Integration layer

The integration layer is the architectural layer of an AI automation system responsible for moving data between the agent and the customer's real systems — CRM, billing, support tools, data warehouse, comms platforms, etc.

It's one of the five layers of a real AI automation deployment (reasoning, knowledge, integration, orchestration, interface). Without a solid integration layer, agents can think but can't act on the real world.

Integration patterns in 2026:

• Native APIs (preferred). Direct integration with the destination system's API. Most reliable, highest performance, deepest capability. Examples: HubSpot API, Stripe API, Slack API, Salesforce REST API.
• MCP servers (rapidly emerging). Use Model Context Protocol where available. Standardizes integration across many tools.
• iPaaS (Zapier / Make / n8n). Pre-built connectors for the long tail of SaaS tools. Lower performance + higher cost at scale, but unbeatable for breadth.
• Webhooks + event streams. Real-time data flow from systems that support them (Stripe events, Shopify webhooks, etc.).
• Custom ETL. For data warehouse + reverse-ETL patterns.

The right mix depends on the customer's stack + the workflow. A typical Solidus engagement uses native APIs for the 3-5 core systems (CRM, support, billing, comms), MCP servers where available for adjacent tools, and n8n / Make as fallback for the long tail.