Where verification debt accumulates
Three categories of operational cost that arrive after the AI starts working. Each is invisible at procurement and paid in operations.
Debt 1
Verification debt
What it is
Outputs your team cannot trust by default. Every result needs human checking.
Where it shows up
Senior staff time consumed by review. Untracked rework cycles. "Second pair of eyes" requirements that were not in the deployment plan.
When it surfaces
Months 3 to 6 after deployment, once volume scales.
Debt 2
Comprehension debt
What it is
Code, configs, and content the AI generated that nobody on the team designed.
Where it shows up
Incidents debugged by engineers reading the code for the first time. Change requests that take days because the structure is opaque.
When it surfaces
First incident or first change request. Compounds over time.
Debt 3
Operational debt
What it is
Silent failures, runaway token bills, model drift. The observability you would build for any other production system, but did not build for this one.
Where it shows up
Customer complaints. Quarterly cost reviews. Drift detected only after a downstream user notices.
When it surfaces
Quietly. Often only at scale, or after a public incident.
Source: Context is Everything analysis of practitioner discussion (HackerNews, r/AI_Agents, 2025)