iPaaS Was Built for Humans—AI May Need Something Different

As AI agents become more common in enterprise systems, the passage argues that traditional iPaaS platforms remain valuable for deterministic, predefined integrations but may be insufficient for agents that need to explore and synthesize data across changing sources. It proposes “universal connectivity” as a complementary architecture that provides governed, live relational access to data through standardized interfaces such as MCP and SQL, allowing agents to discover schemas, use shared business semantics, query across systems, and perform reliable bidirectional write-backs. Unlike curated iPaaS endpoints and workflows, this model aims to let agents investigate unanticipated questions while maintaining platform-level controls such as permissions, rate limits, and audit trails. The passage concludes that organizations will likely need a mix of both approaches, using iPaaS for repeatable automation and universal connectivity for AI workloads requiring broad-context reasoning, flexible data exploration, and deterministic execution.

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