Something I've been thinking about recently: Let's say an AI agent makes a recommendation and that recommendation ends up influencing a real business decision. A few months later, someone wants to understand why that decision was made. In most cases, it's not that hard to find the model, the prompt, or the output. Teams are getting much better at tracking those things. What feels harder is understanding what happened in between. Who reviewed the recommendation? Who approved it? What information were they looking at when they decided to move forward with it? Maybe there was a conversation that wasn't documented. Maybe there was context that seemed obvious at the time but wasn't recorded anywhere. Maybe there were reasons for trusting the recommendation that never made it into a system. I keep coming back to this because the output is only one part of the story. The decision happens when a person looks at that output and decides what to do next. If that context disappears, it becomes much harder to understand how a decision was made, even when the AI history is still available. Curious how other teams are thinking about this.
IF you can get things to a point where AI is coding for you in an automated way, you have no control over the costs generated. There needs to be a governance layer betwee AI code generation and Human requirements. We believe it is BlastRadius, but we beleive it needs to exist more than anything. The only way to control AI growth, cost, drift, or hallucinations is to code surgically against an existing code base. Governance allows this to happen at Human pace.
