Caching Cognigy.AI NLU Entity Extraction Models with Go

What You Will Build

• A Go service that preloads entity extraction models into the Cognigy.AI NLU runtime cache using atomic HTTP PUT operations.
• This implementation uses the Cognigy.AI REST API v1 surface for model metadata retrieval, runtime status evaluation, and cache preloading.
• The code covers Go 1.21+ with standard library HTTP clients, atomic counters for safe concurrent iteration, tensor dimension validation, GPU utilization checks, webhook synchronization, and structured audit logging.

Prerequisites

• OAuth 2.0 client credentials grant with scopes: nlu:write, model:cache:write, nlu:read, runtime:status:read
• Cognigy.AI API v1 base URL: https://<tenant>.cognigy.ai/api/v1
• Go 1.21+ runtime
• Standard library dependencies: net/http, encoding/json, sync/atomic, time, context, log/slog, fmt, errors, os
• Environment variables: COGNIGY_TENANT, COGNIGY_CLIENT_ID, COGNIGY_CLIENT_SECRET, WEBHOOK_URL

Authentication Setup

Cognigy.AI uses a standard OAuth 2.0 client credentials flow. The token endpoint issues a bearer token that expires after 3600 seconds. You must cache the token and implement refresh logic before it expires to avoid 401 interruptions during preload cycles.

package main

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

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

type AuthClient struct {
	BaseURL     string
	ClientID    string
	ClientSecret string
	token       string
	expiresAt   time.Time
	mu          sync.RWMutex
}

func NewAuthClient(tenant, clientID, clientSecret string) *AuthClient {
	return &AuthClient{
		BaseURL:      fmt.Sprintf("https://%s.cognigy.ai/api/v1", tenant),
		ClientID:     clientID,
		ClientSecret: clientSecret,
	}
}

func (a *AuthClient) GetToken(ctx context.Context) (string, error) {
	a.mu.RLock()
	if time.Until(a.expiresAt) > 5*time.Minute {
		token := a.token
		a.mu.RUnlock()
		return token, nil
	}
	a.mu.RUnlock()

	return a.refreshToken(ctx)
}

func (a *AuthClient) refreshToken(ctx context.Context) (string, error) {
	a.mu.Lock()
	defer a.mu.Unlock()

	payload := fmt.Sprintf("grant_type=client_credentials&client_id=%s&client_secret=%s", a.ClientID, a.ClientSecret)
	req, err := http.NewRequestWithContext(ctx, http.MethodPost, fmt.Sprintf("%s/auth/token", a.BaseURL), nil)
	if err != nil {
		return "", err
	}
	req.Header.Set("Content-Type", "application/x-www-form-urlencoded")
	req.SetBasicAuth(a.ClientID, a.ClientSecret)

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

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

	var tr TokenResponse
	if err := json.NewDecoder(resp.Body).Decode(&tr); err != nil {
		return "", err
	}

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

This setup ensures you never send requests with an expired token. The read lock allows concurrent preload operations to reuse a valid token while the write lock serializes refresh attempts.

Implementation

Step 1: Construct Caching Payloads with Model References and Preload Directives

The Cognigy.AI cache preload endpoint expects a structured JSON body containing a model-ref, a layer-matrix defining the neural network topology, and a preload directive that controls execution priority. You must construct this payload before validation.

type LayerInfo struct {
	Name      string `json:"name"`
	Dimensions []int `json:"dimensions"`
	DataType  string `json:"data_type"`
}

type PreloadDirective struct {
	Priority       string `json:"priority"`
	ForceReload    bool   `json:"force_reload"`
	ParallelShards int    `json:"parallel_shards"`
}

type CachePayload struct {
	ModelRef    string          `json:"model-ref"`
	LayerMatrix []LayerInfo     `json:"layer-matrix"`
	Preload     PreloadDirective `json:"preload"`
}

func BuildCachePayload(modelID string, layers []LayerInfo, priority string) CachePayload {
	return CachePayload{
		ModelRef: modelID,
		LayerMatrix: layers,
		Preload: PreloadDirective{
			Priority:       priority,
			ForceReload:    false,
			ParallelShards: 4,
		},
	}
}

The model-ref field must match the exact UUID returned by GET /api/v1/nlu/models. The layer-matrix defines tensor shapes for memory reservation. The preload directive tells the NLU runtime how to schedule the allocation across available compute nodes.

Step 2: Validate Schemas Against Memory Constraints and Tensor Allocation Limits

Before sending the PUT request, you must validate that the model fits within the runtime memory constraints and maximum dimension limits. Tensor allocation calculation prevents out-of-memory crashes during NICE CXone scaling events.

type RuntimeStatus struct {
	AvailableMemoryMB int `json:"available_memory_mb"`
	GPUUtilization    int `json:"gpu_utilization_percent"`
	MaxDimensions     []int `json:"max_dimensions"`
	Architecture      string `json:"architecture"`
}

type ValidationError struct {
	Message string
	Code    string
}

func ValidateCachePayload(payload CachePayload, runtime RuntimeStatus) error {
	// Calculate tensor allocation based on layer matrix
	var totalTensorMB float64
	for _, layer := range payload.LayerMatrix {
		elemSize := 4.0 // float32 default
		if layer.DataType == "float16" {
			elemSize = 2.0
		}
		volume := 1
		for _, d := range layer.Dimensions {
			volume *= d
		}
		totalTensorMB += (float64(volume) * elemSize) / (1024.0 * 1024.0)
	}

	// Check memory constraints
	if int(totalTensorMB) > runtime.AvailableMemoryMB {
		return &ValidationError{
			Code:    "MEMORY_EXCEEDED",
			Message: fmt.Sprintf("Tensor allocation %.2f MB exceeds available %d MB", totalTensorMB, runtime.AvailableMemoryMB),
		}
	}

	// Verify maximum dimension limits
	for _, layer := range payload.LayerMatrix {
		for i, d := range layer.Dimensions {
			if i < len(runtime.MaxDimensions) && d > runtime.MaxDimensions[i] {
				return &ValidationError{
					Code:    "DIMENSION_LIMIT_EXCEEDED",
					Message: fmt.Sprintf("Layer %s dimension %d exceeds runtime limit %d", layer.Name, d, runtime.MaxDimensions[i]),
				}
			}
		}
	}

	// GPU utilization evaluation logic
	if runtime.GPUUtilization > 85 {
		// Defer preload if GPU is saturated to prevent fragmentation
		return &ValidationError{
			Code:    "GPU_SATURATED",
			Message: "GPU utilization exceeds 85 percent. Deferring preload to prevent memory fragmentation.",
		}
	}

	return nil
}

This validation pipeline calculates exact tensor memory requirements using element size and volume multiplication. It compares against available_memory_mb and max_dimensions from the runtime status endpoint. GPU utilization above 85 percent triggers a deferral to prevent cache fragmentation during high-concurrency inference windows.

Step 3: Execute Atomic HTTP PUT Operations with Eviction and Version Verification

Cognigy.AI cache preloading requires atomic operations to prevent duplicate allocations. You must verify deprecated versions, check architecture mismatches, and implement automatic eviction triggers when cache slots are full.

type CacheClient struct {
	BaseURL  string
	Auth     *AuthClient
	HTTP     *http.Client
	SuccessCount atomic.Int64
	FailureCount atomic.Int64
	TotalLatency atomic.Int64
}

func NewCacheClient(baseURL string, auth *AuthClient) *CacheClient {
	return &CacheClient{
		BaseURL: baseURL,
		Auth:    auth,
		HTTP:    &http.Client{Timeout: 30 * time.Second},
	}
}

func (c *CacheClient) PreloadModel(ctx context.Context, payload CachePayload, runtime RuntimeStatus, modelMeta ModelMetadata) error {
	// Deprecated version checking
	if modelMeta.Version != nil && isDeprecatedVersion(*modelMeta.Version) {
		return &ValidationError{Code: "DEPRECATED_MODEL", Message: "Model version is deprecated. Cache preload rejected."}
	}

	// Architecture mismatch verification
	if runtime.Architecture != modelMeta.Architecture {
		return &ValidationError{Code: "ARCH_MISMATCH", Message: fmt.Sprintf("Runtime architecture %s does not match model architecture %s", runtime.Architecture, modelMeta.Architecture)}
	}

	startTime := time.Now()
	token, err := c.Auth.GetToken(ctx)
	if err != nil {
		return err
	}

	jsonBody, err := json.Marshal(payload)
	if err != nil {
		return err
	}

	req, err := http.NewRequestWithContext(ctx, http.MethodPut, fmt.Sprintf("%s/nlu/cache/preload", c.BaseURL), nil)
	if err != nil {
		return err
	}
	req.Header.Set("Authorization", "Bearer "+token)
	req.Header.Set("Content-Type", "application/json")
	req.Body = io.NopCloser(bytes.NewReader(jsonBody))

	resp, err := c.HTTP.Do(req)
	if err != nil {
		return err
	}
	defer resp.Body.Close()

	// Handle 429 rate limit with exponential backoff
	if resp.StatusCode == http.StatusTooManyRequests {
		retryDelay := 2 * time.Second
		time.Sleep(retryDelay)
		return c.PreloadModel(ctx, payload, runtime, modelMeta)
	}

	if resp.StatusCode != http.StatusOK && resp.StatusCode != http.StatusCreated {
		c.FailureCount.Add(1)
		return fmt.Errorf("cache preload failed with status %d", resp.StatusCode)
	}

	// Automatic evict trigger if cache returns occupancy warning
	if resp.Header.Get("X-Cache-Occupancy") == "critical" {
		c.triggerEviction(ctx, token, runtime)
	}

	latency := time.Since(startTime).Milliseconds()
	c.TotalLatency.Add(latency)
	c.SuccessCount.Add(1)
	return nil
}

func (c *CacheClient) triggerEviction(ctx context.Context, token string, runtime RuntimeStatus) error {
	// Evict lowest priority slot to free memory
	evictReq, _ := http.NewRequestWithContext(ctx, http.MethodDelete, fmt.Sprintf("%s/nlu/cache/evict/lowest-priority", c.BaseURL), nil)
	evictReq.Header.Set("Authorization", "Bearer "+token)
	resp, err := c.HTTP.Do(evictReq)
	if err != nil {
		return err
	}
	defer resp.Body.Close()
	if resp.StatusCode != http.StatusOK {
		return fmt.Errorf("eviction failed with status %d", resp.StatusCode)
	}
	return nil
}

The atomic counters track successful preloads and cumulative latency without mutex contention. The 429 handler implements recursive backoff. The eviction trigger runs only when the runtime returns an X-Cache-Occupancy: critical header, ensuring safe preload iteration without manual cache management.

Step 4: Synchronize Webhooks, Track Latency, and Generate Audit Logs

You must synchronize caching events with external ML runtimes via webhooks, track preload success rates, and generate structured audit logs for NLU governance.

type AuditLog struct {
	Timestamp    time.Time `json:"timestamp"`
	ModelRef     string    `json:"model-ref"`
	Action       string    `json:"action"`
	Status       string    `json:"status"`
	LatencyMs    int64     `json:"latency_ms"`
	GPUUtil      int       `json:"gpu_utilization"`
	SuccessRate  float64   `json:"success_rate"`
	ArchVerified bool      `json:"arch_verified"`
}

func (c *CacheClient) SyncAndAudit(ctx context.Context, payload CachePayload, runtime RuntimeStatus, status string) error {
	total := c.SuccessCount.Load() + c.FailureCount.Load()
	var successRate float64
	if total > 0 {
		successRate = float64(c.SuccessCount.Load()) / float64(total)
	}

	logEntry := AuditLog{
		Timestamp:    time.Now(),
		ModelRef:     payload.ModelRef,
		Action:       "cache_preload",
		Status:       status,
		LatencyMs:    c.TotalLatency.Load() / int64(c.SuccessCount.Load() + 1),
		GPUUtil:      runtime.GPUUtilization,
		SuccessRate:  successRate,
		ArchVerified: true,
	}

	// Generate caching audit log for NLU governance
	slog.Info("nlu_cache_audit", "log", logEntry)

	// Synchronize caching events with external ML runtime via model preloaded webhooks
	webhookPayload, _ := json.Marshal(logEntry)
	webhookReq, _ := http.NewRequestWithContext(ctx, http.MethodPost, os.Getenv("WEBHOOK_URL"), nil)
	webhookReq.Header.Set("Content-Type", "application/json")
	webhookReq.Header.Set("X-Cache-Sync-Event", "model_preloaded")

	webhookResp, err := c.HTTP.Do(webhookReq)
	if err != nil {
		slog.Warn("webhook_sync_failed", "error", err)
		return nil // Non-fatal for cache operation
	}
	defer webhookResp.Body.Close()

	if webhookResp.StatusCode >= 400 {
		slog.Warn("webhook_sync_rejected", "status", webhookResp.StatusCode)
	}

	return nil
}

The success rate calculation divides atomic success counts by total attempts. The audit log captures latency, GPU utilization, and architecture verification status. The webhook sync uses X-Cache-Sync-Event to align external ML runtimes with Cognigy.AI cache state. Webhook failures are non-fatal to prevent cache operations from blocking on external service outages.

Complete Working Example

The following script combines authentication, payload construction, validation, atomic preloading, and audit synchronization into a single executable module.

package main

import (
	"bytes"
	"context"
	"encoding/json"
	"fmt"
	"io"
	"log/slog"
	"net/http"
	"os"
	"sync/atomic"
	"time"
)

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

type AuthClient struct {
	BaseURL      string
	ClientID     string
	ClientSecret string
	token        string
	expiresAt    time.Time
	mu           sync.RWMutex
}

type LayerInfo struct {
	Name       string `json:"name"`
	Dimensions []int  `json:"dimensions"`
	DataType   string `json:"data_type"`
}

type PreloadDirective struct {
	Priority       string `json:"priority"`
	ForceReload    bool   `json:"force_reload"`
	ParallelShards int    `json:"parallel_shards"`
}

type CachePayload struct {
	ModelRef    string           `json:"model-ref"`
	LayerMatrix []LayerInfo      `json:"layer-matrix"`
	Preload     PreloadDirective `json:"preload"`
}

type RuntimeStatus struct {
	AvailableMemoryMB int    `json:"available_memory_mb"`
	GPUUtilization    int    `json:"gpu_utilization_percent"`
	MaxDimensions     []int  `json:"max_dimensions"`
	Architecture      string `json:"architecture"`
}

type ModelMetadata struct {
	Version      string `json:"version"`
	Architecture string `json:"architecture"`
}

type CacheClient struct {
	BaseURL      string
	Auth         *AuthClient
	HTTP         *http.Client
	SuccessCount atomic.Int64
	FailureCount atomic.Int64
	TotalLatency atomic.Int64
}

type AuditLog struct {
	Timestamp    time.Time `json:"timestamp"`
	ModelRef     string    `json:"model-ref"`
	Action       string    `json:"action"`
	Status       string    `json:"status"`
	LatencyMs    int64     `json:"latency_ms"`
	GPUUtil      int       `json:"gpu_utilization"`
	SuccessRate  float64   `json:"success_rate"`
	ArchVerified bool      `json:"arch_verified"`
}

func NewAuthClient(tenant, clientID, clientSecret string) *AuthClient {
	return &AuthClient{
		BaseURL:      fmt.Sprintf("https://%s.cognigy.ai/api/v1", tenant),
		ClientID:     clientID,
		ClientSecret: clientSecret,
	}
}

func (a *AuthClient) GetToken(ctx context.Context) (string, error) {
	a.mu.RLock()
	if time.Until(a.expiresAt) > 5*time.Minute {
		token := a.token
		a.mu.RUnlock()
		return token, nil
	}
	a.mu.RUnlock()
	return a.refreshToken(ctx)
}

func (a *AuthClient) refreshToken(ctx context.Context) (string, error) {
	a.mu.Lock()
	defer a.mu.Unlock()
	payload := fmt.Sprintf("grant_type=client_credentials&client_id=%s&client_secret=%s", a.ClientID, a.ClientSecret)
	req, _ := http.NewRequestWithContext(ctx, http.MethodPost, fmt.Sprintf("%s/auth/token", a.BaseURL), nil)
	req.Header.Set("Content-Type", "application/x-www-form-urlencoded")
	req.SetBasicAuth(a.ClientID, a.ClientSecret)
	resp, err := (&http.Client{Timeout: 10 * time.Second}).Do(req)
	if err != nil {
		return "", err
	}
	defer resp.Body.Close()
	if resp.StatusCode != http.StatusOK {
		return "", fmt.Errorf("auth token request failed with status %d", resp.StatusCode)
	}
	var tr TokenResponse
	if err := json.NewDecoder(resp.Body).Decode(&tr); err != nil {
		return "", err
	}
	a.token = tr.AccessToken
	a.expiresAt = time.Now().Add(time.Duration(tr.ExpiresIn) * time.Second)
	return a.token, nil
}

func NewCacheClient(baseURL string, auth *AuthClient) *CacheClient {
	return &CacheClient{
		BaseURL: baseURL,
		Auth:    auth,
		HTTP:    &http.Client{Timeout: 30 * time.Second},
	}
}

func isDeprecatedVersion(v string) bool {
	deprecated := map[string]bool{"1.0.0": true, "1.1.0": true, "2.0.0-rc1": true}
	return deprecated[v]
}

func ValidateCachePayload(payload CachePayload, runtime RuntimeStatus) error {
	var totalTensorMB float64
	for _, layer := range payload.LayerMatrix {
		elemSize := 4.0
		if layer.DataType == "float16" {
			elemSize = 2.0
		}
		volume := 1
		for _, d := range layer.Dimensions {
			volume *= d
		}
		totalTensorMB += (float64(volume) * elemSize) / (1024.0 * 1024.0)
	}
	if int(totalTensorMB) > runtime.AvailableMemoryMB {
		return &ValidationError{Code: "MEMORY_EXCEEDED", Message: fmt.Sprintf("Tensor allocation %.2f MB exceeds available %d MB", totalTensorMB, runtime.AvailableMemoryMB)}
	}
	for _, layer := range payload.LayerMatrix {
		for i, d := range layer.Dimensions {
			if i < len(runtime.MaxDimensions) && d > runtime.MaxDimensions[i] {
				return &ValidationError{Code: "DIMENSION_LIMIT_EXCEEDED", Message: fmt.Sprintf("Layer %s dimension %d exceeds runtime limit %d", layer.Name, d, runtime.MaxDimensions[i])}
			}
		}
	}
	if runtime.GPUUtilization > 85 {
		return &ValidationError{Code: "GPU_SATURATED", Message: "GPU utilization exceeds 85 percent. Deferring preload to prevent memory fragmentation."}
	}
	return nil
}

type ValidationError struct {
	Code    string
	Message string
}

func (e *ValidationError) Error() string { return e.Message }

func (c *CacheClient) PreloadModel(ctx context.Context, payload CachePayload, runtime RuntimeStatus, modelMeta ModelMetadata) error {
	if modelMeta.Version != "" && isDeprecatedVersion(modelMeta.Version) {
		return &ValidationError{Code: "DEPRECATED_MODEL", Message: "Model version is deprecated. Cache preload rejected."}
	}
	if runtime.Architecture != modelMeta.Architecture {
		return &ValidationError{Code: "ARCH_MISMATCH", Message: fmt.Sprintf("Runtime architecture %s does not match model architecture %s", runtime.Architecture, modelMeta.Architecture)}
	}
	startTime := time.Now()
	token, err := c.Auth.GetToken(ctx)
	if err != nil {
		return err
	}
	jsonBody, err := json.Marshal(payload)
	if err != nil {
		return err
	}
	req, _ := http.NewRequestWithContext(ctx, http.MethodPut, fmt.Sprintf("%s/nlu/cache/preload", c.BaseURL), nil)
	req.Header.Set("Authorization", "Bearer "+token)
	req.Header.Set("Content-Type", "application/json")
	req.Body = io.NopCloser(bytes.NewReader(jsonBody))
	resp, err := c.HTTP.Do(req)
	if err != nil {
		return err
	}
	defer resp.Body.Close()
	if resp.StatusCode == http.StatusTooManyRequests {
		time.Sleep(2 * time.Second)
		return c.PreloadModel(ctx, payload, runtime, modelMeta)
	}
	if resp.StatusCode != http.StatusOK && resp.StatusCode != http.StatusCreated {
		c.FailureCount.Add(1)
		return fmt.Errorf("cache preload failed with status %d", resp.StatusCode)
	}
	if resp.Header.Get("X-Cache-Occupancy") == "critical" {
		c.triggerEviction(ctx, token)
	}
	latency := time.Since(startTime).Milliseconds()
	c.TotalLatency.Add(latency)
	c.SuccessCount.Add(1)
	return nil
}

func (c *CacheClient) triggerEviction(ctx context.Context, token string) error {
	req, _ := http.NewRequestWithContext(ctx, http.MethodDelete, fmt.Sprintf("%s/nlu/cache/evict/lowest-priority", c.BaseURL), nil)
	req.Header.Set("Authorization", "Bearer "+token)
	resp, err := c.HTTP.Do(req)
	if err != nil {
		return err
	}
	defer resp.Body.Close()
	if resp.StatusCode != http.StatusOK {
		return fmt.Errorf("eviction failed with status %d", resp.StatusCode)
	}
	return nil
}

func (c *CacheClient) SyncAndAudit(ctx context.Context, payload CachePayload, runtime RuntimeStatus, status string) error {
	total := c.SuccessCount.Load() + c.FailureCount.Load()
	var successRate float64
	if total > 0 {
		successRate = float64(c.SuccessCount.Load()) / float64(total)
	}
	logEntry := AuditLog{
		Timestamp:    time.Now(),
		ModelRef:     payload.ModelRef,
		Action:       "cache_preload",
		Status:       status,
		LatencyMs:    c.TotalLatency.Load() / int64(c.SuccessCount.Load() + 1),
		GPUUtil:      runtime.GPUUtilization,
		SuccessRate:  successRate,
		ArchVerified: true,
	}
	slog.Info("nlu_cache_audit", "log", logEntry)
	webhookPayload, _ := json.Marshal(logEntry)
	webhookReq, _ := http.NewRequestWithContext(ctx, http.MethodPost, os.Getenv("WEBHOOK_URL"), nil)
	webhookReq.Header.Set("Content-Type", "application/json")
	webhookReq.Header.Set("X-Cache-Sync-Event", "model_preloaded")
	webhookResp, err := c.HTTP.Do(webhookReq)
	if err != nil {
		slog.Warn("webhook_sync_failed", "error", err)
		return nil
	}
	defer webhookResp.Body.Close()
	if webhookResp.StatusCode >= 400 {
		slog.Warn("webhook_sync_rejected", "status", webhookResp.StatusCode)
	}
	return nil
}

func main() {
	ctx := context.Background()
	tenant := os.Getenv("COGNIGY_TENANT")
	clientID := os.Getenv("COGNIGY_CLIENT_ID")
	clientSecret := os.Getenv("COGNIGY_CLIENT_SECRET")

	auth := NewAuthClient(tenant, clientID, clientSecret)
	cacheClient := NewCacheClient(fmt.Sprintf("https://%s.cognigy.ai/api/v1", tenant), auth)

	payload := CachePayload{
		ModelRef: "mdl_9a8b7c6d5e4f3g2h1i0j",
		LayerMatrix: []LayerInfo{
			{Name: "embedding", Dimensions: []int{768, 512}, DataType: "float32"},
			{Name: "crf_layer", Dimensions: []int{512, 128}, DataType: "float16"},
		},
		Preload: PreloadDirective{Priority: "high", ForceReload: false, ParallelShards: 4},
	}

	runtime := RuntimeStatus{
		AvailableMemoryMB: 8192,
		GPUUtilization:    45,
		MaxDimensions:     []int{4096, 2048},
		Architecture:      "cuda-12.1",
	}

	modelMeta := ModelMetadata{Version: "2.1.0", Architecture: "cuda-12.1"}

	if err := ValidateCachePayload(payload, runtime); err != nil {
		slog.Error("validation_failed", "error", err)
		os.Exit(1)
	}

	if err := cacheClient.PreloadModel(ctx, payload, runtime, modelMeta); err != nil {
		slog.Error("preload_failed", "error", err)
		os.Exit(1)
	}

	cacheClient.SyncAndAudit(ctx, payload, runtime, "success")
	slog.Info("cache_preload_complete")
}

Common Errors & Debugging

Error: 401 Unauthorized

• What causes it: The OAuth token expired or the client credentials are invalid.
• How to fix it: Verify COGNIGY_CLIENT_ID and COGNIGY_CLIENT_SECRET match the Cognigy.AI console configuration. Ensure the token refresh logic runs before expiration.
• Code showing the fix: The AuthClient.refreshToken method automatically re-authenticates when time.Until(a.expiresAt) <= 5*time.Minute.

Error: 403 Forbidden

• What causes it: The OAuth token lacks required scopes or the tenant does not have NLU cache write permissions.
• How to fix it: Add nlu:write, model:cache:write, and nlu:read scopes to the OAuth client configuration in the Cognigy.AI admin console.
• Code showing the fix: Scopes are granted server-side. Verify the token payload contains scope: "nlu:write model:cache:write nlu:read".

Error: 429 Too Many Requests

• What causes it: The Cognigy.AI API rate limiter blocks rapid preload requests.
• How to fix it: Implement exponential backoff. The PreloadModel method sleeps for 2 seconds and retries atomically.
• Code showing the fix: if resp.StatusCode == http.StatusTooManyRequests { time.Sleep(2 * time.Second); return c.PreloadModel(...) }

Error: 400 Bad Request (MEMORY_EXCEEDED or DIMENSION_LIMIT_EXCEEDED)

• What causes it: The tensor allocation exceeds available memory or layer dimensions exceed runtime limits.
• How to fix it: Reduce parallel_shards, switch to float16 data types, or request a larger runtime instance.
• Code showing the fix: The ValidateCachePayload function calculates totalTensorMB and compares against runtime.AvailableMemoryMB before sending the PUT request.

Error: 503 Service Unavailable

• What causes it: GPU saturation or runtime maintenance windows block cache operations.
• How to fix it: Wait for gpu_utilization_percent to drop below 85 percent. The validation pipeline defers preloads automatically.
• Code showing the fix: if runtime.GPUUtilization > 85 { return &ValidationError{Code: "GPU_SATURATED", ...} }

Official References

• Cognigy.AI API v1 Documentation
• Cognigy.AI OAuth 2.0 Authentication
• NLU Cache Preload Endpoint
• Runtime Status and GPU Metrics
• Go net/http Package
• Go sync/atomic Package