Introducing role-based access control (RBAC) for AI agents

Why does AI agent RBAC matter?

As AI agents move beyond experimentation into real customer interactions, more teams get involved—product, support, engineering, operations—and each brings their own needs and risks. 

For example, without AI role-based access control (AI RBAC), anyone can edit your AI agent’s knowledge base or agentic workflow (AI SOP), potentially leading to inconsistent behavior, off-brand experiences, or compliance violations.

AI RBAC provides a structured way to manage access so the right people can do their part, and nothing more. By allowing you to scale AI agents without the chaos, bottlenecks, or finger-pointing, RBAC helps teams collaborate effectively to maximize the quality, safety, and accountability of AI systems.

The challenge: Agentic AI governance in enterprise operations

AI agents make decisions autonomously, touch sensitive data, and serve as the frontline for a growing number of enterprise functions—from customer service to fraud detection to employee support.

Effectively governing these agentic capabilities requires collaboration across multiple teams, from design to training to deployment. As a result, agents often evolve rapidly through continuous iteration.

Without a scalable permissioning structure for AI agents, enterprises face not just chaos but real risk from:

• Unauthorized access to sensitive AI agent logic, AI model parameters, and training content

• Untracked changes to agent knowledge and tools

• Risky AI deployments that bypass review

• Inefficient workflows and bottlenecks

• Confusion across teams over who owns what

AI governance is often overlooked by fast-moving teams. However, access controls are essential to turning agentic AI projects into successful, sustainable AI customer service operations. For example:

• Regional support teams are assigned to specific agents but can’t access others

• Product teams have full access to development environments, but only team leads can promote changes to production

• Ops teams can review flagged messages for hallucinations and compliance, but can’t edit AI SOPs (actionbooks)

• Compliance teams get read-only access to AI agent performance metrics and outputs

To scale AI operations without sacrificing oversight and control of AI agents, enterprises need a scalable way to manage role-based access for everyone involved at a targeted level.

Powering safe and scalable AI collaboration with AI RBAC

Sendbird launched role-based access controls and permissions for AI support agents. This way, multiple teams can work in parallel without confusion, bottlenecks, and avoidable risks.

From inside the AI agent dashboard, you can now define exactly who can access what to support effective AI governance, down to specific agents, features, and functions.

This allows teams to:

• Assign custom roles for different team types (e.g. support lead vs admin)

• Grant specific permission sets across your digital workforce

• Safely delegate work while preserving platform, model, and data integrity

• Restrict access to specific features (knowledge sources, AI SOPs, or testing centers)

• Separate development and production environments

• Grant access by region or use case

Example: A permission set for a “Compliance Reviewer” might allow review of flagged messages and read-only access to AI agent conversations, but block access to editing tools and the deployment environment.

Designed to support multi-team AI operations, AI agent role-based access control makes it fast and easy for team leads and stakeholders to manage access at a detailed level. When everyone works efficiently within clearly defined guardrails, you never have to choose between speed and safety as you scale AI operations.

How it works: Define access in one of two ways

Sendbird's AI agent RBAC separates access into two different layers:

Each member is assigned a role that defines their level of access to Sendbird AI agents and other products (like Sendbird Desk). For more exact control over AI agent features, you can create a permission set that manages access to specific agents and their specific functionalities.

Permissions are the specific tools a person can use. A role is the blueprint that says which tools are needed for which job. Assign someone the role, and you’ve automatically handed them the right tools for the tasks they’re responsible for.

Here’s an example of a permission set and the custom roles that can be associated with it:

Permission set:

• View data on AI agent performance

• Edit knowledge base

• Deploy actionbook workflows (AI SOPs)

• Access the AI agent test center

Role:

• AI Admin → all permissions

• AI QA analyst → view data and test only

• AI support team lead → View performance data, edit knowledge base, deploy AI SOPs, access test center

How do you set up roles and permissions in the AI agent dashboard?

We recommend you start by creating custom permission sets, and then create roles based on those sets. This way, permission sets are modular, reusable, and easy to manage at scale, and can be assigned to certain roles, which align with your broader organizational designations.

Step 1: Create a permission set

Inside the AI agent dashboard, navigate to the Organization Settings > Roles > Permission Set for AI Agent, then click Create Permission Set +. From here, you can define what specific actions users can take in the AI agent environment.

To create a new permission set, fill fill out the following fields, then click Save:

• Name: Provide a unique title for the permission set.

• Description: Write a short sentence explaining what this permission set permits.

• Permissions: Select functionalities accessible with this permission set

• AI agent access: Select the AI agent(s) that can be managed with this set

By creating a custom permission set, you customize which members can do what under what conditions. Next, you’ll create one or more roles that use that permission set.

Step 2: Create a role, and assign a permission set to it

Once you've created a permission set for AI agents, you’ll create a new role to assign the set to. In the dashboard, go to Organization Settings > Roles > Create role+. 

To create a new role, fill out the following fields, then hit Save:

• Name: Provide a unique title for the role.

• Description: Write a short sentence explaining what this role permits.

• Permissions: Select products and their functionalities accessible in this role.

• AI agent permission set: Select permission sets granted to a member with this role.

With both roles and permissions defined, you have a tailored access model that reflects your operational structure. You can now assign a custom role to any dashboard member, making it easy to give everyone the exact right access and permissions they need to collaborate safely and effectively.

Ready to strengthen AI governance and collaboration with AI RBAC?

With role-based access control, AI leaders can define precise access for each contributor, ensuring everyone has exactly the access they need, and nothing they don’t.

Role-based access control gives teams the freedom to move quickly with clear guardrails. Support, product, QA, engineering, and compliance teams can all collaborate without stepping on each other, while AI agents stay accurate, secure, and on-brand.

This is effective AI governance in action. AI agent RBAC is now live in the Sendbird AI agent platform, and part of our AI Trust OS—a robust suite of trust features for ensuring AI control, AI transparency, and AI responsibility that includes:

• AI agent activity logs

• Hallucination detection

• Real-time AI agent monitoring and safeguarding
  and more.

👉 Contact our AI sales team or your CSM to learn more.