Data Schemas

Data Schemas for Flowcept data.

Schemas:

• Task Data Schema
• Workflow Data Schema
• Blob Object Schema

PROV-AGENT and Flowcept

PROV-AGENT is a W3C PROV extension for capturing provenance of agentic AI workflows. It is described in:

18. Souza et al., PROV-AGENT: Unified Provenance for Tracking AI Agent Interactions in Agentic Workflows, IEEE International Conference on e-Science, Chicago, IL, USA, 2025. https://arxiv.org/abs/2508.02866

PROV-AGENT names the main building blocks you see in modern AI systems:

• Activities such as Campaign, Workflow, Task, AIModelInvocation, and AgentTool

• Agents such as an AI agent or a human user

• Data Objects such as domain data, prompts, responses, scheduling info, and telemetry

• Relations such as used, wasGeneratedBy, wasAssociatedWith, wasAttributedTo, and wasInformedBy

The goal is to keep agent interactions, model calls, and traditional tasks in one connected provenance graph.

How Flowcept represents PROV-AGENT

Flowcept stores provenance according to PROV-AGENT, but keeps the storage model simple. Everything is captured with three main record types:

• Workflow: high-level run context, user and environment info, and workflow-level inputs and outputs.

• Task: units of work with inputs, outputs, timing, telemetry, and links to other tasks and agents.

• Blob/Object: metadata and linkage for stored binary payloads, datasets, models, artifacts, and input files.

At a high level:

• Activities map to the Workflow and Task records.

• Agents attach to those records through simple fields, for example an agent identifier.

• Data Objects live in used and generated for inline provenance values, or in the objects collection when Flowcept stores payload metadata through BlobObject.

• Relations are preserved with IDs and standard fields (for example, workflow IDs, parent or dependency links), so the graph remains connected and queryable.

PROV-AGENT task subtypes

The subtype field on a Task record narrows it to a specific PROV-AGENT activity class. Use the PROV_AGENT enum to set these values:

Enum value	Stored string	Description
PROV_AGENT.AI_MODEL_INVOCATION	ai_model_invocation	A single LLM prompt→response call (AIModelInvocation in PROV-AGENT). Captured automatically by FlowceptLLM. used.prompt stores the input; generated.response stores the output; custom_metadata.llm_usage stores token counts.
PROV_AGENT.AGENT_TOOL	agent_tool	A tool execution by an AI agent (AgentTool in PROV-AGENT). Captured automatically by the agent_flowcept_task() decorator applied to MCP tools and LangGraph tool nodes. used stores tool arguments; generated stores the return value.

The wasInformedBy relation — an AgentTool activity informing an AIModelInvocation — is the key link for root-cause analysis and downstream impact tracing in PROV-AGENT. In Flowcept this is expressed through the agent_id field: every task with the same agent_id belongs to the same AI agent and can be queried together to reconstruct the full agent provenance graph.

The UI uses subtype to visually distinguish AI agent activities from regular workflow tasks. Filter for subtype == "ai_model_invocation" or subtype == "agent_tool" to isolate agent interactions from the provenance database.

Figure

PROV-AGENT overview. Dashed arrows denote subClassOf.