C3 AI Workflows Overview
C3 AI Workflows enable you to build automated, multi-step processes that combine human logic with AI agent capabilities. Think of workflows as a way to connect different tasks together, where each step can involve data processing, decision-making, or agent interactions.
Workflow fundamentals
Workflows are automated sequences of connected tasks that process data and make decisions. Each workflow consists of individual steps called nodes that perform specific operations, connected by paths called edges that control the order of execution.
You can build workflows that handle complex business processes by breaking them into smaller, manageable steps. For example, a document review workflow is made up of nodes that handle information extraction, code validation, stakeholder-approval gathering, and publishing. Edges connect each node to form a sequential workflow.
Learn more about the Genai.Workflow Type by following the link.
Key benefits
Automation: Replace manual, repetitive processes with automated workflows that run consistently and reliably.
Agent integration: Combine structured data processing with AI agent reasoning and decision-making capabilities.
Visual design: Build workflows using a visual interface where you can see how data flows between different steps.
Reusability: Create workflow templates that can be used across different projects and scenarios.
Scalability: Handle large volumes of data or requests by distributing work across multiple processing nodes.
Key concepts
Nodes: Individual processing steps in your workflow. Each node performs a specific task like loading data, making calculations, or calling an AI agent. Nodes can be simple operations or complex processing logic.
Edges: Connections between nodes that control execution flow. Standard edges create sequential processing, while conditional edges enable branching based on data values or agent decisions.
Workflow state: A shared data workspace that maintains all information flowing through your workflow. Each node can read data from the state and write results back to it.
Templates: Reusable node definitions that specify what a node does, what inputs it needs, and what outputs it produces. Templates make it easy to build consistent workflows.
Agent integration: AI agents that can be integrated into workflow nodes to provide intelligent processing, decision-making, and reasoning capabilities. Dynamic agents are available out of the box, but workflows are not limited to them, you can also define and use your own custom agents within node templates.
Key Types
The workflow system uses several important Types:
Genai.Agent.Resource.Workflow: The core workflow functionality that maintains both construction-time (Python graph) and execution-time (GenaiCore graph) representations. Handles workflow creation, modification, execution, and state management with dynamic agent integration.
Genai.Agent.Dynamic.Workflow: A specialized Dynamic Agent type that extends the core Dynamic Agent to handle workflow-related operations. It manages the creation and initialization of workflow instances, providing the intelligence layer for workflow automation. This agent does not automatically participate in workflow execution, you must explicitly add an agent node if you want it involved in runtime processing.
Genai.Workflow.Node: Represents individual nodes within a workflow graph. Created from NodeTemplate specifications, each node contains input/output schemas, execution configuration, and can serve as START, GENERAL, or END points in the workflow.
Genai.Workflow.Config: Defines global configuration settings for workflow generation and execution. This singleton type manages available node template toolkits and maintains mappings between native Python objects and C3 resource types.
Genai.Workflow: Acts as a namespace container that organizes related workflow types under the Genai.Workflow hierarchy, providing logical grouping for workflow-related components.
Genai.WorkflowExecution: Represents a single execution instance of a workflow, tracking execution state, managing output resources, and maintaining execution history through unique thread IDs for monitoring progress and handling outputs.
Common use cases
Customer support automation: When a customer submits a support ticket, the workflow automatically categorizes the issue, checks against known solutions, and either provides an immediate response or escalates to the appropriate specialist. Dynamic agent can understand the customer's tone and urgency, ensuring critical issues get priority handling while routine questions receive instant answers.
Data analysis and reporting: Schedule workflows to pull data from multiple sources daily, clean and validate the information, run analysis algorithms, and generate reports for different stakeholders. Dynamic agents can spot unusual patterns in the data and provide intelligent insights or recommendations in plain language that business users can understand.
Order processing and fulfillment: When orders come in, workflows automatically verify inventory, process payments, update systems, and trigger shipping. Dynamic agents can handle special cases like bulk orders or international shipping requirements, making intelligent decisions about expedited processing or alternative fulfillment options.