Top 3 Mistakes to Avoid When Connecting Your Enterprise Tools & Data to an AI Agent

Connecting your enterprise tools and data to a large language model (LLM) and agentic AI unlocks powerful automation and productivity gains—but it also introduces new risks. As AI agents become more integrated into business workflows, they must operate with the same governance, access controls, and data protection standards as any other system. Otherwise, they risk becoming a new vector for data leaks, compliance violations, or misuse.

Here are the top three mistakes organizations should avoid when integrating AI agents into their enterprise stack.

1. Assuming the Agent Knows Who It’s Acting On Behalf Of

A common failure point is the breakdown of role-based access control (RBAC). Just because an agent has system-level permissions doesn’t mean every user interacting with the agent should have access to all those capabilities.

For example, an AI agent integrated with Salesforce might have the technical ability to update customer pricing, but it should only do so when a user with pricing privileges makes the request. Without enforcing user-level authorization checks, you risk allowing anyone—even interns or contractors—to trigger actions they’re not allowed to take.

Best practice: Agents should enforce RBAC at the user level, verifying whether the human behind each request is authorized before taking action. Think of the agent as a proxy, not an independent actor.

2. Creating Toxic Tool Combinations Without Guardrails

Many AI agents are granted access to multiple enterprise systems to operate efficiently—email, CRM, document repositories, and ticketing systems. But this convenience can backfire when sensitive data is unintentionally combined or shared across contexts.

Consider an agent that manages customer inquiries. If it pulls deal information from a CRM and automatically replies to inbound emails, what happens when someone asks about a competitor’s deal? Or worse, what if someone requests their login credentials and the agent mistakenly sends them an export of everyone’s?

These “toxic combinations” arise when agents are over-permissioned and under-supervised. Even if each system is secure in isolation, the agent’s ability to bridge them can create accidental data exposure.

Best practice: Implement strict data governance policies that limit what agents can access and when they can share it. Use fine-grained access control, and consider redaction or output filters to prevent sensitive information from being shared without explicit validation.

3. Forgetting the Human-in-the-Loop Principle

LLMs and AI agents are powerful, but they’re not perfect. Organizations that fully automate actions without human oversight—especially in sensitive workflows like finance, legal, or HR—are playing with fire.

Whether it's approving payments, sending compliance documents, or responding to customer disputes, agents should have workflows in place that allow human review for high-risk actions. Blind trust in the agent’s logic or training data can lead to brand-damaging mistakes.

Best practice: Design workflows that incorporate human review or approval for sensitive actions. Let the agent draft, recommend, or prepare the work—but don’t let it run wild.

Final Thoughts

Enterprise LLM and Agentic AI integrations can offer incredible ROI—but only if security, access control, and data governance are built into the foundation. Avoiding these three common mistakes can help ensure your AI agents are as safe, smart, and compliant as they are powerful.