Systems integration

What it includes

• Connect the agent to systems of record (CRM, ERP, ticketing, email) with scoped permissions.
• Design the data flow: what it reads, what it writes, and how often.
• Manage authentication, credentials and access limits per system.
• Handle integration errors: retries, queues, and what happens when a system is down.

When you need it

• The agent works on test data but can’t touch the real systems.
• People copy-paste between the AI and the company’s tools by hand.
• Every integration is hand-built and breaks with each change on the other end.

How it’s measured

• Coverage: share of key systems connected vs. manual work remaining.
• Integration reliability (successful call rate).
• Recovery time when a connected system fails.

Common mistakes

• Giving the agent admin credentials instead of least-privilege access per task.
• Fragile integrations with no retries or queues: one blip takes down the whole process.
• Pushing sensitive data through the integration without controlling where it ends up.

FAQ

Does integration come before or after building the agent?

In parallel. An agent with no integration doesn’t operate, and an integration with no agent does nothing. The key is scoping permissions from the design, not adding security at the end.

Do you need a data warehouse to integrate AI?

Not always. Many processes integrate directly with the systems of record. A warehouse helps when the agent needs historical data or several sources at once.

Related capabilities