Data Agent Instantiation
Creating running Data Agent instances from blueprints.
What is Instantiation?β
Instantiation is the process of creating a Data Agent instance from a Data Agent blueprint. Think of a blueprint as a recipeβit defines what data to collect, how to process it, and what insights to generate. An instance is the actual agent running that recipe, continuously processing data according to the blueprint's specifications.
Blueprint vs. Instanceβ
Blueprint - A template configuration that defines:
- Data sources to monitor
- Filters and processing rules
- Insights to generate
- Output format and structure
Instance - A running agent that:
- Executes the blueprint continuously
- Processes live data in real-time
- Generates fresh insights
- Provides API/MCP endpoints for access
One blueprint can have multiple instances. Each instance runs independently with its own configuration tweaks, allowing personalized agents for different users or use cases.
When to Instantiateβ
Personalized Data Needsβ
Create an instance when you need data tailored to specific requirements. Different projects, teams, or applications can each have their own instance with custom filters.
Example: A blueprint tracks crypto Twitter sentiment. Team A's instance focuses on DeFi tokens, Team B's instance tracks NFT projects, Team C's instance monitors L2 solutions.
Specific Filter Setsβ
Instantiate when you need narrow, focused data. Start with a broad blueprint and create instances that filter for exactly what each use case needs.
Example: A Reddit sentiment blueprint. Instance 1 monitors r/cryptocurrency, Instance 2 tracks r/defi, Instance 3 follows r/ethereum.
Independent Deploymentsβ
Each instance runs independently. If one instance has issues, others continue unaffected. This isolation provides reliability and fault tolerance.
Testing & Productionβ
Create separate instances for testing and production. Test new configurations on a dev instance before applying changes to your production instance.
How to Instantiateβ
Via COOK (Visual Interface)β
The easiest way to create instances:
- Browse Blueprints - Explore "Ready to Deploy" agents in COOK
- Select Blueprint - Choose an agent that matches your needs
- Configure Instance - Follow the 4-step wizard:
- Define your agent's purpose
- Customize intelligence settings
- Set your focus and filters
- Deploy and get endpoints
- Deploy - Your instance starts running immediately
Managing Multiple Instancesβ
Instance Lifecycleβ
Create - Deploy a new instance from a blueprint
Update - Modify configuration (filters, sources, settings)
Monitor - Track activity, performance, and output quality
Delete - Remove instances you no longer need
Best Practicesβ
Naming Convention - Use clear, descriptive names (e.g., "twitter-defi-sentiment-prod")
Configuration Management - Document what filters and settings each instance uses
Resource Planning - Consider rate limits and processing capacity when scaling instances
Regular Review - Periodically assess which instances are actively used
Instance Configurationβ
Each instance can be customized independently:
Data Filtersβ
- Keywords and hashtags
- User accounts to follow
- Geographic regions
- Time ranges
Processing Optionsβ
- Analysis depth
- Update frequency
- Insight types
- Output format
Integration Settingsβ
- API authentication
- Webhook endpoints
- MCP server configuration
- Rate limits
Use Cases by Instance Typeβ
Development Instanceβ
Test configurations, experiment with filters, validate outputs before production deployment.
Production Instanceβ
Serves live applications, feeds AI agents, powers real-time dashboards with reliable, tested configuration.
Specialized Instanceβ
Highly focused on narrow data sets (specific token, particular subreddit, single influencer).
Multi-tenant Instanceβ
Serves multiple users or applications with shared configuration but isolated data access.
Next Stepsβ
Ready to create your first instance?
- π Use COOK to Create Instances
- π Explore Replication
- π Python SDK Reference