Spotting Genesys Cloud Agent Assist API competitor keywords via Agent Assist API with C#

What You Will Build

A C# service that configures and validates Genesys Cloud Agent Assist keyword spotting rules, enforces NLP constraints, tracks detection latency, and synchronizes matches to external sales tools via webhooks. This tutorial uses the Genesys Cloud Agent Assist API and the official C# SDK. The code is written in C# 10+ with modern async patterns and production-grade error handling.

Prerequisites

• OAuth2 Client Credentials grant with scopes: agentassist:spotting:read, agentassist:spotting:write, webhooks:write
• Genesys Cloud C# SDK: GenesysCloud NuGet package v2.0+
• Runtime: .NET 6 or later
• External dependencies: System.Text.Json, System.Diagnostics, Microsoft.Extensions.Logging.Abstractions
• A Genesys Cloud organization with Agent Assist enabled and a registered OAuth2 confidential client

Authentication Setup

Genesys Cloud requires OAuth2 Bearer tokens for all API calls. The Client Credentials flow is the standard for server-to-server integrations. You must cache the token and refresh it before expiration to avoid 401 Unauthorized errors during long-running spotting validations.

using System;
using System.Collections.Generic;
using System.Net.Http;
using System.Text.Json;
using System.Threading.Tasks;

public class GenesysAuthClient
{
    private readonly HttpClient _httpClient;
    private readonly string _clientId;
    private readonly string _clientSecret;
    private readonly string _baseUrl;

    public GenesysAuthClient(string clientId, string clientSecret, string baseUrl = "https://api.mypurecloud.com")
    {
        _httpClient = new HttpClient();
        _clientId = clientId;
        _clientSecret = clientSecret;
        _baseUrl = baseUrl;
    }

    public async Task<string> GetAccessTokenAsync()
    {
        var content = new FormUrlEncodedContent(new[]
        {
            new KeyValuePair<string, string>("grant_type", "client_credentials"),
            new KeyValuePair<string, string>("client_id", _clientId),
            new KeyValuePair<string, string>("client_secret", _clientSecret)
        });

        var response = await _httpClient.PostAsync($"{_baseUrl}/oauth/token", content);
        response.EnsureSuccessStatusCode();

        var json = await response.Content.ReadAsStringAsync();
        var tokenData = JsonSerializer.Deserialize<Dictionary<string, object>>(json);
        
        if (tokenData == null || !tokenData.ContainsKey("access_token"))
            throw new InvalidOperationException("OAuth response missing access_token");

        return tokenData["access_token"].ToString();
    }
}

The token response returns access_token, expires_in, and token_type. Store the token in memory with a refresh threshold set to expires_in - 300 seconds to prevent mid-operation authentication failures.

Implementation

Step 1: Construct Spotting Payloads with Keyword References and Match Matrix

The Agent Assist API uses SpottingConfiguration to define detection rules. You must structure the payload with keyword references, a match matrix, and a flag directive. The match matrix determines how keywords combine (AND/OR), and the flag directive controls whether the system raises an alert or logs silently.

OAuth Scope Required: agentassist:spotting:write

using GenesysCloud;
using GenesysCloud.Api;
using GenesysCloud.Models.Agentassist;
using System.Collections.Generic;
using System.Threading.Tasks;

public class SpottingPayloadBuilder
{
    private readonly GenesysCloudPlatformClient _client;
    private readonly AgentassistApi _agentassistApi;

    public SpottingPayloadBuilder(GenesysCloudPlatformClient client)
    {
        _client = client;
        _agentassistApi = new AgentassistApi(_client);
    }

    public async Task<SpottingConfiguration> BuildCompetitorSpottingConfigAsync(string configName, List<string> keywords)
    {
        var ruleConditions = new List<SpottingRuleCondition>();
        foreach (var kw in keywords)
        {
            ruleConditions.Add(new SpottingRuleCondition
            {
                Type = "keyword",
                Keyword = kw,
                CaseSensitive = false
            });
        }

        var ruleOutput = new SpottingRuleOutput
        {
            Type = "flag",
            Flag = new Flag { Name = "CompetitorMention", Severity = "high" }
        };

        var spottingRule = new SpottingRule
        {
            Name = $"{configName}_Rule",
            Enabled = true,
            Conditions = ruleConditions,
            Output = ruleOutput,
            MatchMatrix = "any", // Triggers on any single keyword match
            ContextWindowSeconds = 30
        };

        var config = new SpottingConfiguration
        {
            Name = configName,
            Description = "Competitor keyword detection pipeline",
            Enabled = true,
            Rules = new List<SpottingRule> { spottingRule },
            FlagDirective = "raise_and_log"
        };

        var response = await _agentassistApi.PostAgentassistSpottingConfigurationsAsync(config);
        return response;
    }
}

The MatchMatrix parameter accepts any, all, or custom. Setting it to any ensures the system flags the conversation when at least one competitor keyword appears. The FlagDirective set to raise_and_log guarantees both real-time UI alerts and backend audit storage.

Step 2: Validate Against NLP Engine Constraints and Dictionary Size Limits

Genesys Cloud NLP engines enforce strict dictionary size limits and regex syntax rules. Submitting a configuration that exceeds the maximum dictionary size or contains invalid regex patterns results in a 400 Bad Request. You must validate payloads locally before transmission.

using System;
using System.Collections.Generic;
using System.Text.RegularExpressions;
using System.Threading.Tasks;

public class SpottingValidator
{
    private const int MaxDictionarySize = 10000;
    private static readonly Regex RegexPatternValidator = new Regex(@"^(?!\s*$).+", RegexOptions.Compiled);

    public ValidationResult ValidateConfiguration(List<string> keywords, bool validateRegex = true)
    {
        if (keywords.Count > MaxDictionarySize)
            return new ValidationResult { IsValid = false, Error = $"Dictionary size {keywords.Count} exceeds maximum limit of {MaxDictionarySize}" };

        var invalidRegexes = new List<string>();
        if (validateRegex)
        {
            foreach (var kw in keywords)
            {
                if (!RegexPatternValidator.IsMatch(kw))
                    invalidRegexes.Add(kw);
            }
        }

        if (invalidRegexes.Count > 0)
            return new ValidationResult { IsValid = false, Error = $"Invalid regex patterns detected: {string.Join(", ", invalidRegexes)}" };

        return new ValidationResult { IsValid = true, Error = null };
    }
}

public class ValidationResult
{
    public bool IsValid { get; set; }
    public string Error { get; set; }
}

The NLP engine rejects configurations with empty strings, unescaped special characters, or dictionaries that exceed memory allocation thresholds. Pre-validation prevents API throttling and preserves rate limit headroom for other microservices.

Step 3: Handle Real-Time Regex Evaluation and Context Window Triggers

Real-time spotting requires atomic GET operations to verify current configuration state before applying updates. You must fetch the existing configuration, verify regex format compatibility, and apply context window triggers safely to avoid race conditions during conversation processing.

OAuth Scope Required: agentassist:spotting:read

using System;
using System.Linq;
using System.Threading.Tasks;

public class SpottingContextManager
{
    private readonly AgentassistApi _agentassistApi;

    public SpottingContextManager(AgentassistApi agentassistApi)
    {
        _agentassistApi = agentassistApi;
    }

    public async Task<SpottingConfiguration> FetchAndVerifyContextAsync(string configId)
    {
        var response = await _agentassistApi.GetAgentassistSpottingConfigurationAsync(configId);
        
        if (response == null)
            throw new InvalidOperationException($"Spotting configuration {configId} not found");

        foreach (var rule in response.Rules)
        {
            if (rule.ContextWindowSeconds < 5 || rule.ContextWindowSeconds > 120)
                throw new InvalidOperationException($"Invalid context window: {rule.ContextWindowSeconds}s. Must be between 5 and 120 seconds");

            foreach (var condition in rule.Conditions)
            {
                if (condition.Type == "regex" && !System.Text.RegularExpressions.Regex.IsMatch(condition.Keyword, @"^\(.*\)$"))
                    throw new InvalidOperationException($"Regex condition {condition.Keyword} does not match expected atomic format");
            }
        }

        return response;
    }
}

Context windows define the audio segment length the NLP engine analyzes. Setting windows below 5 seconds causes false negatives due to insufficient phoneme capture. Setting windows above 120 seconds increases latency and memory consumption. The atomic GET ensures you are not modifying a configuration that is currently undergoing schema migration.

Step 4: Implement Synonym Expansion and Case Sensitivity Verification

Keyword spotting accuracy degrades when synonyms are missing or case sensitivity flags are misconfigured. You must run a validation pipeline that expands synonym sets, normalizes casing, and verifies that the CaseSensitive property aligns with your detection strategy.

using System;
using System.Collections.Generic;
using System.Linq;
using System.Threading.Tasks;

public class KeywordValidationPipeline
{
    private static readonly Dictionary<string, List<string>> SynonymMap = new Dictionary<string, List<string>>
    {
        { "competitorA", new List<string> { "rivalA", "vendorA", "alternativeA" } },
        { "competitorB", new List<string> { "rivalB", "vendorB" } }
    };

    public List<string> ExpandAndValidateKeywords(List<string> baseKeywords, bool enforceCaseSensitivity)
    {
        var expandedKeywords = new HashSet<string>(StringComparer.OrdinalIgnoreCase);

        foreach (var kw in baseKeywords)
        {
            expandedKeywords.Add(kw);
            if (SynonymMap.TryGetValue(kw, out var synonyms))
            {
                foreach (var syn in synonyms)
                    expandedKeywords.Add(syn);
            }
        }

        var validatedList = expandedKeywords.Select(kw =>
        {
            if (enforceCaseSensitivity)
                return kw;
            return kw.ToLowerInvariant();
        }).ToList();

        return validatedList;
    }
}

The pipeline normalizes casing to prevent duplicate entries that consume dictionary slots. Enforcing case sensitivity is only recommended when exact brand name matching is required. For competitor detection, case-insensitive matching yields higher recall rates without significant precision loss.

Step 5: Synchronize Events via Webhooks and Track Latency and Success Rates

Spotting events must synchronize with external sales tools. You configure a webhook endpoint to receive agentassist.spotting.flagged events. You also track request latency and flag success rates to identify NLP engine bottlenecks and configuration drift.

OAuth Scope Required: webhooks:write

using GenesysCloud.Api;
using GenesysCloud.Models;
using System;
using System.Diagnostics;
using System.Threading.Tasks;

public class SpottingEventSyncManager
{
    private readonly WebhooksApi _webhooksApi;
    private readonly string _webhookUrl;
    private readonly string _webhookName;

    public SpottingEventSyncManager(WebhooksApi webhooksApi, string webhookUrl, string webhookName)
    {
        _webhooksApi = webhooksApi;
        _webhookUrl = webhookUrl;
        _webhookName = webhookName;
    }

    public async Task<Webhook> RegisterSpottingWebhookAsync()
    {
        var stopwatch = Stopwatch.StartNew();

        var webhook = new Webhook
        {
            Name = _webhookName,
            Enabled = true,
            Type = "rest",
            Url = _webhookUrl,
            Events = new List<string> { "agentassist.spotting.flagged", "agentassist.spoting.resolved" },
            Headers = new Dictionary<string, string> { { "Content-Type", "application/json" } }
        };

        try
        {
            var response = await _webhooksApi.PostWebhooksAsync(webhook);
            stopwatch.Stop();
            
            LogAudit($"Webhook registered successfully. Latency: {stopwatch.ElapsedMilliseconds}ms");
            return response;
        }
        catch (ApiException ex) when (ex.StatusCode == 429)
        {
            await Task.Delay(1000);
            return await _webhooksApi.PostWebhooksAsync(webhook);
        }
        catch (ApiException ex)
        {
            stopwatch.Stop();
            LogAudit($"Webhook registration failed. Status: {ex.StatusCode}. Latency: {stopwatch.ElapsedMilliseconds}ms");
            throw;
        }
    }

    private void LogAudit(string message)
    {
        Console.WriteLine($"[AUDIT] {DateTime.UtcNow:yyyy-MM-ddTHH:mm:ssZ} - {message}");
    }
}

The webhook payload contains the conversationId, flagName, matchedKeyword, and timestamp. External sales tools parse these fields to update CRM records automatically. The latency tracker records the time between PostWebhooksAsync invocation and response receipt. Persistent latency above 2000 milliseconds indicates network congestion or webhook endpoint overload.

Complete Working Example

The following module combines authentication, payload construction, validation, context management, synonym expansion, webhook synchronization, and audit logging into a single executable service.

using System;
using System.Collections.Generic;
using System.Threading.Tasks;
using GenesysCloud;
using GenesysCloud.Api;

public class CompetitorKeywordSpotter
{
    private readonly GenesysCloudPlatformClient _client;
    private readonly AgentassistApi _agentassistApi;
    private readonly WebhooksApi _webhooksApi;
    private readonly SpottingValidator _validator;
    private readonly KeywordValidationPipeline _pipeline;
    private readonly SpottingContextManager _contextManager;
    private readonly SpottingEventSyncManager _syncManager;

    public CompetitorKeywordSpotter(string clientId, string clientSecret, string webhookUrl)
    {
        var auth = new GenesysAuthClient(clientId, clientSecret);
        var token = auth.GetAccessTokenAsync().Result;

        _client = new GenesysCloudPlatformClient();
        _client.SetAccessToken(token);

        _agentassistApi = new AgentassistApi(_client);
        _webhooksApi = new WebhooksApi(_client);
        _validator = new SpottingValidator();
        _pipeline = new KeywordValidationPipeline();
        _contextManager = new SpottingContextManager(_agentassistApi);
        _syncManager = new SpottingEventSyncManager(_webhooksApi, webhookUrl, "CompetitorSpottingSync");
    }

    public async Task<Dictionary<string, object>> DeployCompetitorSpottingAsync(List<string> baseKeywords)
    {
        var result = new Dictionary<string, object>();

        // Step 1: Expand and validate keywords
        var expandedKeywords = _pipeline.ExpandAndValidateKeywords(baseKeywords, enforceCaseSensitivity: false);
        var validation = _validator.ValidateConfiguration(expandedKeywords);

        if (!validation.IsValid)
            throw new InvalidOperationException($"Validation failed: {validation.Error}");

        // Step 2: Build and submit spotting configuration
        var builder = new SpottingPayloadBuilder(_client);
        var config = await builder.BuildCompetitorSpottingConfigAsync("CompetitorDetection_v1", expandedKeywords);
        result["ConfigurationId"] = config.Id;

        // Step 3: Verify context window and regex format
        var verifiedConfig = await _contextManager.FetchAndVerifyContextAsync(config.Id);
        result["ContextWindowVerified"] = true;

        // Step 4: Register webhook for external sync
        var webhook = await _syncManager.RegisterSpottingWebhookAsync();
        result["WebhookId"] = webhook.Id;

        // Step 5: Audit log generation
        Console.WriteLine($"[AUDIT] Spotting pipeline deployed. Config: {config.Id} | Keywords: {expandedKeywords.Count} | Webhook: {webhook.Id}");
        
        return result;
    }
}

// Execution entry point
public static class Program
{
    public static async Task Main(string[] args)
    {
        var spotter = new CompetitorKeywordSpotter("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET", "https://your-sales-tool.com/api/webhooks/genesys");
        var keywords = new List<string> { "competitorA", "competitorB", "rivalX" };
        
        try
        {
            var deployment = await spotter.DeployCompetitorSpottingAsync(keywords);
            Console.WriteLine($"Deployment successful. Configuration ID: {deployment["ConfigurationId"]}");
        }
        catch (Exception ex)
        {
            Console.WriteLine($"Deployment failed: {ex.Message}");
        }
    }
}

Common Errors & Debugging

Error: 400 Bad Request

• Cause: Dictionary size exceeds 10,000 terms, regex pattern contains unescaped characters, or MatchMatrix value is invalid.
• Fix: Run _validator.ValidateConfiguration() before submission. Truncate synonym expansions or split configurations into multiple smaller dictionaries. Verify MatchMatrix accepts only any, all, or custom.
• Code Fix: Add pre-submission size checks and regex compilation tests as shown in Step 2.

Error: 401 Unauthorized / 403 Forbidden

• Cause: Expired access token or missing OAuth scopes. The Agent Assist API requires agentassist:spotting:read and agentassist:spotting:write. Webhook registration requires webhooks:write.
• Fix: Implement token refresh logic before expiration. Verify the OAuth2 client in Genesys Cloud has the exact scopes granted. Re-authenticate if the token age exceeds expires_in - 300 seconds.

Error: 429 Too Many Requests

• Cause: Rate limit exceeded due to rapid configuration updates or webhook polling. Genesys Cloud enforces per-endpoint rate limits.
• Fix: Implement exponential backoff. The SpottingEventSyncManager demonstrates a single retry delay. Production systems should use a jitter-based backoff strategy with maximum retry caps.

Error: 503 Service Unavailable

• Cause: NLP engine undergoing maintenance or temporary overload during peak conversation volume.
• Fix: Retry with increasing delays up to 30 seconds. Monitor Genesys Cloud status pages for scheduled maintenance. Queue configuration updates locally and flush when the API returns 200 OK.

Official References

• Agent Assist Spotting Configurations API
• Agent Assist Spotting Rules API
• Webhooks API Reference
• Genesys Cloud C# SDK Documentation
• OAuth2 Client Credentials Flow