1. Batch orientation vs. real-time context requirements.
Most iPaaS workflows run on schedules. Every hour, every night, every week — data gets extracted, transformed, and loaded somewhere else. The latency is acceptable when the consumer is a dashboard someone checks in the morning.
Agents don't check dashboards in the morning. They reason against the data they have at the moment they need it. A two-hour-old record isn't approximately right — it's a confident hallucination waiting to happen. Agents don't know when their data was last refreshed. They'll reason clearly from stale premises and deliver wrong answers with full grammatical confidence.
2. Rigid schema contracts vs. adaptive data consumption.
iPaaS enforces structure. Data arrives in a defined shape, transforms according to a defined mapping, and exits in another defined shape. Break the contract anywhere in the chain, and the integration fails.
Agents don't consume data according to fixed contracts. They interpret it. A change in field naming, a new optional attribute, a slightly different response structure — these are things an agent can reason through. But if a brittle iPaaS layer breaks before the data ever reaches the agent, the agent never gets the chance.
3. Latency stacking vs. decision loop requirements.
Agents work in tight reasoning loops. Perceive, think, act, observe, repeat — and the loop needs to be fast. Every integration hop adds latency. While small delays are barely noticeable in traditional applications, they severely impact an agent's reasoning loop, which is the difference between a functional workflow and a broken one. Furthermore, constant loops in reasoning might impact rate limiting, increase costs, and extend processing time. iPaaS wasn't designed for the low-latency context retrieval required at reasoning speed; it was designed for reliable eventual delivery. Those are different things.