Instead, outputs are designed to be interactive, real-time, and actionable.
Output Model
Avigrah provides two primary ways to interact with AI outputs:- Automation Panel → Structured execution and actions
- Conversation System → Real-time reasoning and insights
1. Automation Panel
The Automation Panel is used for structured execution of workflows and actions. It provides:- Predefined workflows
- Task execution controls
- System-triggered actions
- Integration-based operations
- Campaign creation
- User management actions
- Workflow execution results
- System-triggered updates
2. Conversation-Based System
The Conversation System enables real-time interaction with AI agents. It is designed for:- Dynamic queries
- On-demand analysis
- Continuous reasoning
Real-Time Execution
The system uses live execution streams (SSE).- Responses are generated progressively
- Intermediate reasoning can be surfaced
- Users can observe execution as it happens
Visual Outputs
AI responses can include:- Graph-based visualizations
- Structured data outputs
- Context-driven insights
- Pipeline data
- Retrieved context
- Agent reasoning
Interaction Model
Users can:- Ask questions
- Trigger analysis
- Explore data through conversation
- Query intent
- Available context
- Active data pipelines
Unified System Behavior
Both output modes are connected to the same execution layer:- Pipelines prepare data
- Context systems provide grounding
- AI agents generate outputs
- Integrations enable actions
- Consistency across outputs
- Real-time data usage
- Actionable intelligence
Execution Flow
User / System Trigger → AI Agents → Context + Data → Output / Action- Queries or workflows are triggered
- Agents process using available context
- Outputs are generated or actions are executed
Positioning
Outputs in Avigrah are not limited to insights. They represent a combination of:- Analysis (Conversation)
- Execution (Automation Panel)