The hard part of building AI agents is testing them
The difficult part of building AI agents is testing them.
A consumer may be in early delinquency and just need a quick reminder. Another may be weeks past due and demanding a settlement.
Someone might be cooperative, another emotional, another hostile. Even something as simple as confirming an email can go ten different ways.
That’s what makes testing #AI agents so difficult. Traditional software can be unit‑tested because inputs and outputs are fixed.
But with AI agents, every conversation is multi‑turn, probabilistic and shaped by context like account status, lender policy and consumer mood.
In fact, 88% of AI pilot projects never make it to wide-scale production. Only 4 out of 33 proof-of-concepts are actually deployed.
At Prodigal, we treat simulation as the core of building AI agents.
Before an AI agent ever reaches a live consumer, it’s put through thousands of conversations that span from regulatory disclosures to payment negotiations.
The point is to make sure it performs in the real world of collections where accuracy, compliance, empathy and resolution all have to coexist.
AI agents don’t fail because they don't know enough. They fail when they aren’t tested against the context they’ll actually face.
Simulation is how we close that gap.
