Data Schemas¶

Data Schemas for Flowcept data.

Schemas:

PROV-AGENT and Flowcept¶

PROV-AGENT is a lightweight extension of W3C PROV for agentic workflows. It names the main building blocks you see in modern AI systems:

Activities such as Campaign, Workflow, and Task
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.

Flowcept stores provenance according to PROV-AGENT, but keeps the storage model simple. Everything is captured with two 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.

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 inside the records, most often in used and generated or in small structured blocks like telemetry and scheduling.
Relations are preserved with IDs and standard fields (for example, workflow IDs, parent or dependency links), so the graph remains connected and queryable.