One agent, four channels, one inbox. The architecture diagram, the gotchas per channel, and what we ship for clients that need omnichannel without losing context.
Customers do not care which channel they messaged you on. They care that the agent knows what they said yesterday on a different channel. Most chat platforms still treat each channel as a separate inbox. Here is the architecture that unifies them without losing context. Why this is harder than it looks The most common failure mode with chat for omnichannel businesses is treating the problem as a model selection problem.
The hard parts are the data pipeline feeding it, the eval that catches regressions, and the human ownership layer that keeps the system honest after the implementer leaves the building. We have shipped this category of system enough times to recognize a few patterns. The teams that win allocate roughly 20 percent of project time to the model and prompts, 40 percent to data and integrations, 25 percent to evals and observability, and 15 percent to change management. The teams that lose flip those numbers, spend 70 percent on prompts, and end up with a great demo that nobody trusts. The good news is that none of this is novel engineering.
One agent, four channels, one inbox. The architecture diagram, the gotchas per channel, and what we ship for clients that need omnichannel without losing context. It is filed under AI Infrastructure because that is where operators looking for this problem actually start, and it is written from production work rather than from a content calendar.