---
id: orientation.user-roles
title: User roles
summary: Understand the responsibilities of project operators, domain experts, AI engineers, accountable owners, and organization administrators.
kind: reference
product_area: reference
status: stable
updated: 2026-08-22
canonical: /docs/getting-oriented/user-roles
---

# User roles

## Definition

Teammately work is coordinated across responsibilities rather than one universal operator. A **project operator** maintains reusable context, assets, standards, and benchmark work. A **domain expert** supplies specialist judgment through Contributions. An **AI engineer** owns candidate Harness behavior and evaluation interpretation. An **accountable owner** decides how customer-owned downstream processes use the evidence. An **organization administrator** manages membership and organizational controls.

Product roles and permissions determine access, but responsibility can still vary by team. The important rule is to preserve who supplied judgment, who changed an artifact, and who owns the next action.

## Fields, states, or lifecycle rules

- Project operators configure Agent Setup, Project Input Schema, Coverage Facets, Assets, and benchmark work according to their access.
- Domain experts use the focused Contribution experience and do not require the full project workbench to complete assigned work.
- Expert profiles are global rather than a project-specific roster; a Contribution selects an appropriate expert for its application domain.
- AI engineers save Harness versions, start or inspect Benchmark Evaluations, and participate in Improvement Sessions.
- Accountable owners inspect versioned evidence and retain authority over downstream product, governance, or operational choices.
- Organization administrators manage organization-level access and controls without becoming the automatic approver of every policy, rubric, or Contribution.
- AI agents prepare and coordinate work but do not inherit human authority.

## Responsibility handoffs

An operator can prepare a Contribution, but the expert owns the specialist judgment. The resulting material can be reconciled into Correctness Governance, Assets, or Coverage Management. An AI engineer can change the Harness and produce new Runs, but cannot rewrite the expert provenance behind a rubric. An accountable owner can act on the evidence without turning that action into a Teammately artifact unless a source-backed workflow exists.

{% example-demo title="Example: role handoff" %}
A project operator requests a grounding Contribution from the policy owner. The expert confirms source authority and contributes a rubric. The operator reconciles it in Correctness Governance. An AI engineer evaluates a saved Harness version and starts an Improvement Session from the failures. The accountable product owner later decides what downstream action to take from the evidence.
{% /example-demo %}

## Source confidence

Code-backed: current project navigation, Contribution expert routes, and administration surfaces support these responsibility boundaries. Exact permissions remain governed by the active role configuration.

## Related task pages

{% related-card-grid title="Related task pages" %}
- [Request an Expert Contribution](/docs/expert-contributions/request-contribution)
- [Complete an Expert Contribution](/docs/expert-contributions/complete-contribution)
- [Run a Benchmark Evaluation](/docs/benchmark-evaluations/run-evaluation)
{% /related-card-grid %}