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Fleet gives you the control layer for scaling agents across your organization: tiered permissions, credential management, human-in-the-loop oversight, and an audit trail for agent actions.

Permissions and sharing

Fleet provides granular control over every agent in two dimensions: who gets access and what they can do.
  • Who: Share with individual users or your entire workspace.
  • What: Three permission levels:
    • Clone — copy and customize the agent
    • Run — use without modifying
    • Edit — full access to change instructions, tools, and settings
You can layer these permissions. Give a core team edit access, share run-only with the broader organization, and revoke at any time. For setup instructions, see Change access to the agent.

Agent identity and credentials

Fleet offers two credential models that control how agents authenticate with external tools:
  • Fixed credentials (“Claws”): The agent uses a single set of credentials regardless of who runs it. Use for shared-resource agents like a team Slack bot where everyone interacts through the same account.
  • User credentials (“Assistants”): The agent acts on behalf of the individual user who invokes it. Each user authenticates with their own account via OAuth. Use for tools where users have different access levels, like a personal email assistant.
This is configurable per agent, so you can choose the right model for each use case. For setup instructions, see Agent identity.

Observability and audit trail

Agent actions in Fleet are captured in a structured LangSmith trace, including tool calls, decisions, and outputs. You can inspect, search, and export traces. Combined with agent identity and permissions, tracing tells you which agent acted, on whose behalf, with what credentials, and what it did at each step.

Human-in-the-loop oversight

Fleet provides a central inbox for reviewing agent actions across all your agents. You can configure agents to pause and request approval before taking specific actions, then review, approve, edit, or reject from one place. For setup instructions, see Human-in-the-loop.