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Sampling agent output: a review routine that keeps the queue short

The Guildwise HQ team · 24 October 2026 · 6 min read

• How-to
• AI agents

Why reviewing everything does not scale

A small team running several agents can produce more output in a day than one person can read closely. The obvious response is a review queue. The queue then becomes the bottleneck, and reviewers either slow the work down or start approving without really reading.

The second failure is the more serious one. Researchers describe automation bias as the tendency to accept a machine's output because the machine produced it. A 2023 study in Frontiers in Psychology on automation bias in medical image reading found that reviewers' accuracy dropped when an automated suggestion was wrong, and the drop appeared across experience levels. The lesson for agent teams is that a reviewer who sees a confident-looking result is at risk of waving it through.

The goal is a routine that is fast on routine work and deliberate where a mistake would be costly.

What the EU AI Act asks of human oversight

Article 14 of the EU AI Act applies to high-risk AI systems. It asks that the people overseeing a system stay aware of the tendency to over-rely on its output. It also asks that they can disregard, override or reverse the output in a particular situation, and that they can interrupt the system through a stop procedure.

Most agent work at a small team is not high-risk under that law, and this article does not offer legal advice. The design principles still transfer well. Decide which outputs can be trusted without a look, decide who can override, and keep a stop path that has been tested.

The European Data Protection Supervisor's 2025 TechDispatch on human oversight of automated decision-making makes a related point. Oversight that is only nominal, where a person clicks approve without the information or time to judge, does not give the protection it promises. The reviewer needs the evidence and the authority to act on it.

Set review depth by the cost of a mistake

The first decision is not how to review, but how deeply. A simple approach is three tiers.

Tier A: full review before the action

This tier covers anything that moves money, changes production configuration, touches customer data, or sends an external message. A named person reads the change and its evidence before it takes effect. There is no sampling here.

The NIST AI Risk Management Framework is a useful reference for this kind of decision. It asks organizations to map their AI uses, assign responsibility and measure outcomes, rather than applying one oversight level to everything.

Tier B: review the evidence, then sample

This tier covers internal code, scripts and data transforms that have automated tests. The reviewer reads the change summary and the test results, then opens the full diff for a sample of changes.

Google's study of modern code review, based on a case study of its own practice, reported that review there served more than bug catching. Reviewers also spread knowledge and understood the code. A reviewer who reads the reasoning and the evidence learns more than one who only hunts for errors.

Tier C: sample after the fact

This tier covers drafts, research summaries and internal notes that nothing downstream depends on. Pick a random sample each week and check it. Start with a sample size you can handle and adjust it after a few weeks of results. The right number depends on your observed error rate, so treat any starting figure as a guess to be tuned.

Make the agent hand over evidence

Reviewing is faster when the reviewer checks claims against evidence rather than rebuilding the work. Require each agent to attach four things to every output:

• A plain list of what changed, with file names or record IDs.
• The command or check that proves the change works, and its output.
• Any check that failed, was skipped, or could not run, stated plainly.
• A short note on what the agent did not finish or is unsure about.

If an agent cannot attach the evidence, the output goes back to the agent. The reviewer should not have to hunt for the missing pieces.

The fourth item matters most for sampling. An honest note about uncertainty tells the reviewer where to look, which lets a sample of ten do the work that a full read would otherwise do.

Plant known errors to test the reviewers

Sampling tells you about the agents. It does not tell you whether the people reviewing them are catching problems. For that, plant a known error in a small share of the batch each week, such as a wrong figure in a summary or a failing test left in place. Track whether the reviewer catches it.

The medical imaging study suggests why this matters. Reviewers' judgment shifted toward whatever the automated suggestion said, so checks that rely on attention alone are fragile. A planted error shows you whether the routine is catching problems or only looking busy.

Keep the planted errors realistic and log the results. If reviewers miss them repeatedly, the fix is usually a clearer checklist or a smaller queue, not a stricter reminder to pay attention.

A sample week for a two-person ops team

Suppose a two-person operations team runs three agents. One drafts invoice category changes for finance (Tier A, because it affects payments). One refactors an internal reporting script that has a test suite (Tier B). One drafts weekly internal summaries (Tier C).

On Monday, the finance lead reviews every invoice category change before it is applied. The change summary and the evidence list are read first. Anything unclear goes back to the agent with a question.

On Wednesday, the engineer reads the reporting script's change summary and test output. They open the full diff for two of the six changes, chosen at random, and spend a few minutes on the agent's note about what was not finished.

On Friday, the other team member samples four of the twelve weekly summaries and checks the figures against the source records. A planted error is inserted into one summary. The result is logged, and the sample size stays at four for the next week.

The whole routine takes about two hours of reviewer time. Most of that time goes to the finance changes, which is where a mistake would cost the most.

A checklist for the first month

• Write down which outputs are Tier A, B and C, and why.
• Require the four evidence items on every agent output.
• Set a starting sample size for Tiers B and C, and record how often sampled items need changes.
• Plant one known error per week and log whether it was caught.
• Review the tiers at the end of the month. Move items up if errors are slipping through, and down if reviews only confirm the agent is right.

Key takeaways

• Reviewing every output at full depth creates a queue, and skimming creates over-trust. Tiered review avoids both.
• Set review depth by the cost of a mistake. Full review belongs where money, production systems, customer data or external messages are involved.
• Require agents to hand over evidence and an honest note on what they did not finish. This makes sampling more reliable.
• Plant known errors to check whether reviewers are catching problems, not just whether agents are producing good output.

This article was written with AI assistance and researched from the sources listed.

Sources

1. Automation bias in medical image reading: a study in Frontiers in Psychology
2. European Data Protection Supervisor TechDispatch on human oversight of automated decision-making
3. Modern code review: a case study at Google
4. NIST AI Risk Management Framework
5. Article 14 of the EU AI Act: human oversight

This article was written by an AI agent for Guildwise HQ (Guildwise blog writer, claude-haiku-5-5) and is reviewed against its sources.