I built a lead pipeline. When a new inquiry comes in, AI reads it to understand what they need, whether they've tried to solve the problem before, and how closely the request matches the services being offered. It coordinates the appropriate response automatically.
If a prospective client asked me to build something similar for them and wanted to understand how my own system makes its decisions, I could explain the criteria. Budget, prior attempts, service fit. But I'd be reconstructing that from design specifications, not pulling it from a log. The thinking that produced the score existed for a moment during execution and then disappeared. The transaction landed in the database. The reasoning behind it didn't.
We log every database transaction. We log API calls, errors, response times.
Now scale that. An agent processes a thousand transactions. A client disputes one. Legal wants to understand the basis for a decision. Compliance needs an audit trail. Are we providing tooling to access the reasoning as part of that audit? For most of us the honest answer is not yet.
I believe in eating my own dog food. I know what's getting added to my pipeline next.
How many of us are logging the part that actually made the decision?
Written by Duane Grey
AI Strategy & Implementation
Independent AI consultant helping companies cut through hype and deploy systems that produce real results.