Complete Database Schema For Multi-Agent Systems

🛠️ Infrastructure Implementation: A Deep Dive into Database Schema (30+ Tables)

In the ever-evolving landscape of multi-agent support systems, a robust and scalable database schema is paramount. This comprehensive guide outlines the implementation of a complete database schema comprising over 30 tables, meticulously designed to support 243 agents across various tiers. We will explore the critical aspects of this infrastructure, ensuring optimal performance, data integrity, and a seamless user experience. The current state features a basic schema, and the goal is to transform it into a comprehensive database schema that supports all business domains and the agents. This includes customer data, conversations, messages, subscriptions, sales, analytics, and agent performance tracking. This transformation is not merely an upgrade; it's a fundamental shift towards a more efficient, data-driven, and agent-centric support system. Let's delve into the core of this infrastructure enhancement.

The Foundation: Database Tables to Create

The construction of a well-structured database schema is the cornerstone of a successful multi-agent support system. The implementation of this schema involves a series of meticulously designed tables, each serving a specific purpose within the broader ecosystem. Here's a breakdown of the key domains and the tables that support them:

1. Customer Domain (6 tables)

• Customers: This existing table requires expansion. Adding columns such as company_name, company_size, industry, mrr, ltv, health_score, churn_risk, nps_score, lead_source, country, timezone, and language. These additions enable a more holistic view of each customer and its relationship with the company.
• Customer Health Events: Tracks changes in customer health scores, indicating potential issues or successes.
• Customer Segments: Enables customer segmentation for targeted marketing and support strategies.
• Customer Notes: Provides a space for internal notes about customers, fostering better communication and understanding within teams.
• Customer Contacts: Allows for multiple contacts per customer, ensuring all relevant stakeholders are accounted for.
• Customer Integrations: Tracks connected integrations, streamlining workflows and data synchronization.

2. Conversation Domain (5 tables)

• Conversations: Another existing table that needs expansion. It stores the history and details of each interaction. This also includes the addition of columns such as channel, intent_confidence, emotion, resolved_by_agent, first_response_time_seconds, csat_score, and last_activity_at, providing a comprehensive view of conversations.
• Messages: Existing table expansion to include agent_confidence, urgency, model_used, and tokens_used. This tracks the individual messages within each conversation.
• Agent Handoffs: Records agent-to-agent handoffs, providing insights into the flow of conversations.
• Agent Collaborations: Enables multi-agent collaboration records, facilitating complex issue resolution.
• Conversation Tags: Allows for the tagging of conversations for analysis and categorization.

3. Subscription & Billing (5 tables)

• Subscriptions: Manages customer subscriptions, including plan details, billing cycles, and status.
• Invoices: Records invoice details, facilitating accurate billing and financial tracking.
• Payments: Tracks payment transactions, ensuring accurate financial records.
• Usage Events: Monitors usage for metering, enabling accurate billing and resource allocation.
• Credits: Manages account credits and promotions, providing incentives to customers.

4. Sales & Leads (5 tables)

• Leads: Tracks lead information, enabling effective lead management and conversion strategies.
• Deals: Manages sales opportunities, providing insights into the sales pipeline.
• Sales Activities: Records sales activities such as calls, emails, and demos, offering a detailed overview of sales efforts.
• Quotes: Manages price quotes, streamlining the sales process.
• Competitors: Tracks competitor information, providing insights into the competitive landscape.

5. Analytics & Metrics (4 tables)

• Agent Performance: An existing table requiring expansion, tracking key metrics for agent performance evaluation.
• Conversation Analytics: Aggregates conversation metrics, providing insights into conversation performance.
• Feature Usage: Tracks feature adoption, offering insights into how customers utilize the platform.
• A/B Tests: Records A/B test results, enabling data-driven decision-making.

6. Automation & Workflow (3 tables)

• Workflows: Defines automated workflow definitions, streamlining operations.
• Workflow Executions: Tracks workflow run history, providing insights into workflow performance.
• Scheduled Tasks: Schedules automated tasks, ensuring tasks are executed efficiently.

7. Security & Compliance (2 tables)

• Audit Logs: Records all system actions, ensuring a secure and compliant environment.
• Data Retention Policies: Defines data retention rules, ensuring compliance with regulations.

8. Employees & Teams (2 tables)

• Employees: Manages internal team members, facilitating organizational structure.
• Teams: Defines the team structure within the organization, enabling efficient collaboration.

Step 1: Expanding Existing Tables

customers table expansion:

We start by expanding the customers table. Additional columns such as company_name, company_size, industry, mrr, ltv, health_score, churn_risk, nps_score, lead_source, country, timezone, and language are added using the SQL ALTER TABLE statement. This enriches the customer profiles and allows for a more detailed analysis of the customer base. These additional columns include both basic and advanced data for a complete customer profile.

conversations table expansion:

The next step involves expanding the conversations table. Adding columns such as channel, intent_confidence, emotion, resolved_by_agent, first_response_time_seconds, csat_score, and last_activity_at. These additions improve tracking and analytics by providing a clear overview of the agent activity. This helps improve the agent workflow.

messages table expansion:

Expanding the messages table enhances our ability to track individual messages. Adding columns like agent_confidence, urgency, model_used, and tokens_used. These additions offer detailed insights into the nature and content of each message, enabling improved data analysis and a more nuanced understanding of conversations.

Step 2: Create New Core Tables

customer health events table:

The customer_health_events table is created to track changes in customer health. This table includes columns such as event_type, old_value, new_value, reason, detected_by, and severity, providing a detailed audit trail of health-related events. An index is created on customer_id and created_at to optimize query performance, allowing for quick retrieval of health events.

subscriptions table:

The subscriptions table manages customer subscription details. It includes columns such as plan, billing_cycle, mrr, arr, seats_total, seats_used, status, current_period_start, and current_period_end. The table also includes check constraints to enforce data integrity, ensuring that plans, billing cycles, and statuses are valid. Indexes are created on customer_id and status to optimize search queries.

agent handoffs table:

The agent_handoffs table tracks the handoff of conversations between agents. It includes columns such as from_agent, to_agent, handoff_reason, state_transferred, confidence_before, and latency_ms. An index is created on conversation_id and timestamp to ensure swift retrieval of handoff data. This provides details for the agent to know what steps were taken to fix the customer issues.

agent collaborations table:

The agent_collaborations table documents collaboration among agents. This table includes columns for collaboration_type, agents_involved, coordinator_agent, start_time, end_time, duration_ms, and outcome. An index is created on conversation_id and collaboration_type to enhance query performance.

leads table:

The leads table tracks potential leads, including their email, name, company, title, and lead-related details such as lead_score. Check constraints ensure data consistency, particularly concerning lead scores and qualification statuses. Indexes are added for efficient searching and filtering. This table manages potential sales.

Step 3: Create SQLAlchemy Models

Creating SQLAlchemy models enables interaction with the database. The Subscription model is created, including columns for subscription details, relationships with the Customer and Invoice models, and check constraints to maintain data integrity. Properties like is_active, is_trial, days_until_renewal, and seat_utilization are included to provide convenient data access. This ensures that the agent has access to all the information needed.

Testing Requirements

Rigorous testing is a fundamental component of a successful database schema implementation. The testing strategy encompasses: testing all 30+ tables exist, ensuring that the Customer model has all necessary fields, and testing the Subscription to the Customer relationship, and testing that all check constraints function correctly. The testing process also involves testing constraints, relationships, and the proper functioning of the models to ensure data integrity and system reliability.

Documentation Requirements

Comprehensive documentation is crucial for maintaining and understanding the database schema. The documentation includes an overview, an entity-relationship diagram, detailed table descriptions, index listings, and performance considerations. The documentation provides a comprehensive guide to understanding the database schema and its components.

Implementation Notes

Careful planning and execution are essential for the smooth implementation of the database schema. It is advisable to run the migration on a staging environment before production, back up the production database, and thoroughly test the rollback process. It's also important to monitor query performance after adding indexes and document any breaking changes.

In conclusion, the implementation of this complete database schema is a critical undertaking that lays the groundwork for a robust and efficient multi-agent support system. The comprehensive structure of the database, combined with rigorous testing and thorough documentation, ensures that the system is scalable, reliable, and capable of meeting the evolving needs of the business. By investing in this infrastructure, we are setting the stage for future growth, innovation, and enhanced customer support capabilities.

For a deeper dive into database design and best practices, check out the official documentation on database schema design.