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Context

Overview

Context is the foundation of intelligent decision-making in the Intent Orchestrator Platform. It encompasses all relevant information that influences how intents are understood, processed, and executed by AI agents.

Context Types

User Context

  • Profile Information: User preferences, roles, and permissions
  • Interaction History: Previous conversations and actions
  • Current State: Active sessions and ongoing processes
  • Preferences: Personal settings and behavioral patterns

System Context

  • Environment State: Current system configuration and status
  • Resource Availability: Available computational and storage resources
  • Network Conditions: Connectivity and performance metrics
  • Security Status: Authentication and authorization state

Domain Context

  • Business Rules: Organizational policies and procedures
  • Industry Standards: Relevant regulations and best practices
  • Historical Data: Past performance and outcomes
  • External Factors: Market conditions and environmental variables

Temporal Context

  • Time Sensitivity: Urgency and deadline information
  • Seasonal Patterns: Time-based trends and behaviors
  • Scheduling Constraints: Availability and timing requirements
  • Historical Trends: Long-term patterns and cycles

Context Management

Context Collection

  • Active Monitoring: Continuous gathering of relevant information
  • Event-Driven Updates: Real-time context modification based on events
  • User Input Processing: Integration of explicit user information
  • External Data Integration: Incorporation of third-party data sources

Context Storage

  • Hierarchical Organization: Structured storage for different context types
  • Version Control: Tracking context changes over time
  • Access Control: Secure management of sensitive context information
  • Performance Optimization: Efficient storage and retrieval mechanisms

Context Processing

  • Relevance Assessment: Determining which context information is most important
  • Conflict Resolution: Handling contradictory or competing context data
  • Context Enrichment: Adding derived insights and relationships
  • Quality Assurance: Validating context accuracy and completeness

Context Utilization

Intent Understanding

  • Ambiguity Resolution: Using context to clarify unclear requests
  • Intent Classification: Categorizing requests based on available context
  • Priority Assessment: Determining urgency and importance
  • Scope Definition: Understanding the boundaries of the request

Agent Coordination

  • Task Assignment: Matching agents to tasks based on context
  • Resource Allocation: Distributing resources according to context needs
  • Workflow Optimization: Adjusting processes based on current conditions
  • Collaboration Facilitation: Enabling effective inter-agent communication

Decision Making

  • Policy Application: Applying relevant rules and constraints
  • Risk Assessment: Evaluating potential outcomes and consequences
  • Alternative Evaluation: Considering different approaches and solutions
  • Outcome Prediction: Forecasting likely results of actions

Context Privacy and Security

Data Protection

  • Encryption: Securing context data at rest and in transit
  • Access Controls: Limiting who can view and modify context
  • Audit Trails: Tracking context access and modifications
  • Data Minimization: Collecting only necessary context information

Privacy Compliance

  • User Consent: Obtaining appropriate permissions for context collection
  • Data Retention: Managing how long context information is stored
  • Right to Deletion: Allowing users to remove their context data
  • Transparency: Providing clear information about context usage

Best Practices

Context Design

  • Relevance Focus: Only include information that affects decision-making
  • Timeliness: Ensure context information is current and up-to-date
  • Completeness: Include all necessary information for effective processing
  • Consistency: Maintain coherent context across different system components

Implementation Guidelines

  • Efficient Updates: Minimize overhead when modifying context
  • Scalable Architecture: Support growing context complexity
  • Error Handling: Gracefully manage context-related failures
  • Performance Monitoring: Track context processing efficiency

This documentation is part of the Intent Orchestrator Platform. For more information, see the Core Concepts overview.