Building customer context into AI agents properly is harder than it looks and most teams are skipping the hard part
I want to be honest about something I got wrong when we started building agent workflows for customer-facing use cases. I assumed the hard part was the agent logic, the prompting, the tool use, the output formatting. It isn't. The hard part is the data layer underneath it.
Specifically the identity resolution layer. When a customer interacts with an agent, the agent needs to know who that customer is across all the ways they've ever interacted with the company. The email they used to sign up. The device they're on now. The support ticket they submitted six months ago under a slightly different name. The purchase they made before they had an account. If those signals aren't unified into a single coherent profile before they hit the agent context, the agent is working with a fragment of the picture and making decisions accordingly.
What I underestimated was how much of the customer identity problem remains unsolved even in companies with mature CRM stacks. Fragmented identifiers are the norm, not the exception. The same person shows up differently depending on which channel they came through and without an identity resolution layer explicitly stitching those views together the agent inherits all that fragmentation.
The vendors that are worth looking at for this are the ones building on deterministic identity resolution rather than probabilistic inference. Deterministic resolution only asserts a match when there's a confirmed signal linkage, which means the customer profile the agent works from is built on confirmed observations rather than modeled guesses. The coverage is lower but the error profile is much cleaner for a use case where a wrong identity match gets surfaced directly to a customer.