> ## Documentation Index > Fetch the complete documentation index at: https://docs.getoneprofile.ai/llms.txt > Use this file to discover all available pages before exploring further. # Create a saved agent > Create an org-level reusable agent: declared inputs, a prompt referencing them, output shape, model and tools. Agent columns can point at it. ## OpenAPI ````yaml /openapi.json post /table-enrichments/saved-agents/ openapi: 3.1.0 info: contact: email: support@getoneprofile.ai name: Oneprofile Support description: >- REST API reference for Oneprofile, the GTM data layer for your CRM. Manage tables, enrichments, and integrations. Authenticate with an API key to get started. termsOfService: https://www.getoneprofile.ai/terms title: API Reference | Oneprofile version: '0.0' servers: - url: https://api.getoneprofile.ai security: [] tags: - description: Sender profiles, connected mailboxes, tags, and the Google connect flow. name: senders - description: The workspace do-not-contact list, enforced before every send. name: suppression - description: 'Sequence authoring: the graph, the table-backed audience, previews.' name: sequences - description: >- The LinkedIn channel: connecting an account to a sender, its safety limits and history, and the protocol the browser extension speaks. name: linkedin - description: 'The unified outreach inbox: threads, replies, labels, events.' name: inbox - description: 'The AI reply layer: workspace settings, drafts, and approval.' name: inbox-ai - description: Notification rules, per-user preferences, and the Slack connection. name: notifications - description: Endpoints for seeing all available integration types. name: integration-types - description: Endpoints for creating and managing integrations. name: integrations - description: Endpoints for the generic OAuth install/claim app-listings engine. name: app-listings - description: Endpoints for creating and managing API keys. name: apikey - description: Endpoints for managing billing and subscription information. name: billing - description: Endpoints for creating and managing tables. name: tables - description: Endpoints for table columns, formula previews, and on-demand column runs. name: table-columns - description: Endpoints for table rows and manual cell edits. name: table-rows - description: Endpoints for observing and cancelling table recompute runs. name: table-runs - description: >- Endpoints for org-shared per-column display preferences: hidden, pinned, width, and colour tint. name: table-ui-prefs - description: >- Endpoints for the org-shared names given to the column tint palette: what this organisation calls each colour. Org-level, not per-table. name: table-column-color-labels - description: >- Endpoints for the enrichment action catalog, model list, presets, draft generation, and saved agents. name: table-enrichments - description: >- Endpoints for teaching a saved agent from example rows: training sessions, per-row feedback, and the proposed prompt to approve. name: agent-training - description: >- The durable audit trail of a table's enrichment actions: configured, edited, run, cancelled, and settled cost. name: table-enrichment-audit-logs - description: Endpoints for saving and applying column templates. name: table-templates - description: Server-sent change feed for a table's grid. name: table-events - description: 'Endpoints for org-shared saved table views: sort, filters, hidden columns.' name: table-views - description: >- The per-table AI Assistant: named conversations, the column suggestions a build turn produces, and the questions an analyze turn answers. name: table-assistant - description: >- Endpoints for PER-USER table preferences: favourites and recently opened. Unlike the rest of the table surface these are personal, so two members of one organisation legitimately see different answers. name: table-user-prefs - description: >- Endpoints for building a table from an uploaded CSV: upload and preview the inferred columns, commit the reviewed mapping, then poll the row load. name: table-imports - description: Endpoint for downloading a table's rows as CSV. name: table-export - description: >- Endpoints for the integration a table imports from: attach and preview it, promote imported fields into columns, and run the import on demand. name: table-sources - description: >- Endpoints for a table's recurring import cadence: set it, pause it, and read the next fire times. name: table-schedules - description: >- Endpoints for pushing a table's rows out to a connected integration: list writable destinations, derive their write capabilities, and map export columns. name: table-writeback - description: >- Endpoints backing a lookup column's setup: the connections whose records a column could read, and the identity fields one record type can be looked up by. name: table-integration-lookup - description: >- Endpoints for sourcing leads from the lead database: count and preview matches, land them in a table, and keep saved searches. name: find-leads - description: >- Endpoints for discovering a domain's subdomains from public certificate logs and landing the results in a table. name: find-subdomains - description: Endpoints for the credit balance meter and usage reporting. name: credits - description: >- Endpoints for the workspace referral program: the shareable link, the friends who joined through it, and claiming a code after signup. name: referral - description: >- Endpoints for the design partner program's customer surface: whether this workspace is a design partner, the badges its feedback has earned, and sending and reading that feedback. name: design-partner - description: >- Endpoints for the org-level AI business context — company description, ideal customer profile, buyer personas and interest signals — plus domain-based generation. name: ai-context paths: /table-enrichments/saved-agents/: post: tags: - table-enrichments summary: Create a saved agent description: >- Create an org-level reusable agent: declared inputs, a prompt referencing them, output shape, model and tools. Agent columns can point at it. operationId: create_saved_agent requestBody: content: application/json: schema: $ref: '#/components/schemas/SavedAgentCreateSchema' required: true responses: '201': content: application/json: schema: $ref: '#/components/schemas/SavedAgentReadSchema' description: Successful Response '401': content: application/json: schema: $ref: '#/components/schemas/UnauthorizedResponse' description: Unauthorized '403': content: application/json: schema: $ref: '#/components/schemas/ForbiddenResponse' description: Forbidden '422': content: application/json: schema: $ref: '#/components/schemas/UnprocessableEntityResponse' description: Invalid request made to the API. '500': content: application/json: schema: $ref: '#/components/schemas/ServerErrorResponse' description: Internal server error. '502': content: application/json: schema: $ref: '#/components/schemas/ServerErrorResponse' description: Bad gateway error. '503': content: application/json: schema: $ref: '#/components/schemas/ServerErrorResponse' description: Service unavailable. security: - HTTPBearer: [] - APIKeyHeader: [] components: schemas: SavedAgentCreateSchema: description: A new saved agent's full configuration. properties: allow_fetch: default: true title: Allow Fetch type: boolean allow_search: default: true title: Allow Search type: boolean description: anyOf: - maxLength: 2000 type: string - type: 'null' title: Description effort: $ref: '#/components/schemas/AgentEffortLevel' default: standard icon: anyOf: - maxLength: 16 minLength: 1 type: string - type: 'null' title: Icon inputs: description: The declared inputs, in the author's order. items: $ref: '#/components/schemas/AgentInputSchema' maxItems: 20 title: Inputs type: array is_template: default: false description: >- Save as a reusable starting point instead of a working agent. Templates are offered in the template gallery and never run. title: Is Template type: boolean model_key: anyOf: - maxLength: 64 minLength: 1 type: string - type: 'null' title: Model Key name: maxLength: 255 minLength: 1 title: Name type: string output_schema: anyOf: - discriminator: mapping: fields: $ref: '#/components/schemas/EnrichmentOutputFields-Input' json_schema: $ref: '#/components/schemas/EnrichmentOutputRawSchema' propertyName: mode oneOf: - $ref: '#/components/schemas/EnrichmentOutputFields-Input' - $ref: '#/components/schemas/EnrichmentOutputRawSchema' - type: 'null' title: Output Schema prompt: $ref: '#/components/schemas/AgentPromptTemplate-Input' sample_rows: description: >- Example input rows to remember for the Train screen — the `test_cases` the build flow just generated. Keyed by input name. Suggestion memory, not configuration: storing them never bumps the agent's version. items: additionalProperties: type: string type: object maxItems: 25 title: Sample Rows type: array use_business_context: default: false title: Use Business Context type: boolean use_row_context: default: false title: Use Row Context type: boolean required: - name title: SavedAgentCreateSchema type: object SavedAgentReadSchema: description: One org-level saved agent as the editor, picker and library render it. properties: allow_fetch: title: Allow Fetch type: boolean allow_search: title: Allow Search type: boolean created_at: format: date-time title: Created At type: string current_version: title: Current Version type: integer description: anyOf: - type: string - type: 'null' title: Description effort: $ref: '#/components/schemas/AgentEffortLevel' favorite: title: Favorite type: boolean icon: anyOf: - type: string - type: 'null' title: Icon id: format: uuid title: Id type: string inputs: items: $ref: '#/components/schemas/AgentInputSchema' title: Inputs type: array is_template: default: false title: Is Template type: boolean model_key: anyOf: - type: string - type: 'null' title: Model Key name: title: Name type: string output_schema: anyOf: - discriminator: mapping: fields: $ref: '#/components/schemas/EnrichmentOutputFields-Output' json_schema: $ref: '#/components/schemas/EnrichmentOutputRawSchema' propertyName: mode oneOf: - $ref: '#/components/schemas/EnrichmentOutputFields-Output' - $ref: '#/components/schemas/EnrichmentOutputRawSchema' - type: 'null' title: Output Schema owner_display_name: anyOf: - type: string - type: 'null' title: Owner Display Name owner_user_id: anyOf: - type: string - type: 'null' title: Owner User Id prompt: $ref: '#/components/schemas/AgentPromptTemplate-Output' updated_at: format: date-time title: Updated At type: string use_business_context: title: Use Business Context type: boolean use_row_context: title: Use Row Context type: boolean required: - id - name - description - effort - created_at - updated_at - prompt - inputs - output_schema - model_key - allow_search - allow_fetch - use_business_context - use_row_context - owner_user_id - favorite - icon - current_version title: SavedAgentReadSchema type: object UnauthorizedResponse: properties: detail: description: The detail of why the request is unauthorized. title: Detail type: string required: - detail title: UnauthorizedResponse type: object ForbiddenResponse: properties: detail: description: The detail of why the request is forbidden. title: Detail type: string required: - detail title: ForbiddenResponse type: object UnprocessableEntityResponse: properties: detail: description: >- The detail of the error. This is the error message to show to the user. You should also use the issues field to show more detailed error messages per field. Use this field for a single liner error message. title: Detail type: string issues: description: >- The issues that caused the error. There can be multiple issues. Each issue is a user-facing error message. items: $ref: '#/components/schemas/UnprocessableEntityIssue' title: Issues type: array required: - detail - issues title: UnprocessableEntityResponse type: object ServerErrorResponse: properties: detail: description: The detail of why the server returned an error. title: Detail type: string required: - detail title: ServerErrorResponse type: object AgentEffortLevel: description: |- How hard the Agent works on each row. Named levels rather than raw step counts: the level is the user-facing contract, and what each level buys (step and page budgets) is a product decision the loop maps internally — re-tuning budgets never invalidates stored columns. enum: - low - standard - high title: AgentEffortLevel type: string AgentInputSchema: description: |- One named input a saved agent declares. The id is client-minted at declaration time — the editor must be able to reference a fresh input before the first save — and is the identity the prompt's ``input_ref`` segments and a deployment's mapping key on. properties: id: format: uuid title: Id type: string name: maxLength: 100 minLength: 1 title: Name type: string required: - id - name title: AgentInputSchema type: object EnrichmentOutputFields-Input: description: |- The fields-mode output shape: repeatable named typed fields. The default is Clay's: one text field named ``response``. properties: fields: items: $ref: '#/components/schemas/EnrichmentOutputField' maxItems: 20 minItems: 1 title: Fields type: array mode: const: fields default: fields title: Mode type: string title: EnrichmentOutputFields type: object EnrichmentOutputRawSchema: description: >- The advanced output shape: a raw strict-mode JSON schema, verbatim. Held to every strict-mode constraint at SAVE time by the service layer — the shape is stored exactly as authored, and validation failures name the exact violated rule rather than surfacing at run time. properties: json_schema: additionalProperties: true description: A strict-mode JSON schema with an object root. title: Json Schema type: object mode: const: json_schema default: json_schema title: Mode type: string required: - json_schema title: EnrichmentOutputRawSchema type: object AgentPromptTemplate-Input: description: |- A saved agent's prompt: literal text with references between the runs. The same convention every other stored prompt in the product uses (``EnrichmentPromptTemplate`` for columns, template segments for sequences): identity-keyed segments, so renaming what a reference points at never rewrites the prompt. Structurally parallel to the enrichment template rather than importing it — an agent prompt references INPUTS, an enrichment prompt references COLUMNS, and the import hierarchy keeps the two domains apart. properties: segments: items: discriminator: mapping: context_ref: $ref: '#/components/schemas/AgentPromptContextRefSegment' input_ref: $ref: '#/components/schemas/AgentPromptInputRefSegment' text: $ref: '#/components/schemas/AgentPromptTextSegment' propertyName: type oneOf: - $ref: '#/components/schemas/AgentPromptTextSegment' - $ref: '#/components/schemas/AgentPromptInputRefSegment' - $ref: '#/components/schemas/AgentPromptContextRefSegment' maxItems: 200 title: Segments type: array title: AgentPromptTemplate type: object EnrichmentOutputFields-Output: description: |- The fields-mode output shape: repeatable named typed fields. The default is Clay's: one text field named ``response``. properties: fields: items: $ref: '#/components/schemas/EnrichmentOutputField' maxItems: 20 minItems: 1 title: Fields type: array mode: const: fields default: fields title: Mode type: string title: EnrichmentOutputFields type: object AgentPromptTemplate-Output: description: |- A saved agent's prompt: literal text with references between the runs. The same convention every other stored prompt in the product uses (``EnrichmentPromptTemplate`` for columns, template segments for sequences): identity-keyed segments, so renaming what a reference points at never rewrites the prompt. Structurally parallel to the enrichment template rather than importing it — an agent prompt references INPUTS, an enrichment prompt references COLUMNS, and the import hierarchy keeps the two domains apart. properties: segments: items: discriminator: mapping: context_ref: $ref: '#/components/schemas/AgentPromptContextRefSegment' input_ref: $ref: '#/components/schemas/AgentPromptInputRefSegment' text: $ref: '#/components/schemas/AgentPromptTextSegment' propertyName: type oneOf: - $ref: '#/components/schemas/AgentPromptTextSegment' - $ref: '#/components/schemas/AgentPromptInputRefSegment' - $ref: '#/components/schemas/AgentPromptContextRefSegment' maxItems: 200 title: Segments type: array title: AgentPromptTemplate type: object UnprocessableEntityIssue: properties: input: anyOf: - type: string - type: integer - type: number - type: boolean - items: {} type: array - additionalProperties: true type: object description: The user's input that caused the error. title: Input loc: description: >- The location of the error. Always contains 1 or 2 elements. The first element is the type of location (e.g. 'body', 'query', 'path', 'header'). The second element is the name of the field that caused the error. If the error is not related to a specific field, the second element is not present. items: type: string title: Loc type: array msg: description: The message of the error. The error message to show to the user. title: Msg type: string type: description: >- The type of the error. Useful for frontend code to handle different types of errors. title: Type type: string required: - loc - msg - type - input title: UnprocessableEntityIssue type: object EnrichmentOutputField: description: |- One named, typed value an enrichment run returns. Field types are drawn from the platform's existing column types, so any single field can later be promoted into an ordinary typed column without inventing a new type system. properties: choices: anyOf: - items: type: string maxItems: 200 type: array - type: 'null' description: The fixed option list for a select field; None elsewhere. title: Choices description: anyOf: - maxLength: 500 type: string - type: 'null' description: 'Shown to the model: what this field should contain.' title: Description field_type: $ref: '#/components/schemas/TableColumnType' default: text name: maxLength: 100 minLength: 1 title: Name type: string required: - name title: EnrichmentOutputField type: object AgentPromptContextRefSegment: description: A reference to the org's business context, resolved per org. properties: section: $ref: '#/components/schemas/AgentContextSection' default: all type: const: context_ref default: context_ref title: Type type: string title: AgentPromptContextRefSegment type: object AgentPromptInputRefSegment: description: A reference to one declared input, resolved per row at run time. properties: input_id: format: uuid title: Input Id type: string type: const: input_ref default: input_ref title: Type type: string required: - input_id title: AgentPromptInputRefSegment type: object AgentPromptTextSegment: description: A literal run of prompt text. properties: text: maxLength: 20000 title: Text type: string type: const: text default: text title: Type type: string required: - text title: AgentPromptTextSegment type: object TableColumnType: description: >- The type a user picks for a table column. Ordered as the picker shows them, which is also the order the groups below run in: text-ish, then numeric, then choice, then structured. enum: - text - paragraph - url - email - image_url - person - select - number - decimal - currency - checkbox - date - multi_select - list - object title: TableColumnType type: string AgentContextSection: description: |- One section of the org's AI business context a prompt can reference. ``ALL`` is the whole context document; the rest are its named sections. enum: - all - company - icp - personas - signals title: AgentContextSection type: string securitySchemes: HTTPBearer: scheme: bearer type: http APIKeyHeader: in: header name: X-API-Key type: apiKey ````