# Agentspace > - Skill: `frank-luongt/agentspace` (Agent Skill) - Install (CLI): `npx skillmds@latest add frank-luongt/agentspace` - Raw SKILL.md: https://api.skillmd.com/api/skills/frank-luongt/agentspace/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: frank-luongt (https://skillmd.com/u/frank-luongt) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/frank-luongt/agentspace --- --- name: agentspace description: Google Agentspace patterns for enterprise AI search and agents. Use when building unified search across 100+ enterprise connectors (SharePoint, Confluence, Jira, Salesforce, ServiceNow), identity-aware access, or custom Agentspace agents. tags: [gcp, agentspace, enterprise-search, connectors] --- # Google Agentspace Build enterprise AI search and agent experiences across 100+ third-party data sources using Google Agentspace. ## When to Use - Building unified enterprise search across SharePoint, Confluence, Jira, Salesforce, ServiceNow, and more - Creating custom AI agents within Agentspace for specific business domains (HR, IT, Finance) - Implementing identity-aware search that respects source system ACLs - Deploying NotebookLM Enterprise for document analysis at scale ## Pre-Built Enterprise Connectors (100+) | Category | Connectors | |---|---| | **Microsoft** | SharePoint Online, OneDrive, Outlook/Exchange, Teams, Dynamics 365 | | **Atlassian** | Confluence, Jira | | **CRM** | Salesforce (Knowledge, Cases, custom objects) | | **ITSM** | ServiceNow (Incidents, Changes, Knowledge) | | **Google** | Workspace (Drive, Gmail, Calendar, Sites, Groups) | | **Dev Tools** | GitHub, GitLab | | **Storage** | Box, Dropbox, Cloud Storage, Amazon S3, Azure Blob | | **Databases** | BigQuery, Cloud SQL, AlloyDB, Spanner | | **Collaboration** | Slack, Notion | | **Custom** | REST API connector, JDBC connector, web crawlers | ## Patterns ### 1. Create Data Store with Connector ```python from google.cloud import discoveryengine_v1 as discoveryengine def create_confluence_data_store( project_id: str, location: str, data_store_id: str, confluence_url: str, space_keys: list[str], ): """Create a data store connected to Confluence.""" client = discoveryengine.DataStoreServiceClient() data_store = discoveryengine.DataStore( display_name="Confluence Knowledge Base", industry_vertical=discoveryengine.IndustryVertical.GENERIC, solution_types=[discoveryengine.SolutionType.SOLUTION_TYPE_SEARCH], content_config=discoveryengine.DataStore.ContentConfig.CONTENT_REQUIRED, ) operation = client.create_data_store( parent=f"projects/{project_id}/locations/{location}/collections/default_collection", data_store=data_store, data_store_id=data_store_id, ) return operation.result() ``` ### 2. Search Across Enterprise Sources ```python from google.cloud import discoveryengine_v1 as discoveryengine def search_enterprise(project_id: str, location: str, engine_id: str, query: str) -> list[dict]: """Search across all connected enterprise sources.""" client = discoveryengine.SearchServiceClient() request = discoveryengine.SearchRequest( serving_config=f"projects/{project_id}/locations/{location}/collections/default_collection/engines/{engine_id}/servingConfigs/default_search", query=query, page_size=10, content_search_spec=discoveryengine.SearchRequest.ContentSearchSpec( snippet_spec=discoveryengine.SearchRequest.ContentSearchSpec.SnippetSpec( return_snippet=True, ), summary_spec=discoveryengine.SearchRequest.ContentSearchSpec.SummarySpec( summary_result_count=5, include_citations=True, model_spec=discoveryengine.SearchRequest.ContentSearchSpec.SummarySpec.ModelSpec( version="gemini-1.5-flash-002/answer_gen/v2", ), ), ), ) response = client.search(request) results = [] for result in response.results: doc = result.document results.append({ "title": doc.derived_struct_data.get("title", ""), "snippet": doc.derived_struct_data.get("snippets", [{}])[0].get("snippet", ""), "link": doc.derived_struct_data.get("link", ""), "source": doc.derived_struct_data.get("source_type", ""), }) return results ``` ### 3. Custom Agentspace Agent Agentspace allows creating domain-specific agents that combine search + actions: ``` IT Help Desk Agent: Data Sources: ServiceNow KB, Confluence IT docs, Jira known issues Actions: Create ServiceNow ticket, Escalate to on-call Identity: Uses Google Workspace SSO, respects ServiceNow ACLs HR Benefits Agent: Data Sources: SharePoint HR policies, Workday benefits docs Actions: Open HR case, Schedule benefits consultation Identity: Uses Google Workspace SSO, scoped to HR data only ``` ### 4. Identity-Aware Access Control Agentspace automatically respects source system permissions: - **Google Workspace**: Uses Google Workspace ACLs (Drive sharing, Gmail access) - **SharePoint**: Maps Azure AD groups to search results filtering - **Confluence**: Respects Confluence space and page permissions - **Salesforce**: Uses Salesforce profile and sharing rules No additional configuration needed -- the connector framework handles ACL passthrough. ## Key Capabilities - **NotebookLM Enterprise**: Embedded document analysis with Audio Overviews - **Multimodal Search**: Text, image, and document search across all connected sources - **Actions**: Agents can create Jira tickets, update ServiceNow records, send Slack messages - **Grounding**: All responses cite source documents with links - **Admin Console**: Central management of connectors, agents, and access policies ## Anti-Patterns - Connecting all data sources without access review -- audit ACLs before enabling connectors - Using Agentspace for real-time transactional data -- it's optimized for search, not OLTP - Skipping connector sync schedule configuration -- stale data leads to poor answers - Not configuring data exclusions -- exclude sensitive folders/spaces from indexing ## References - [Google Agentspace](https://cloud.google.com/agentspace) - [Agentspace Connectors](https://cloud.google.com/agentspace/docs/connect-data-sources) - [Vertex AI Search](https://cloud.google.com/generative-ai-app-builder/docs/introduction)