FaceUp is an anonymous reporting tool designed for companies and schools, enabling employees and students to safely report issues and misconduct.
FaceUp is an anonymous reporting tool designed for companies and schools, enabling employees and students to safely report issues and misconduct. On Nagent, Faceup is exposed as a fully-configurable customer support integration that any agent can call — 1 action, and API key authentication. No code is required to wire Faceup into your workflow — connect it once via the External Integrations panel and reuse it across every agent you build.
Agent builders use Faceup to automate the kinds of tasks customer support teams previously handled manually. Concrete examples — each one is a single agent step in Nagent — include:
Every action and trigger is paired with a structured input/output schema (visible in the sections below), so when you wire Faceup into Helix — our agentic agent builder — the editor knows exactly what each step expects and produces. Configure once, deploy anywhere across your Nagent agents.
Every operation an agent can call against Faceup, with input parameters and output schema. Drop these into any step of an agent built in Helix.
FACEUP_GRAPHQL_STATISTICS_QUERYExecute GraphQL queries against the FaceUp statistics endpoint. Retrieves whistleblowing statistics with filtering by report criteria. Requires filter (ReportFilterInput), isPartnerAdministration, onlyAccessibleReports, and motherIds parameters. Returns both data and errors from the GraphQL API for flexible error handling.
Input parameters
The GraphQL query string to execute against the FaceUp statistics endpoint. The statistics query requires these arguments: filter (ReportFilterInput!), isPartnerAdministration (Boolean!), onlyAccessibleReports (Boolean!), and motherIds (\[UUID!\]!). Use __typename to discover available fields. Note: Introspection queries (__schema, __type) are disabled.
Variables for the GraphQL query. For statistics queries, provide: filter (ReportFilterInput object, can be empty {}), isPartnerAdministration (boolean), onlyAccessibleReports (boolean), and motherIds (array of valid UUIDs). Example: {'filter': {}, 'isPartnerAdministration': false, 'onlyAccessibleReports': true, 'motherIds': \[\]}
Output
Data from the action execution
Error if any occurred during the execution of the action
Whether or not the action execution was successful or not
No publicly available marketplace agent is found using this tool yet. There are 68 agents privately built on Nagent that already use Faceup.
Build on Nagent
Connect Faceup to any Nagent agent in minutes — no API key management, no boilerplate. Just configure and deploy.
The five questions agent builders ask before adopting a new integration.
Open the External Integrations panel inside Nagent (app.nagent.ai/externalIntegration), find Faceup, and click "Connect Now." You'll authenticate with an API key — Nagent handles credential storage and refresh automatically. Once connected, Faceup is available to any agent in your workspace.
No. Nagent provides no-code integration for every tool. Once Faceup is connected, you configure its 1 action directly in the agent builder UI — no API calls, no boilerplate, no schema management.
Helix — Nagent's agentic agent builder — lets you drop Faceup steps into any workflow visually. Pick an action (e.g., one of those listed above), fill in the inputs (Helix knows the required vs. optional schema for each parameter), and connect it to upstream/downstream steps. Triggers run as the entry point of an agent, so when a Faceup event fires, the agent kicks off automatically.
Every Faceup action and trigger ships with a fully-typed schema — input parameters with name, type, required flag, and description, plus the output payload shape. The schemas are documented in the sections above. Helix uses these schemas to validate your configuration at build time and to type-check the data flowing between steps.
Yes. While Faceup ships with 1 pre-built customer support action, you can layer custom logic around them inside Helix — pre/post-processing steps, conditional branches, retries, or stitching Faceup together with other connected tools. For deeper customization, talk to our team about Nagent's Agentic AI Lab — forward-deployed engineers who build Faceup-based workflows tailored to your business.