Agent Copilot is a generative AI-based solution that provides a chat-driven interface to assist agents in resolving customer cases more efficiently. Integrated into the Sprinklr platform, it uses real-time and historical data, natural language processing, and structured workflows to improve case handling and agent productivity. 

Agent Copilot supports the following core capabilities: 

• Summarize Current Case 
  Analyzes ongoing case conversations to identify key contact drivers and recommend next steps, enabling faster understanding and resolution. 

• Answer Customer Queries 
  Interprets customer questions in real time and generates responses by retrieving information from connected knowledge sources, such as the brand’s internal knowledge base or documentation. 

• Summarize Historical Customer Interactions 
  Reviews previous interactions and customer profile data to generate a concise summary that helps agents understand the customer’s context before responding. 

• Supervisor Escalation 
  Monitors sentiment and intent within case conversations to detect potential dissatisfaction and automatically trigger escalation workflows to supervisors. 

• Generate Email Responses 
  Agent Copilot can draft email responses by utilizing knowledge retrieved from training content sources. 

• Update Case and Profile Level Custom Field 
  Update Case and Profile level custom fields using Agent Copilot. 

By combining these capabilities, Agent Copilot functions as an intelligent assistant within the agent workspace, reducing manual effort and improving response quality through context-aware recommendations and actions. 

Agent Copilot Architecture Overview 

The Agent Copilot is built on a modular and scalable architecture. It is designed to manage user interactions and deliver intelligent responses using a combination of AI services and Sprinklr capabilities. This section provides a detailed overview of the core components and how they work together to power the Agent Copilot. 

1. Support Agent Interaction 

When a support agent interacts with the Agent Copilot, the input is routed to the Orchestrator, which serves as the central processing unit. It receives queries or bot-triggered messages and initiates the task-handling workflow. 

2. Orchestrator 

The Orchestrator is the brain that coordinates the Agent Copilot’s behavior. It is responsible for interpreting the input, breaking it down into actionable tasks, and managing the entire workflow from start to finish. It acts as the control hub that coordinates all other components, including the Data Layer, Skills Engine, Unified Data Layer, and AI providers. 

It performs the following actions in real-time: 

• Receives and parses incoming agent or system-generated messages. 

• Matches tasks with appropriate skills, based on request context. 

• Coordinates with the Data Layer to extract context and apply masking. 

• Executes functions using skills and references data from the Unified Data Layer. 

• Adapts by integrating with various AI providers and custom models for maximum flexibility. 

3. Skills Engine 

Skills are modular micro-capabilities used to execute tasks. Each task can invoke one or more skills—such as updating a case, generating a summary, or triggering an API call—based on the user’s intent and task logic. 

4. Data Layer 

The Data Layer manages all incoming request data. It extracts relevant context, applies PII (Personally Identifiable Information) masking where needed, and prepares the input for task execution. This promotes secure and context-aware processing of every request. 

5. Unified Data Layer 

• The Unified Data Layer provides the Orchestrator with structured access to Sprinklr’s capabilities and user data such as Knowledge Base articles and Universal Case data. Integrates Universal Case functionality for customer support use cases. Supplies user-specific information for personalized interactions. 

6. Integration with AI Providers 

Agent Copilot supports seamless integration with available third-party AI providers (see Sprinklr’s Subprocessor List) which enables enhanced natural language understanding, reasoning, and content generation capabilities. To connect with these providers, you can use Sprinklr-provided keys or your own keys using BYOK (Bring Your Own Key) enablement type. You can also integrate custom AI models using BYOM (Bring Your Own Model) enablement type that supports function calling. This allows you to build domain-specific intelligence into your Agent Copilot for highly tailored use cases. 

System Workflow: From Interaction to Response 

The architecture follows a streamlined flow for handling every user query: 

1. Agent Interaction: The request is received by the Orchestrator. 

2. Task Management: Each query is routed to a task based on query and task matching.   

3. Skills Execution: Copilot skills can be executed within a task to generate the best suited response. 

4. Data Processing: Contextual information is extracted, and personal data is masked in accordance with the PII masking template configured. 

5. Response Generation: AI providers or custom models are invoked to formulate intelligent responses. 

6. Unified Data Access: Data is retrieved from Sprinklr data layer for enriched and accurate output. 

The Agent Copilot architecture is a flexible, modular system designed to deliver intelligent, secure, and efficient task execution. By organizing skills, using contextual data, and integrating with industry-leading AI models, the platform provides high-quality user experiences tailored to your organization’s needs. 

Benefits of Agent Copilot 

Agent Copilot delivers measurable benefits across organizational operations, customer experience, and agent performance by automating routine tasks and providing intelligent, context-aware assistance. 

1. Improved Operational Efficiency 

• Reduced Average Handling Time (AHT) 
  By summarizing cases, retrieving relevant knowledge, and automating task execution, Agent Copilot significantly reduces the time agents spend per case. 

• Lower Training and Onboarding Time 
  New agents gain contextual insights instantly through automatic summaries and guided workflows, minimizing ramp-up time. 

• Scalable Support 
  Organizations can support higher case volumes without increasing headcount by automating repetitive interactions and minimizing manual effort. 

2. Enhanced Customer Experience 

• Faster Response Time 
  With real-time suggestions and knowledge retrieval, agents respond more quickly and accurately to customer queries. 

• Consistency and Accuracy of Responses 
  AI-generated answers are based on approved organization content to support enhanced accuracy and consistency across interactions. 

• Proactive Escalation 
  Agent Copilot monitors sentiment and intent signals to detect dissatisfaction and escalate cases before they degrade the customer experience. 

3. Increased Agent Productivity 

• Context-Aware Assistance 
  Agents receive relevant insights from historical and current case data without having to search manually, enabling them to focus on decision-making. 

• Task Automation 
  Copilot automates multi-step tasks using preconfigured skills, reducing cognitive load and minimizing errors. 

• Knowledge Access 
  Agents gain instant access to structured and unstructured knowledge sources, eliminating the need to navigate multiple systems. 

4. Organizational Insights and Governance 

• Centralized Configuration 
  Capabilities, tasks, and skills can be centrally managed and updated through AI+ Studio, supporting enterprise-wide governance and control. 

• Auditability  

Interaction logs allow visibility over usage. 

• Compliance 
  PII masking supports data minimization from a compliance perspective. 

• Flexible AI Integration 
  Support for multiple AI providers and custom models allows organizations to choose and control their integration. 

By integrating Agent Copilot into the service workflow, organizations can streamline operations, improve response quality, and deliver consistent, scalable support at lower operational cost.