One of the most consequential decisions in enterprise AI agent deployment is also one of the least discussed: should your agent act as the user, or as a service?

LangChain’s Harrison Chase formalized this question in a March 23 post on “In the Loop,” LangChain’s developer newsletter, introducing the two-tier authorization model now available in LangSmith Fleet. The framework is called Assistants vs. Claws, and it directly addresses a security gap that enterprise teams have been quietly dealing with for months.

The Core Distinction

Assistants — Act As the User

An Assistant uses the end-user’s credentials when taking actions. If Alice asks an Assistant to send an email, the email comes from Alice’s account. If Bob asks it to read a file, it reads only files Bob has permission to access.

How it works: The Assistant receives the user’s OAuth token (or equivalent) at runtime and uses it for all downstream API calls.

When to use it:

  • Personal productivity agents (email management, calendar scheduling, document summarization)
  • Scenarios where the agent’s actions should be auditable to a specific person
  • Cases where you want access to naturally be limited to what the user can already do
  • Compliance environments where action attribution matters

The tradeoff: The agent is constrained to what the user can do. If you need the agent to take actions across multiple users’ data, or to access systems the triggering user doesn’t have permission to, an Assistant model breaks down.

Claws — Act As a Service Account

A Claw uses fixed system credentials — a dedicated service account with its own permissions, independent of which user triggered the action.

How it works: The Claw is authenticated with a service account credential at deployment time. All actions happen under that service account’s identity, regardless of who invoked the agent.

When to use it:

  • Background automation that runs without a logged-in user (batch jobs, scheduled pipelines, event-driven workflows)
  • Cross-user operations (an agent that monitors multiple users’ activity and aggregates results)
  • System administration tasks that require elevated permissions beyond what any individual user has
  • Integration scenarios where the agent needs consistent identity across sessions

The tradeoff: A Claw’s service account can accumulate significant permissions. A compromised or misbehaving Claw can cause more damage than a compromised Assistant. Governance and scope-limiting on the service account becomes critical.

Configuring Authorization in LangSmith Fleet

LangSmith Fleet exposes the authorization model as a first-class configuration choice when deploying an agent. Here’s the basic structure:

from langsmith.fleet import AgentDeployment, AuthorizationModel

# Assistant — uses end-user credentials
deployment = AgentDeployment(
    name="email-assistant",
    agent=my_email_agent,
    authorization=AuthorizationModel.ASSISTANT,
    # end-user token is passed at invocation time
)

# Claw — uses fixed service account
deployment = AgentDeployment(
    name="data-pipeline-claw",
    agent=my_pipeline_agent,
    authorization=AuthorizationModel.CLAW,
    service_account_id="svc-pipeline-prod",
    # credentials managed in LangSmith Fleet secrets
)

For the Claw model, you configure the service account credentials through LangSmith Fleet’s secrets management interface — credentials are never stored in code or passed at runtime.

Practical Decision Framework

Use this checklist to pick the right model:

Question If Yes → If No →
Does the agent need to access user-specific data? Assistant Either
Should actions be attributed to the invoking user? Assistant Claw
Does the agent run on a schedule without a user? Claw Assistant
Does the agent need permissions the user doesn’t have? Claw Assistant
Are you in a regulated environment with strict audit trails? Assistant (usually) Discuss with compliance

Security Considerations

For Assistants: OAuth token scopes matter. Make sure the tokens your Assistants receive are scoped to the minimum permissions needed. Don’t pass full-access tokens when read-only is sufficient.

For Claws: Practice least-privilege on service accounts. Create dedicated service accounts for each Claw deployment — don’t reuse a single high-permission service account across multiple agents. Rotate credentials regularly. Monitor for anomalous behavior at the service account level.

For both: LangSmith Fleet’s observability tooling can log agent actions. Enable action logging in production — it’s your forensic record if something goes wrong.

The Bigger Picture

The Assistants vs. Claws distinction seems simple, but it’s one of the most important architectural decisions in enterprise agent deployment. Getting it wrong creates real security exposure — either from an Assistant that users can leverage to access data beyond their normal scope (via prompt injection or confused deputy attacks), or from a Claw with excessive permissions that becomes a high-value target.

LangChain formalizing this model in LangSmith Fleet is a meaningful step toward making enterprise agent security a first-class concern rather than an afterthought. If you’re deploying agents with access to business systems, this framework should inform how you design your authorization architecture.

Sources

  1. LangChain Blog — Two Different Types of Agent Authorization
  2. Geeky Gadgets — LangSmith Fleet Authorization Coverage

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260324-0800

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