Executive Demo: Multi-Agent Call Center

📞 1. Automatic Triggering

User Story: As an agent, I want the system to activate automatically when I pick up the phone, so I don't waste time.

flowchart LR A[📞 Incoming Call] --> B{Agent Picks Up?} B -->|Yes| C[🚀 Multi-Agent Trigger] B -->|No| D[📋 Queue] C --> E[✅ System Activated] style A fill:#e1f5fe style C fill:#e8f5e8 style E fill:#f1f8e9

<0.1s

Activation Time

100%

Success Rate

How it works:

When a customer calls, the system automatically detects when an agent picks up the phone. This instantly triggers the entire multi-agent ecosystem to start working in the background. No manual activation needed - the AI begins analyzing and preparing solutions immediately.

Key benefit: Zero delay between call pickup and AI assistance, giving agents immediate support.

🎤 2. Real-time Transcription

User Story: As an agent, I want to see the conversation transcribed in real-time, so I can focus on active listening.

flowchart TD A[🎵 Audio Stream] --> B[🎤 Transcription Agent] B --> C[📝 Real-time Text] C --> D[📺 Interface Display] C --> E[⏭️ To Requirement Detection] style A fill:#fff3e0 style B fill:#f3e5f5 style C fill:#e8f5e8 style D fill:#e1f5fe

95%

Accuracy

0.2s

Latency

How it works:

Advanced speech-to-text technology converts the customer's voice into text in real-time with only 0.2 seconds delay. The transcription appears instantly on the agent's screen, while simultaneously being sent to the requirement detection system for analysis.

Key benefit: Agents can focus on listening and building rapport while the AI processes the conversation content.

🧠 3. Intelligent Requirement Detection

User Story: As an agent, I want the system to automatically identify customer requirements, so I can anticipate solutions.

flowchart LR A[📝 Text] --> B[🧠 AI Analysis Agent] B --> C{Requirement Detected?} C -->|Yes| D[🎯 Classification] C -->|No| E[⏳ Continue Listening] D --> F[📋 Add to List] F --> G[🔍 To Research] style B fill:#fff3e0 style D fill:#e8f5e8 style F fill:#f1f8e9

How it works:

AI continuously analyzes the transcribed text to identify customer needs, problems, and requirements. When it detects a specific need, it automatically classifies it (billing issue, technical problem, account inquiry, etc.) and adds it to a dynamic list that appears on the agent's screen.

Key benefit: Requirements are identified and categorized automatically, eliminating the need for agents to manually note down customer needs.

🔍 4. Multi-Agent Research Orchestration

User Story: As a system, I want to intelligently orchestrate research across all databases to optimize performance.

flowchart TD A[🎯 Identified Requirement] --> B[🔍 Research Coordinator] B --> C[📊 Metadata Consultation] C --> D{Research Type?} D -->|SQL| E[🗃️ SQL Agent] D -->|Vector| F[🔎 Vector Agent] D -->|API| G[🌐 API Agent] style B fill:#e8f5e8 style H fill:#f1f8e9

How it works:

The Research Coordinator acts as the brain of the system. It first consults metadata from all available databases to understand what information is available. Then, based on the type of requirement detected, it intelligently selects and launches the appropriate specialized agents (SQL for structured data, Vector for semantic search, API for external services).

Key benefit: Smart orchestration ensures the right agents are used for the right type of data, optimizing both speed and accuracy.

⚡ 5. Parallel Agent Execution

User Story: As a system, I want to execute searches in parallel when possible to reduce latency.

Scenario 1: Multiple SQL Agents

flowchart TD A[🔍 Execution Plan] --> B{Parallel Possible?} B -- Yes --> C[⚡ Parallel Execution] C --> D[🗃️ SQL Agent] D --> E[📊 Billing DB] D --> F[📊 Customer DB] D --> G[📊 Transaction DB] E --> H[💾 Aggregation] F --> H G --> H B -- No --> I[🔄 Sequential Execution] I --> H style C fill:#e8f5e8 style H fill:#f1f8e9

Scenario 3: Mixed Configuration

flowchart TD A[🔍 Execution Plan] --> B{Parallel Possible?} B -- Yes --> C[⚡ Parallel Execution] C --> D[🗃️ SQL Agent] C --> E[🔎 Vector Agent] C --> F[🌐 API Agent] D --> G[📊 SQL Database] E --> H[🔍 Vector Database 1] E --> I[🔍 Vector Database 2] F --> J[🌐 External API] G --> K[💾 Aggregation] H --> K I --> K J --> K B -- No --> L[🔄 Sequential Execution] L --> K style C fill:#e8f5e8 style K fill:#f1f8e9

How it works:

The system intelligently determines whether searches can be executed in parallel or must be sequential. When parallel execution is possible, multiple agents work simultaneously across different databases and services. A single agent can access multiple databases of the same type, or different specialized agents can work together.

Key benefit: Parallel execution reduces total search time by 2-3x compared to sequential processing, dramatically improving response times.

🎯 6. Synthesis and Action Generation

User Story: As an agent, I want ready-to-use solutions with action buttons to resolve issues quickly.

flowchart TD A[💾 Aggregated Data] --> B[🎯 Synthesis Agent] B --> C[📊 Confidence Scoring] C --> D{Score > Threshold?} D -->|Yes| E[✅ Solution Generation] D -->|No| F[❓ Request Clarification] E --> G[🔘 Create Buttons] G --> H[📺 Agent Display] style B fill:#fff3e0 style E fill:#e8f5e8 style G fill:#f1f8e9 style H fill:#e1f5fe

How it works:

The Synthesis Agent combines all the data collected from different sources and generates intelligent solutions. It assigns confidence scores to each solution based on data quality and consistency. When confidence is high enough, it automatically creates clickable action buttons that agents can use to resolve issues instantly.

Key benefit: Agents get ready-to-use solutions with one-click actions, eliminating the need to manually navigate through multiple systems.

✅ 7. Action Execution

User Story: As an agent, I want to resolve the problem with one click to improve customer experience.

flowchart LR A[🔘 Action Button Click] --> B[⚙️ Automatic Execution] B --> C{Action Successful?} C -->|Yes| D[✅ Confirmation] C -->|No| E[❌ Error Handling] D --> F[📞 Customer Notification] E --> G[🔄 Alternative Action] F --> H[📊 History Update] style A fill:#e1f5fe style B fill:#fff3e0 style D fill:#e8f5e8 style F fill:#f1f8e9

How it works:

When an agent clicks an action button, the system automatically executes the required operations across all relevant systems (databases, APIs, etc.). It handles success and error scenarios, provides immediate feedback, and updates all systems with the resolution. The customer is automatically notified of the resolution.

Key benefit: One-click resolution eliminates manual system navigation and reduces the risk of errors, providing faster and more reliable customer service.

🤖 Executive Demo: Multi-Agent Call Center

🖥️ Agent Interface (Live View)

🎤 Call Transcription

🎯 Detected Requirements

💡 Solutions & Actions

⚙️ System Process (Background)

🤖 Multi-Agent Call Center Flows

📞 1. Automatic Triggering

How it works:

🎤 2. Real-time Transcription

How it works:

🧠 3. Intelligent Requirement Detection

How it works:

🔍 4. Multi-Agent Research Orchestration

How it works:

⚡ 5. Parallel Agent Execution

Scenario 1: Multiple SQL Agents

Scenario 3: Mixed Configuration

How it works:

🎯 6. Synthesis and Action Generation

How it works:

✅ 7. Action Execution

How it works: