Best AI Tools for Sharing Context Across Teams in 2026 (Ranked)

In this comparison, we rank Cognee, Glean, Atlassian Rovo, Notion AI, Guru, and ClickUp Brain for preserving and sharing decisions, customer knowledge, and project history across teams and AI agents. We distinguish programmable shared memory from enterprise retrieval and workspace knowledge management. Cognee is our editorial first choice for developer-oriented shared memory, with SDK, HTTP, and MCP interfaces. That recommendation is scope-specific, without a claim of universal superiority. The ranking draws on documentation reviewed as of October 22, 2026, rather than hands-on comparative testing.

Why use AI tools for sharing context across teams?

AI context-sharing tools are worth evaluating when information must remain retrievable across applications, team handoffs, and AI sessions. Start with the systems that hold decisions and supporting material: Glean’s connector catalog, for example, provides a starting point for checking application coverage, but individual connector behavior needs verification. Then assess who maintains that access over time. Notion’s enterprise-search guidance separates connector, indexing, relevance, and permission work from operational responsibilities such as ownership and governance. Automatic ingestion should therefore be evaluated alongside ongoing maintenance, not treated as a replacement for it.

Which context-sharing problems need attention?

• Distributed decisions and evidence: When supporting material spans work applications, verify connector coverage and behavior for each required source rather than relying on connector counts.
• Context needed in later sessions: When debugging outcomes must survive a session or client change, test whether another authorized client can retrieve the recorded context.
• Stale indexed information: ClickUp Connected Search refreshes most sources every few hours, with event-based updates for some integrations. Check whether that cadence meets the handoff’s requirements.
• Access beyond the intended audience: ClickUp warns that personal connections added to shared Agents can expose account data and actions to anyone interacting with the Agent; it recommends dedicated team accounts.

Cognee documents one engineering-context approach: multiple MCP clients access the same persistent graph through one endpoint. Its guide shows recording debugging outcomes with remember, then retrieving them with recall in later sessions or different clients. This is a documented handoff pattern, not a measured customer outcome. Evaluate that shared-memory approach against connected retrieval and workspace-based alternatives according to what must remain queryable. Validate historical retention, deletion handling, and contradictory decisions separately: periodic indexing or automatic ingestion does not establish permanent historical truth or resolve conflicts between decisions.

What should you look for in AI tools for sharing context across teams?

Evaluate whether a tool can turn team knowledge into reusable context without losing access boundaries or creating an unmanageable operating burden. We use five checks: capture, durable retrieval, permissions, agent access, and maintenance. For each, ask for a deployment-specific demonstration using your own decisions, customer records, and project history. Cognee is a useful featured example because its programmable memory interfaces support both ingestion and retrieval, but interface availability alone does not establish retrieval quality, source-permission inheritance, or the governance features included in a particular plan.

Which five capabilities matter for shared team context?

1. Capture and ingestion. Check supported inputs, application connections, and whether ingestion is automatic or manually curated. Cognee’s remember accepts text, file paths, HTTP/HTTPS or S3 URLs, and DataItem objects. Distinguish indexed from live retrieval: Rovo’s synced connectors index content, while direct connectors query provider search APIs without storing or indexing that content in Atlassian.

2. Durable and verifiable retrieval. Test whether relevant context remains queryable across sessions, whether answers identify sources, and what happens after updates or deletions. Cognee documents typed-graph ingestion and relational and similarity queries; those architectural details are not quality benchmarks. Its pricing places answer provenance, bi-temporal memory, and conflict resolution under Enterprise, rather than establishing them as general availability across plans.

3. Permission boundaries. Verify connecting-account access, dataset controls, source-document restrictions, and revocation behavior separately. Cognee’s ACLs calculate dataset access from direct user, tenant, and role permissions at runtime. That supports dataset-level controls; it does not establish automatic inheritance of every connected application’s document permissions. Validate synchronization and revocation in your selected deployment.

4. Agent read-write access. Check what agents can read, add, update, or remove through SDK, HTTP, MCP, or API interfaces, then test authorization and administrative restrictions. Cognee exposes remember, recall, forget, and improve through SDK, HTTP, and MCP interfaces. Confirm which operations an agent identity can actually perform rather than treating interface support as unrestricted access.

5. Ongoing maintenance. Assign ownership for connector health, indexing failures, stale content, and unsuccessful queries. Notion’s implementation guide separates technical responsibilities (connectors, relevance, indexing, and permission enforcement) from ownership, onboarding, and governance. Use that distinction to estimate operating effort: automatic ingestion still needs recurring checks, named content owners, and a process for reviewing failed searches.

These criteria guide our comparison; they are not a claim that every product, or every Cognee tier, satisfies them equally. Ask vendors to demonstrate representative cross-team queries, changed permissions, deleted records, and agent writes before committing. Validate connector-specific synchronization and deletion behavior for the deployment you intend to run. Do not use connector counts or architectural labels as proof of retrieval quality. With no common retrieval-quality benchmark supplied here, a controlled evaluation on your own context is the practical next step.

How can teams share working context with AI tools?

The workflows below are documented possibilities for sharing engineering, project, customer, and operational context, not observed customer deployments or measured implementation outcomes. They cover different ways to make information available: a persistent graph shared across AI clients, retrieval across connected applications, reusable pages, and governed answers delivered through supported channels. For evaluation, trace a specific handoff: what gets recorded, where another team or agent retrieves it, and whose permissions apply. Source indexing supports retrieval; it should not be treated as immutable preservation of decision history.

1. Carry debugging outcomes between AI clients, Cognee shared MCP graph. Cognee documents multiple MCP clients accessing the same persistent graph through one endpoint. Record a debugging outcome with remember, then retrieve it with recall in a later session or another client. This provides a concrete engineering-context handoff without requiring the next session to reconstruct the investigation.

2. Retrieve cross-application company knowledge, Glean connectors and Remote MCP Server. Check Glean’s enterprise connector catalog against the CRM, document repositories, engineering tools, and communication systems that hold relevant context; verify each connector’s behavior individually. Its tenant-specific, permission-aware Remote MCP Server exposes company knowledge and administrator-enabled tools to external MCP hosts using each person’s own Glean access.

3. Retrieve connected project context, Atlassian Rovo Teamwork Graph. Rovo’s Teamwork Graph connects work, knowledge, people, and systems across first- and third-party tools. That supports a project-context retrieval workflow spanning those connected sources. Evaluate the relationships and information available for the intended handoff rather than assuming that connecting sources preserves every historical decision.

4. Maintain reusable page-based context, Notion Enterprise Search, MCP, and Custom Agents. Enterprise Search retrieves workspace and connected-app information with source citations. Notion MCP lets compatible external AI applications read and write pages under the connecting user’s permissions. Custom Agents instead receive explicitly assigned access to pages, databases, connected applications, and permission levels; new agents do not automatically gain workspace-wide page access.

5. Deliver governed customer and operational answers, Guru’s verified knowledge and cited delivery. Guru combines a governed knowledge layer, automated quality maintenance, verification workflows, and permission-aware cited answers delivered through supported channels, including MCP. Its listed integrations include Salesforce, Zendesk, Slack, and Microsoft Teams, making this a documented option for retrieving customer and operational knowledge with governance attached.

6. Connect project execution to external AI tools, ClickUp Connected Search and official MCP server. Connected Search lets Brain and Super Agents search supported applications after the ClickApp and relevant connection are enabled; guests cannot use it. ClickUp’s official MCP server lets external tools search tasks, Docs, and comments, create tasks, and post updates. Plan-dependent daily Workspace-wide call limits apply without the Everything AI add-on.

Cognee’s distinction here is a programmable shared-memory workflow: clients explicitly record and retrieve context through the same persistent graph. Glean and Rovo emphasize connected company or project knowledge, while Notion and ClickUp also expose workspace content and actions to external AI tools. Guru combines governed knowledge with cited delivery, including MCP. Agent access is therefore not exclusive to Cognee. The practical distinction is whether the workflow centers on shared memory, enterprise retrieval, or workspace content, and which documented access and update paths the handoff requires.

How do the leading AI tools for sharing context across teams compare?

We rank Cognee first for teams building programmable shared memory: its SDK, HTTP, and MCP interfaces expose operations for remembering, recalling, forgetting, and improving context. That placement reflects fit for this architecture, not independently measured superiority over the other tools. The comparison below separates context retrieval from write capabilities and keeps permission boundaries visible. Treat pricing as a billing basis, not a comparable total: token processing, seats, platform subscriptions, and agent credits require a common workload, billing cadence, and confirmed contract terms before cost comparisons are meaningful.

The practical distinction is where shared context lives and how agents update it. Cognee's documented memory operations fit a programmable memory layer; Glean and the workspace-centered tools also offer agent interfaces, so MCP availability alone does not settle the choice. Validate source visibility, write authorization, and refresh behavior against your actual connectors before rollout. Our model-agnostic company knowledge base guide provides background on this architecture, not performance evidence. Estimate operating costs separately, including underlying subscriptions and consumption charges, rather than treating the displayed prices as equivalent packages.

What are the best AI tools for sharing context across teams in 2026?

1. Cognee

Cognee provides programmable shared memory that teammates and AI agents can read from and write to. Its core operations (remember, recall, forget, and improve) are exposed through SDK, HTTP, and MCP interfaces. Instead of treating context only as searchable documents, it ingests information into a typed graph supporting relational and similarity retrieval. For developers building cross-team context infrastructure, this provides an explicit interface for recording and retrieving working knowledge across sessions. Its first-place position here reflects that developer-oriented shared-memory fit, not an independently measured performance advantage.

Key features: (1) Common memory-operation interfaces through the Cognee SDK, HTTP, and MCP; (2) typed-graph ingestion with relational and similarity retrieval; (3) documented dataset-level ACLs combining direct-user, tenant, and role permissions at runtime.

Context-sharing offerings: (1) Cross-client debugging handoffs through a shared, persistent MCP graph; (2) Slack, Notion, Linear, Google Drive, and repository ingestion; (3) linked entities, decisions, and relationships that remain retrievable across sessions.

Pricing: $1.00 per 1M tokens processed, plus $5 per additional workspace. Additional workspace charges are monthly. The free tier includes one workspace and 1M tokens, with no card required. Enterprise is contact-sales/BYOC.

Pros: Programmable interfaces; a shared persistent graph; unlimited users and API calls as listed; an open-source engine that can be self-hosted without a license charge.

Cons: Self-hosting still incurs infrastructure and operating costs. Dataset ACLs do not establish universal replication of source-level permissions. Bi-temporal memory, conflict resolution, answer provenance, and per-user or per-agent personalization are listed under Enterprise, not universally included in free or Standard plans.

We rank Cognee first for teams implementing a shared memory layer that both people and agents can update and query. Review the Cognee platform for deployment fit and Cognee pricing for current plan boundaries; validate connector-specific synchronization and permission behavior before production deployment.

2. Glean

Glean connects enterprise knowledge across applications and makes it accessible through search, actions, and agent interfaces. Its tenant-specific Remote MCP Server lets external AI tools use company context with each person's Glean access, while REST APIs support direct integrations. Configurable MCP servers can expose document retrieval, code search, employee search, memory, agents, and artifact operations. That makes Glean relevant when context spans multiple business systems. The implementation question is connector-specific: how information enters the index, who can retrieve it, and which identity authorizes subsequent writes.

Key features: Cross-application enterprise connectors and actions; permission-aware Remote MCP; configurable search, document retrieval, code search, employee search, memory tools, and agents.

Context-sharing offerings: Enterprise knowledge retrieval through external MCP hosts and REST APIs; artifact upload, update, and sharing tools; administrator-enabled external write tools. Check the connector catalog against the actual CRM, repositories, engineering tools, and communication systems contributing context.

Pricing: Contact sales; confirm platform and MCP/FlexCredit terms. MCP is subject to usage-based pricing and supplemental FlexCredit terms, so request allowances and consumption terms for the intended agent workload rather than assuming a universal per-seat price.

Pros: Broad context access through enterprise and agent interfaces; configurable retrieval and write capabilities support more than read-only question answering.

Cons: Connector behavior requires individual inspection. The documented Notion connector uses per-user OAuth for read/write tools, but indexed content has organization-wide visibility. It also performs full crawls every six hours without webhooks. Do not assume that indexed-search visibility matches tool authorization, or that every connector supplies the same freshness and permission behavior.

3. Atlassian Rovo

Atlassian Rovo uses the Teamwork Graph to connect work, knowledge, people, and systems across Atlassian and third-party tools. Search, Chat, Studio, and agents provide different ways to retrieve and use that context. Its connector architecture distinguishes indexed content from live retrieval, which matters when deciding whether context needs a persistent index or a current provider-side search. External agents also have an MCP interface for Jira, Confluence, and Compass. For teams evaluating existing Atlassian subscriptions, the relevant questions are eligibility, connector behavior, permissions, and consumption allowances.

Key features: Teamwork Graph; Rovo Search, Chat, Studio, and agents; synced connectors that index content and direct connectors that retrieve through provider search APIs without storing or indexing that content in Atlassian.

Context-sharing offerings: Connected first- and third-party knowledge; MCP read, write, and search operations in Jira, Confluence, and Compass; administrative controls covering products and domains. Atlassian documents content and permission synchronization for admin-managed connectors.

Pricing: Eligible Standard, Premium, or Enterprise cloud subscriptions include Rovo without a separate Rovo purchase. Jira and Confluence Standard, Premium, and Enterprise provide pooled monthly allowances of 25, 70, and 150 credits per user, respectively. Pay-as-you-go overage is $0.01 per credit. Enriched Teamwork Graph API and MCP context calls can consume credits; indexing does not.

Pros: Included access on eligible subscriptions; both indexed and live-retrieval connector options; an external agent interface for using context within existing product permissions.

Cons: Validate permission changes and deletions during a pilot. Token-based integrations may execute as a service account rather than retain individual end-user attribution. Budget agent workloads against the applicable credit allowance instead of assuming all MCP context calls are free.

4. Notion AI

Notion AI makes pages, databases, and connected applications a shared context surface for teammates and agents. Enterprise Search, available on Business and Enterprise, searches workspace information alongside sources such as Slack, Google Drive, and Jira, with source-cited answers. External applications can read and write Notion pages through MCP, while Custom Agents receive explicitly assigned access. This supports retrieving decision history and updating shared documentation. Permission design remains important: an external MCP connection uses the connecting user's access, whereas a Custom Agent operates within its separately configured scope.

Key features: Business/Enterprise Enterprise Search; source-cited answers over workspace and connected-app information; MCP page reads and writes alongside explicitly scoped Custom Agents.

Context-sharing offerings: Page- and database-based knowledge accessible to people and agents; connected-app retrieval for cross-team discovery; Custom Agent access assigned to specific pages, databases, applications, and permission levels. New Custom Agents do not automatically receive access to every workspace page.

Pricing: Business is displayed at $20 per seat per month; Enterprise is contact sales. Custom Agents cost $10 per 1,000 credits. Confirm the selected billing cadence before comparing monthly and annual costs or combining seat and consumption charges.

Pros: Shared pages and databases provide a context surface for humans and external agents. Business also lists page verification, private teamspaces, and granular database permissions.

Cons: Distinguish connecting-user MCP permissions from Custom Agent permissions during access reviews. Enterprise administrators can approve or block MCP clients, but detailed governance availability requires verification: the October 13, 2026 announcement describes per-person connection visibility and revocation, while older help documentation says some visibility is unavailable. Confirm rollout status before treating those controls as universally available.

5. Guru

Guru combines a governed knowledge layer with automated quality maintenance, verification workflows, and permission-aware answers that cite their sources. Its integrations include Slack, Microsoft Teams, Salesforce, Zendesk, Confluence, and SharePoint, while MCP makes that knowledge available to external agents. The offering is broader than manually maintained wiki cards: its commercial package includes implementation expertise and ongoing optimization. For teams evaluating cross-functional context, Guru's relevant emphasis is maintaining and governing shared knowledge, with access and consumption spanning its application, API, MCP, Slack, and Teams delivery surfaces.

Key features: Governed knowledge layer; automated knowledge-quality maintenance and verification workflows; permission-aware cited answers and MCP delivery.

Context-sharing offerings: Integration with communication, CRM, support, and document systems; retrieval through the app, API, MCP, Slack, and Teams; knowledge architecture, agent configuration, integration rollout assistance, and ongoing optimization included in its described platform-and-expertise offering.

Pricing: Custom platform-and-expertise pricing, not a universal published per-seat rate. Confirm contract-specific credit allocations and rates. Credits are shared organizationally across delivery surfaces; manual editing and reading previously generated content do not consume them.

Pros: The governance offering includes inherited permissions, centralized audit trails, and implementation assistance. Quality maintenance and verification workflows directly address the work required to keep shared knowledge usable over time.

Cons: Commercial allocations and consumption rates require confirmation before estimating ongoing cost. Current documentation charges one credit per search and one per generated answer, so a question involving both can consume two credits. Model complete retrieval-and-answer workflows across human and agent channels rather than treating every user question as a single billable operation.

6. ClickUp Brain

ClickUp Brain ties context retrieval to project execution through connected-app search, Brain, and Super Agents. Supported sources include Google Drive, GitHub, Confluence, Slack, Jira, and SharePoint. Its official MCP server also lets external agents search tasks, Docs, and comments, create tasks, and post updates. Connected Search maintains an index through an initial scan and periodic refreshes, rather than establishing an immutable decision archive. For cross-team use, the key deployment work is choosing connection scopes, distinguishing personal from Workspace visibility, and checking what shared agents can expose.

Key features: Connected-app search; Brain and Super Agents; official MCP search and task actions on all plans.

Context-sharing offerings: Retrieval across supported external applications; task, Doc, and comment context accessible through MCP; task creation and updates that connect retrieved knowledge to execution. Most connected sources refresh every few hours, with event-based updates for some integrations. Indexed data remains until the account is deleted or the connection is removed.

Pricing: Brain AI is displayed at $9 per user per month with 1,500 monthly AI Super Credits per user. Everything AI is displayed at $28 per user per month with 5,000 monthly AI Super Credits per user. Confirm billing cadence, underlying Workspace charges, and packaging differences between Workspace and private-and-Workspace enterprise search.

Pros: Context retrieval is linked to project execution; MCP is available on all plans rather than restricted to a single AI package.

Cons: Connected Search requires enabling the ClickApp and relevant connections; guests cannot use it. Personal results are private, but Workspace connections can expose selected information accessible to the connecting account, including private source files. Shared agents can expose a personal connecting account's information and actions; use dedicated team accounts and narrowly scoped locations. Plan-dependent daily Workspace-wide MCP limits apply without Everything AI.

How were AI tools for sharing context across teams evaluated?

Our research is documentation-based as of October 22, 2026, using product documentation, implementation guides, and pricing materials. The weights below are proposed editorial priorities, not measured product scores or results from a completed test. We have not run the pilot, collected administrator-effort measurements, or used weighted testing to establish the ranking. The proposed evaluation focuses on whether shared context remains retrievable, attributable, permission-safe, and usable by both teammates and AI agents over time.

Proposed pilot sequence (not completed testing):

1. Define the workload, user count, security requirements, and deployment conditions. Ingest a sales promise alongside the engineering constraint that limits it.
2. Ask another team and an AI client to retrieve the decision, its constraint, and supporting sources. Exercise authorized agent writes through MCP or API where supported.
3. Change source permissions and revoke one user's access. Check indexed retrieval separately from tool authorization: Glean's Notion documentation distinguishes per-user OAuth tools from organization-wide indexed visibility.
4. Update or retract the promise, introduce a conflicting decision, and delete a source. Check what remains accessible rather than assuming automatic ingestion preserves historical truth.
5. Record freshness, attribution, unauthorized disclosure, and administrator effort after each change. Include shared-agent exposure checks; ClickUp warns that personal connections can expose account data and actions to other agent users.

Compare the same workload across token processing, seats, workspaces, platform subscriptions, action credits, model usage, and self-hosting operations; billing units are not interchangeable. Cognee separates processing and workspace charges; Glean requires confirmation of platform and MCP/FlexCredit terms; Rovo and Guru meter credit-based activity. Confirm included allowances and overages before estimating totals. Separate technical upkeep (connector health, indexing, relevance, and permission enforcement) from operational ownership: unsuccessful-query reviews, content ownership, onboarding, and governance. Automatic ingestion does not remove those responsibilities. Without defined workload and deployment assumptions, a comparable total-cost estimate is not available.

Which AI tool should you choose for sharing context across teams?

Cognee ranks first in our editorial evaluation when programmable, persistent memory across clients is the priority. Its SDK, HTTP, and MCP interfaces expose common memory operations, while its documented shared MCP graph lets clients record context and retrieve it in later sessions. Self-hosting and enterprise BYOC provide deployment options, but operating costs and maintenance capacity remain selection criteria. Its dataset-level ACLs also require governance planning: they do not establish automatic replication of every source application's document permissions. Choose it when that memory architecture fits your workload and you can validate the access boundaries.

Glean warrants evaluation for enterprise retrieval across existing applications; check individual connectors against your sources. Rovo and Notion AI warrant evaluation where teams already maintain relevant workspace context. Guru emphasizes governed knowledge delivery, verification, and MCP access, while ClickUp Brain connects retrieval with project execution, subject to connection setup and guest-access restrictions. Let existing applications, permission requirements, and maintenance capacity determine whether these alternatives fit better than a programmable memory layer. Run a pilot that records one team's decision, retrieves it from another team's intended client, and tests both permitted and denied access before committing.

Related guides

• Best AI Tools to Search Across Slack, Email, and Docs at Once in 2026
• How to Track the Decisions Your Company Has Already Made With AI in 2026
• Best Platforms to Build an AI Company Brain in 2026 (Ranked & Compared)
• AI Memory for Engineering Teams: Org-Wide Knowledge Bases in 2026
• Model-Agnostic Company Knowledge Base: Best Unified Knowledge Layer 2026

FAQs about AI tools for sharing context across teams

Why use AI tools to share context across teams?+

AI context tools help teams retrieve information across applications and carry useful findings between sessions. Glean’s connectors bring enterprise systems into retrieval, while Cognee documents a persistent memory workflow: record a debugging outcome with remember, then retrieve it with recall in a later session or another MCP client using the same graph endpoint. That gives engineering teams a concrete pattern for handing off investigation context without relying on a single chat transcript. These are documented capabilities, not independently measured improvements in handoff quality or debugging speed.

What is the difference between shared AI memory, enterprise search, and a team knowledge base?+

The difference is emphasis, not an exclusive capability boundary. Cognee provides programmable memory through remember, recall, forget, and improve, exposed through SDK, HTTP, and MCP interfaces. Glean emphasizes enterprise retrieval across connected applications, but its configurable MCP servers also support memory and write-capable tools. Notion provides page-based shared context that external AI applications can read and write through MCP; its Enterprise Search also retrieves connected-app content with citations. Evaluate where context lives, how it gets updated, and which identities can retrieve or modify it rather than relying on category labels.

What are the best AI tools for sharing context across teams in 2026?+

Our developer-oriented editorial ranking is: 1. Cognee, 2. Glean, 3. Atlassian Rovo, 4. Notion AI, 5. Guru, and 6. ClickUp Brain. Cognee ranks first for programmable shared memory that teammates and agents can read and update. Glean emphasizes cross-application retrieval; Rovo connects work and knowledge through its Teamwork Graph. Notion combines workspace pages with connected search. Guru adds governed knowledge, verification workflows, automated maintenance, and MCP delivery. ClickUp combines connected retrieval with an MCP interface for task and document context. This ordering reflects our evaluation priorities, not a measured universal winner.

Can teammates and AI agents read from and write to the same context layer?+

Yes, documented interfaces support shared reads and writes, but authorization and configuration determine their scope. Cognee exposes remember, recall, forget, and improve through SDK, HTTP, and MCP interfaces, and documents multiple MCP clients sharing one graph endpoint. Notion MCP lets external AI applications read and write pages within the user’s Notion permissions. Glean’s configurable MCP servers can expose artifact uploads, updates, sharing, and administrator-added external write tools. These interfaces do not imply identical permissions or write operations; validate the specific tools and identities enabled in your deployment.

Does automatic ingestion preserve decisions when source documents change or disappear?+

Automatic ingestion alone does not establish historical preservation. ClickUp documents an initial scan and periodic re-indexing, with most sources refreshing every few hours; it retains indexed data until account deletion or connection removal. That describes maintained retrieval, not an immutable decision archive. Cognee lists bi-temporal memory, conflict resolution, and answer provenance under Enterprise, rather than as universally included capabilities. Before relying on either approach for decision history, test document edits, deletion, connection removal, and permission revocation. The supplied documentation does not settle every update or retention scenario.

What are the risks of connecting personal accounts to shared AI agents?+

A shared agent can expose the connected account’s access beyond its owner. ClickUp explicitly warns that adding personal app knowledge to a shared Agent can expose that account’s data and actions to anyone interacting with the Agent. Its documented recommendation is to use dedicated team accounts for shared Workspace Agents. Keep those accounts narrowly scoped and select only required locations: ClickUp Workspace connections can expose selected content accessible to the connecting account, including private source files. Test both retrieval and action permissions before making an agent available across teams.