The QA Problem Nobody Named
Most organizations deploying AI agents have not solved the quality assurance problem. Most have not even named it.
Traditional QA assumes a human created the work and another human reviews it. The volume is manageable. The cadence is predictable. A developer writes code, a reviewer checks it. A writer produces a draft, an editor refines it. One to one. Maybe one to three. Human-scale ratios.
With agents, the volume of output can overwhelm human review capacity in hours. One agent can produce 100 reports, 50 code changes, 200 data analyses in a single day. Who reviews all of that? The honest answer in most organizations is: nobody.
Not Everything, Not Nothing
The answer is not "review everything" or "review nothing." Both extremes fail. Review everything and you negate the speed advantage of agents. Review nothing and you accept unknown error rates in production outputs.
The answer is risk-based review. High-stakes outputs (customer-facing communications, financial calculations, legal documents) get human review. Every time. No exceptions. Low-stakes outputs (internal summaries, data formatting, routine calculations) get automated validation.
The key word is "automated." Not "skipped." The low-stakes outputs still get checked. They just get checked by machines against defined criteria, not by humans eyeballing each one.
The Missing Criteria
Automated validation requires clear success criteria. And most organizations do not have them written down. "Good enough" does not work when an agent produces 100 outputs per hour. You need to define what "good" means, explicitly, measurably, and without ambiguity.
What makes a customer email acceptable? What makes a data analysis reliable? What makes a code change safe to deploy? These questions have answers. But in most organizations, those answers live in people's heads, applied through judgment and experience. Not written down. Not measurable. Not automatable.
The act of defining these criteria is itself high-leverage work. Because once you define them, you can automate the checking. And once you automate the checking, you can scale the output without scaling the review team.
The Review Pipeline
Leading teams are building review pipelines. The pattern is straightforward:
Agent produces output. Automated checks validate against defined criteria. Outputs that pass all checks ship automatically. Outputs that fail any check get flagged for human review. Humans review only the flagged items.
This inverts the traditional model. Instead of humans reviewing everything and occasionally delegating to automation, automation reviews everything and occasionally escalates to humans. The human becomes the exception handler, not the bottleneck.
At Sneeze It, every agent output goes through validation. Our email agent drafts responses, but every draft is checked against client communication standards before it reaches a human for final approval. Our analytics agent runs calculations, but every calculation is validated against baseline ranges before it enters a report. The agents do not know they are being checked. They do not need to.
The Double Dividend
Here is the meta-insight that most people miss. Defining quality criteria for agent review also improves human work quality. The act of making standards explicit raises the bar for everyone.
Before you can tell an agent what "good" looks like, you have to define what "good" looks like. And that definition, once it exists, applies to human work too. The new hire reads the same criteria. The freelancer follows the same standards. The quality bar becomes a shared artifact instead of tribal knowledge.
Every organization that has gone through this exercise reports the same thing: "We thought we were defining quality for our agents. We ended up defining quality for our entire team."
What OTP Enables
OTP's claim validation system is a working example of this pattern. When a publisher submits an OOS, the platform validates structure, checks for PII, scores quality, and flags issues before any human reviews it. The protocol embeds quality assurance at the infrastructure level.
This is not a feature bolted on after launch. It is a design principle. Organizational intelligence is too important to ship without validation. The review pipeline is the product, as much as the intelligence marketplace itself.
Define Your Quality Criteria
Pick three common outputs your agents produce. Define the quality criteria for each one. Write them down. Make them measurable. That is the foundation of agent QA, and the beginning of better quality for your entire team.