Agent using predefined Prompts from Datatable
An AI agent workflow that dynamically selects and executes predefined prompts from a datatable, enabling structured, reusable, and scalable conversational logic.
This project demonstrates an AI agent built around predefined prompts stored in a datatable, allowing the agent to respond intelligently based on structured instructions rather than hard-coded logic.
The workflow listens for incoming chat messages, retrieves relevant prompt definitions from a centralized data source, and routes them through an AI agent to perform context-aware actions such as retrieving records, creating tasks, or updating existing data. By separating prompt logic from the agent itself, the system becomes easier to maintain, extend, and scale.
Key highlights include:
- Centralized prompt management via a datatable
- Dynamic prompt selection based on user input
- Modular AI agent design with tool integration
- Clean separation between data, logic, and execution
- Ideal for task automation, internal tools, and AI-powered assistants
This approach makes it simple to update agent behavior without redeploying workflows, enabling faster iteration and more reliable AI systems.
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