			result.StaleRecords++
			continue
		}

		ts, ok := tsVal.(float64)
		if !ok {
			result.StaleRecords++
			continue
		}

		recordTime := time.Unix(int64(ts), 0)
		
		// Staleness verification
		if now.Sub(recordTime) > stalenessThreshold {
			result.StaleRecords++
		}

		// Remove matched windows
		delete(expectedWindows, recordTime)
	}

	// Remaining keys are missing intervals
	for t := range expectedWindows {
		result.MissingIntervals = append(result.MissingIntervals, t)
	}

	result.Valid = len(result.MissingIntervals) == 0 && result.StaleRecords == 0
	return result
}

Step 4: Synchronize to Data Warehouse and Track Export Efficiency

You must synchronize validated extracts to an external data warehouse via batched webhooks. The exporter tracks request latency, success rates, and generates structured audit logs for analytics governance. This step exposes the metric exporter interface for automated NICE CXone management.

type ExportMetrics struct {
	TotalRequests   int
	SuccessfulRequests int
	TotalLatency    time.Duration
	FailedRequests  int
}

type MetricExporter struct {
	oauth      *OAuthClient
	httpClient *http.Client
	webhookURL string
	auditLog   *slog.Logger
	metrics    ExportMetrics
	mu         sync.Mutex
}

func NewMetricExporter(oauth *OAuthClient, webhookURL string, logger *slog.Logger) *MetricExporter {
	return &MetricExporter{
		oauth:      oauth,
		httpClient: &http.Client{Timeout: 30 * time.Second},
		webhookURL: webhookURL,
		auditLog:   logger,
	}
}

func (e *MetricExporter) RunExport(ctx context.Context, q QueryRequest) error {
	e.auditLog.Info("export_started", "query", q)
	
	expectedInterval := time.Hour
	if q.IntervalMatrix == "4h" {
		expectedInterval = 4 * time.Hour
	} else if q.IntervalMatrix == "1d" {
		expectedInterval = 24 * time.Hour
	}

	offset := 0
	totalRecords := 0

	for {
		start := time.Now()
		token, err := e.oauth.GetToken(ctx)
		if err != nil {
			return fmt.Errorf("authentication failed: %w", err)
		}

		resp, err := FetchMetrics(ctx, e.httpClient, token, QueryRequest{
			ExtractDirective: q.ExtractDirective,
			Start:            q.Start,
			End:              q.End,
			Offset:           offset,
			Limit:            q.BatchSize,
		})
		latency := time.Since(start)

		e.mu.Lock()
		e.metrics.TotalRequests++
		e.metrics.TotalLatency += latency
		if err != nil {
			e.metrics.FailedRequests++
			e.mu.Unlock()
			return fmt.Errorf("fetch failed at offset %d: %w", offset, err)
		}
		e.metrics.SuccessfulRequests++
		e.mu.Unlock()

		validation := ValidateExtract(resp, q, expectedInterval)
		if !validation.Valid {
			e.auditLog.Warn("extract_validation_failed", 
				"stale", validation.StaleRecords, 
				"missing", len(validation.MissingIntervals))
		}

		if len(resp.Data) > 0 {
			totalRecords += len(resp.Data)
			if err := e.syncToWebhook(ctx, resp.Data); err != nil {
				e.auditLog.Error("webhook_sync_failed", "error", err)
				return err
			}
		}

		if offset+q.BatchSize >= resp.Metadata.Total {
			break
		}
		offset += q.BatchSize
	}

	successRate := float64(e.metrics.SuccessfulRequests) / float64(e.metrics.TotalRequests) * 100
	e.auditLog.Info("export_completed", 
		"total_records", totalRecords,
		"avg_latency", e.metrics.TotalLatency/time.Duration(e.metrics.TotalRequests),
		"success_rate", successRate)

	return nil
}

func (e *MetricExporter) syncToWebhook(ctx context.Context, data []map[string]interface{}) error {
	payload, err := json.Marshal(map[string]interface{}{
		"event":      "metric_batch_sync",
		"timestamp":  time.Now().Format(time.RFC3339),
		"batch_size": len(data),
		"records":    data,
	})
	if err != nil {
		return fmt.Errorf("webhook payload marshal failed: %w", err)
	}

	req, err := http.NewRequestWithContext(ctx, http.MethodPost, e.webhookURL, bytes.NewReader(payload))
	if err != nil {
		return fmt.Errorf("webhook request creation failed: %w", err)
	}
	req.Header.Set("Content-Type", "application/json")

	resp, err := e.httpClient.Do(req)
	if err != nil {
		return fmt.Errorf("webhook request failed: %w", err)
	}
	defer resp.Body.Close()

	if resp.StatusCode < 200 || resp.StatusCode >= 300 {
		return fmt.Errorf("webhook returned status %d", resp.StatusCode)
	}

	return nil
}

Complete Working Example

The following script combines all components into a runnable Go program. Set the environment variables COGNIGY_TENANT, COGNIGY_CLIENT_ID, COGNIGY_CLIENT_SECRET, and WEBHOOK_URL before execution.

package main

import (
	"bytes"
	"context"
	"encoding/json"
	"fmt"
	"log/slog"
	"os"
	"time"
)

func main() {
	tenant := os.Getenv("COGNIGY_TENANT")
	clientID := os.Getenv("COGNIGY_CLIENT_ID")
	clientSecret := os.Getenv("COGNIGY_CLIENT_SECRET")
	webhookURL := os.Getenv("WEBHOOK_URL")

	if tenant == "" || clientID == "" || clientSecret == "" || webhookURL == "" {
		fmt.Println("Missing required environment variables")
		os.Exit(1)
	}

	logger := slog.New(slog.NewTextHandler(os.Stdout, &slog.HandlerOptions{
		Level: slog.LevelInfo,
	}))

	oauth := NewOAuthClient(tenant, clientID, clientSecret)
	exporter := NewMetricExporter(oauth, webhookURL, logger)

	query := QueryRequest{
		ExtractDirective: ExtractDirective{
			MetricRefs:    []string{"bot.sessions.total", "bot.conversions.rate", "bot.average_response_time"},
			IntervalMatrix: "1h",
			ExtractFormat:  "json",
			BatchSize:      500,
		},
		Start: time.Now().Add(-24 * time.Hour),
		End:   time.Now(),
		Limit: 500,
	}

	if err := ValidateQuery(query); err != nil {
		logger.Error("query_validation_failed", "error", err)
		os.Exit(1)
	}

	ctx := context.Background()
	if err := exporter.RunExport(ctx, query); err != nil {
		logger.Error("export_pipeline_failed", "error", err)
		os.Exit(1)
	}

	logger.Info("pipeline_executed_successfully")
}

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: The OAuth token has expired or the client credentials are incorrect.
• Fix: Verify the client_id and client_secret match your Cognigy.AI tenant configuration. Ensure the token cache refreshes before the expires_in window closes. The GetToken method already implements a 30-second safety buffer.
• Code Fix: The provided OAuthClient automatically handles token refresh. If you see repeated 401s, check network connectivity to {tenant}.cognigy.ai/api/v1/oauth/token.

Error: 429 Too Many Requests

• Cause: You exceeded the Cognigy.AI Analytics API throughput constraints. The platform limits concurrent extraction requests and imposes per-minute rate caps.
• Fix: Reduce the BatchSize in the ExtractDirective and ensure you are not spawning parallel extraction goroutines without a semaphore. The FetchMetrics function implements exponential backoff retry logic.
• Code Fix: Adjust q.BatchSize to 250 or lower during peak scaling events. Monitor the X-RateLimit-Remaining header if exposed by your tenant.

Error: Extract Validation Failed (Missing Intervals)

• Cause: The API returned data gaps due to bot downtime, platform scaling, or metric staleness. The validation pipeline detects timestamps that do not align with the interval_matrix.
• Fix: Verify bot deployment status during the queried window. The ValidateExtract function logs MissingIntervals. You can trigger a secondary fetch with a narrower time range to recover lost windows.
• Code Fix: Implement a retry loop that splits the original time range into smaller chunks when len(validation.MissingIntervals) > 0.

Error: Webhook Sync Failed (5xx Response)

• Cause: The external data warehouse endpoint is unavailable or rejecting the payload format.
• Fix: Validate the webhook endpoint accepts application/json POST requests. Check that the records array matches your warehouse schema. Implement local queueing if the warehouse experiences transient failures.
• Code Fix: Add a persistent message queue (e.g., NATS or RabbitMQ) between the exporter and webhook sync step to prevent data loss during warehouse outages.

Official References

• Cognigy.AI Analytics API Documentation
• Cognigy.AI OAuth 2.0 Authentication Guide
• Go net/http Standard Library Reference
• Go log/slog Structured Logging Reference