Ways to Leverage AI Agents

Redbird leverages AI agents across the platform to support processes ranging from data collection and processing through to insight generation, advanced analytics, data science, and output generation. As a user, selected agents are exposed for you to explicitly incorporate into your workflows to perform specific tasks.

This section highlights a sample of commonly used agents within Redbird. These examples illustrate some of the capabilities available across the platform but do not reflect the full range of agents or supported use cases.

If you would like a more comprehensive overview of available agents or are interested in enabling additional capabilities within your workspace, please contact [email protected].

Below, we describe the different ways AI agents can be leveraged within Redbird, depending on how structured, transparent, or dynamic you require their execution to be.

AI DT - For Step-by-Step, Granular Workflows

You can use Agents via AI DT (AI Data Tool) when you want AI embedded into granular, step-by-step workflows, alongside other data transformation and analysis steps.

This approach is ideal when:

• AI needs to feed into and out of other transformation or analysis steps
• You want fine-grained control within repeatable, production workflows
• AI logic needs to be composed within deterministic data processing

See here for more information on how to use AI DT.

AI Agent Run - AI Agents as Transparent Workflow Steps

An AI Agent Run allows you to run an AI agent as a single, standalone workflow node with clearly defined inputs and outputs.

This approach is best when:

• You want the agent’s inputs and requirements to be explicit, visible, and easy to inspect
• Those inputs should be editable without changing the rest of the workflow
• The agent needs to execute consistently and produce well-defined outputs

See here for more information on AI Agent Run.

AI Chat - Dynamic Agent Interaction

AI Chat provides a dynamic way to run AI agents using natural language prompts.

This mode is best suited for:

• Conversational interaction and ad hoc exploration
• Iterative questioning and rapid insight discovery
• Testing ideas before formalizing them into workflows

See here for more information on AI Chat.