Advanced Topics

As you become more proficient with AI Builder, understanding and leveraging its event-driven architecture can help you build more sophisticated, powerful, and efficient applications. This guide explores advanced topics focused on event-driven patterns and their practical applications.

Event-Driven Architecture (EDA)

Event-driven architecture is the foundation of AI Builder’s flexibility:

Events as First-Class Citizens: All system and user actions generate events
Decoupled Components: Services communicate through events, not direct calls
Asynchronous Processing: Actions occur independently of event producers
Scalability: Components can scale independently based on event load
Extensibility: New capabilities can subscribe to existing event streams

In Prisme.ai, events flow through the system as messages containing:

An event type (e.g., message.created, user.login)
A payload with event-specific data
Metadata about the source, timestamp, and routing information

Working with Events

Emitting Events

In automations, you can emit events to trigger other processes:

- emit:
    event: user-action-completed
    payload:
      userId: "{{user.id}}"
      action: "profile-update"
      timestamp: "{{now}}"

Blocks can also emit events when users interact with them:

- slug: Action
  text: Update Profile
  type: event
  value: user-update-profile
  payload:
    section: "personal-info"

These events flow through the system and can trigger other automations or be recorded for analysis.

Listening for Events

Automations can be triggered by specific events:

slug: "process-profile-update"
name: "Process Profile Update"
when:
  events:
    - user-update-profile
do:
  - set: payload
    value: "{{event.payload}}"
  - callAPI:
      method: POST
      url: /api/profiles/update
      body: "{{payload}}"

This creates a chain of actions that can flow through your application, each step triggered by the completion of previous steps.

Accessing Event History

View event history in several ways:

Activity Tab: See recent events in your workspace
Event Explorer: Query and filter events for analysis
Elasticsearch/OpenSearch: Advanced querying for deeper analysis

The complete event stream provides valuable insights into application usage, performance, and user behavior.

Analyzing Event Patterns

Advanced analytics can reveal important patterns:

User Journeys: Track how users move through your application
Bottlenecks: Identify where processes slow down
Error Patterns: Detect recurring issues
Usage Trends: See how usage evolves over time
Feature Adoption: Measure which features are most used

These insights drive continuous improvement of your applications.

Advanced Event Analytics

Every event in your workspace is stored in Elasticsearch/OpenSearch, enabling custom analysis:

System Mapping

Create visual maps of your systems based on actual usage:

Track event flows between components

Visualize user journeys through your application

Identify unused features or dead-end paths

Discover unexpected usage patterns

Map integration points with external systems

Usage Analytics

Understand how users engage with your applications:

Measure feature adoption and frequency of use

Track user session patterns and duration

Identify popular and underutilized features

Analyze conversion funnels and drop-off points

Segment users by behavior patterns

Performance Monitoring

Track system performance metrics:

Measure response times for different operations

Identify bottlenecks in processing flows

Track API usage and latency

Monitor automation execution times

Analyze resource utilization patterns

Pattern Discovery

Find meaningful patterns in your event data:

Discover common user behavior sequences

Identify correlations between events

Detect anomalies that may indicate issues

Recognize seasonal or time-based patterns

Find optimization opportunities

Event Mapping for Analytics

Introduction to Event Mapping

Benefits of Explicit Mapping

Implementing Event Mapping

To implement explicit event mapping, add configuration to your workspace YAML:

events:
  types:
    usage:
      schema:
        usage:
          type: object
          title: Usage
          properties:
            total_tokens:
              type: number
            completion_tokens:
              type: number
            prompt_tokens:
              type: number
            cost:
              type: number
              format: double
            firstTokenDuration:
              type: number

This example defines the schema for the usage event type, specifying that fields like usage.total_tokens and usage.cost should be treated as numeric values with specific formats.

When implementing:

Identify the event types requiring explicit mapping
Define their schema with appropriate data types
Add the configuration to your Workspace
Emit sample events
Test with analytical queries to verify proper behavior

When declaring a new field inside events mapping, ES/OS should take it into account for any upcoming event.

When updating an existing field inside events mapping, the ES/OS index must be rollover’ed to take it into account for future events.

Advanced Usage Analytics Example

Here’s how you might use mapped events for advanced analytics:

// Query to analyze token usage patterns over time
const usageAnalytics = await searchEvents({
  "query": {
    "bool": {
      "filter": [
        {
          "term": {
            "type": "usage"
          }
        }
      ]
    }
  },
  aggs: {
    usage_over_time: {
      date_histogram: {
        field: "createdAt",
        interval: "day"
      },
      aggs: {
        total_tokens: {
          sum: {
            field: "aggPayload.usage.total_tokens"
          }
        },
        avg_cost: {
          avg: {
            field: "aggPayload.usage.cost"
          }
        },
        models: {
          terms: {
            field: "aggPayload.model"
          }
        }
      }
    }
  }
});

// Results can be used for visualizations, billing, or optimization

With proper mapping, these aggregations will be fast and accurate, providing valuable insights into application usage and performance.

Note our aggregation needs to target aggPayload.* to benefit from our custom mapping

Practical Event-Driven Patterns

User Activity Tracking

Track and analyze user behavior:

# Emit events for user actions
- emit:
    event: user-action
    payload:
      action: page-view
      page: "{{page.slug}}"
      sessionId: "{{session.id}}"
      timestamp: "{{now}}"

These events can be analyzed to:

Create user journey maps
Identify popular features
Measure engagement
Detect unusual behavior
Personalize experiences based on usage patterns

AI Model Performance Tracking

Monitor and optimize AI model usage:

# Emit events for AI model usage
- emit:
    event: model-usage
    payload:
      model: "{{model.name}}"
      prompt_tokens: "{{result.usage.prompt_tokens}}"
      completion_tokens: "{{result.usage.completion_tokens}}"
      total_tokens: "{{result.usage.total_tokens}}"
      duration: "{{result.duration}}"
      cost: "{{result.cost}}"

This data enables:

Cost tracking and optimization
Performance benchmarking
Model selection refinement
Usage pattern analysis
Identifying optimization opportunities

Multi-Step Workflows

Implement complex business processes through event chains:

# First automation: Document processing initiated
- emit:
    event: document-processing-started
    payload:
      documentId: "{{document.id}}"
      stage: "initiated"
      
# Second automation (triggered by the first event)
when:
  events:
    - document-processing-started
do:
  # Process document...
  - emit:
      event: document-processing-completed
      payload:
        documentId: "{{event.payload.documentId}}"
        stage: "processed"
        
# Third automation (triggered by the second event)
when:
  events:
    - document-processing-completed
do:
  # Generate summary...
  - emit:
      event: document-summary-ready
      payload:
        documentId: "{{event.payload.documentId}}"
        stage: "summarized"

This approach creates modular, maintainable workflows that are:

Easily extendable with new steps
Resilient to failures (steps can be retried individually)
Transparent (full visibility into process state)
Analyzable (measure performance of each step)

Overview

AI SecureChat

AI Store

AI Knowledge

AI Builder

AI Governance

AI Collection (beta)

AI Insights (beta)

Event-Driven Architecture (EDA)

Working with Events

Advanced Event Analytics

System Mapping

Usage Analytics

Performance Monitoring

Pattern Discovery

Event Mapping for Analytics

Reliability and Consistency

Performance Optimization

Maintenance and Scalability

Cross-Platform Compatibility

Practical Event-Driven Patterns

Overview

AI SecureChat

AI Store

AI Knowledge

AI Builder

AI Governance

AI Collection (beta)

AI Insights (beta)

​Event-Driven Architecture (EDA)

​Working with Events

​Advanced Event Analytics

System Mapping

Usage Analytics

Performance Monitoring

Pattern Discovery

​Event Mapping for Analytics

​Practical Event-Driven Patterns

Event-Driven Architecture (EDA)

Working with Events

Advanced Event Analytics

Event Mapping for Analytics

Practical Event-Driven Patterns