Agent Output Verification Gate (QA)

Agent Output Verification Gate (QA)

1. 01

   Define the acceptance criteria for each agent output before deployment

   Specify what constitutes a pass for accuracy, completeness, and format. For example, a prospecting agent must include verified contact details and a personalized opening line.

2. 02

   Run a pilot batch of 20 to 50 real or simulated inputs through the agent

   Use a representative sample that covers edge cases, such as unusual names, missing data, or ambiguous queries. This surfaces failure modes early.

3. 03

   Check every output for hallucinated facts, fabricated sources, or invented data points

   Cross-reference claims against the client's CRM, website, or other ground truth. The GPTZero finding of fabricated sources in PwC reports shows even professional firms are not immune.

4. 04

   Verify that the agent's actions respect data access boundaries and permissions

   Confirm the agent only touches records it is authorized to see, and that it cannot modify or export sensitive client data without approval. The OpenAI security eval breach of Hugging Face underscores the risk of credential exposure.

5. 05

   Measure the agent's error rate against the acceptance criteria and document the results

   Track the percentage of outputs that pass. If the error rate exceeds your threshold, adjust prompts, retrain, or add human review steps before proceeding.

6. 06

   Establish a human review loop for outputs that fall below the confidence threshold

   Define which outputs require a human sign-off, such as anything going to a client or containing financial figures. This balances automation speed with accountability.

7. 07

   Log the verification results and feed them back into the agent's configuration

   Use the findings to refine prompts, update knowledge bases, or adjust model parameters. Continuous improvement depends on capturing what failed and why.