Frontegg AgentLink Part 2: Agent IAM

You want to embrace agentic activity in your SaaS app, but right now, it feels too risky. Your hesitation is well-placed. You wouldn’t blanket-trust humans to do just anything in your product, let alone an AI agent. The truth is that without the right checks in place, even well-intentioned agent activity can go off the rails: placing multi-million dollar orders, deleting records en masse, or exposing sensitive customer data.

Frontegg AgentLink was built to make SaaS products secure for the AI era, so they can open up to agentic access and activity without fear. At the heart of that mission is Agent IAM: the part of AgentLink that extends identity and access management to AI agent interfaces. It gives you the roles, permissions, and guardrails to let agents interact with your product responsibly.

You’ve already learned about Agent Connector in Part 1.

Here in Part 2, we’ll take a look at the next layer of AgentLink: Agent IAM.

Guard against disaster: Agent-aware authorization

Agents (usually) do what they’re told, but sometimes what they’re told isn’t smart or safe. Most SaaS platforms aren’t set up to treat agent activity differently from regular traffic. Agent IAM adds a layer that changes that.

You can define what agents are allowed to do based on conditions that reflect heightened risk. Say you want to prevent large unintended transactions or block agents from deleting customer records. Agent IAM lets you create policies that catch these cases and stop them.

• name: "Customer Order Safeguards"
• customer_tier: "enterprise"
• policies:
• - resource: "orders"
• action: "create"
• condition: "total_amount > 100000"
• effect: "require_human_approval"
• - resource: "customer_data"
• action: "delete"
• effect: "deny"
• message: "AI agents cannot delete customer data"

These policies can be customized per customer and even offered as a self-service option.

Control access by role: RBAC for AI tools

AI agents shouldn’t get a blank check. Without role-based access, users who are supposed to have limited authority might end up taking unauthorized action through AI interfaces.

Agent IAM ensures that AI agents interacting with your product inherit the roles and permissions assigned to their human users. For example, if a marketing specialist connects through Claude, they get access to submit_expense and view_marketing_budget — but not approve_expense or view_all_salaries.

When a user tries to access a restricted tool, they get denied, the event is logged, and an admin is notified. You can view all attempts through the audit dashboard.

Step-up authentication for high-risk actions

Some agent actions warrant checking whether they were really intended by the human user. We offer the ability to configure step-up authentication for irreversible or sensitive operations, such as bulk deletes, permission changes, large transactions, data exports. When an agent crosses a threshold or triggers a risky pattern, the end user is asked to perform quick verification (e.g., OTP, WebAuthn).

Verification succeeds? Proceed with confidence. Fails or times out? The operation safely halts. You keep friction low for the everyday, and add security only where it counts.

Here’s an example of how step-up authentication works in practice:

1. Agent calls bulk_delete_records
2. User receives an email with a one-time code
3. Agent pauses until the human user confirms
4. Action resumes only after successful verification

You’ll also get a full audit trail and can configure different thresholds for different customers.

Human-in-the-Loop Approvals

For complex or high-value operations, Agent IAM lets you route approvals to real humans. Define approval workflows by customer, action, value, or volume — for example, single approver, multi-approver, or conditional routing. Approval requests are sent through your customers’ preferred channels (e.g., Slack, Teams, email) with full context approvals that can be granted, denied, or sent back for clarification. You can also set escalation paths and SLAs.

Here’s an example of setting human-in-the-loop rules:

• approval_rules:
• - customer: "EnterpriseCorp"
• channel: "teams"
• rules:
• - action: "bulk_delete"
• condition: "count > 100"
• approvers: ["data-governance"]

Dynamic masking for sensitive data

Sensitive data should never be an accident waiting to happen. Agent IAM lets you enforce different masking rules depending on the tool, the user’s role, or the data type.

For example, an agent pulling customer records for support purposes might get masked phone numbers and redacted medical history. A different tool for finance might receive unmasked billing data where necessary.

You can configure exactly how each field is handled. If needed, you can fully block access to fields that should never reach AI interfaces.

Why build it yourself when we already have?

Building a full identity and access management (IAM) system for agentic interfaces would take months, and then you’d need to maintain it. This is no small feat if this isn’t your core competency. You’d need to replicate policy engines, role mapping, data protection, step-up auth, approval routing, and observability. Frontegg AgentLink applies hardened IAM principles to AI, giving everything you need right out of the box.

You keep your backend. You keep your identity provider. We bring the enterprise-grade, low-code layer that allows you to open your product to agentic access — responsibly.

Want to learn more about how Agent IAM works inside Frontegg AgentLink?

• Learn more about Agent IAM
• Explore the full Frontegg AgentLink platform
• Explore the docs
• Read Part 3: Agent Analytics