Experiment Reader: An AI Agent That Reads Out Experiments Honestly

Among the AI agent rails, this one exists to protect a decision process, not to speed up a decision.

Rail at a glance

The Experiment Reader's rail ties every run to a plan version and a read-out date, so the query service, not the prompt, decides when results can be read.

What it does and who uses it

The Experiment Reader writes the first summary an analyst would write, and it writes it the same way each time. It helps teams running more tests than analysts can narrate, where read-outs can drift towards the result people hoped for.

In this illustrative case, a digital bank tested a shorter onboarding checklist. Plan version 3 was fixed before launch, and every number is invented.

The read-out says "inconclusive", not "no effect", and it makes no recommendation to ship. It also states that the data cannot show long-term retention. Seeing a breached guardrail, the owner and analyst hold the change, and they write a new plan for a mobile test.

Why this level

The Experiment Reader stays at L1 because a read-out can be corrected and reissued at no cost, though decisions that follow it are harder to reverse. Its blast radius runs through people: a slanted read-out can ship a harmful change to everyone.

Its session scores low on the Rule of Two 5. Plans and results are internal, aggregates hold no individual records, and no tool can change state. Giving it the power to end a test or move traffic would add that third property, and it would let the reader act on its own reading.

Reading and stopping need to stay apart. Evan Miller's 2010 essay shows why: in one scenario, stopping at the first significant result pushed a nominal 5% false-positive rate to 26.1% 4. If you need early stopping, write a sequential design into the plan, and let the experiment platform apply it as a rule at set checkpoints.

This rail has no promotion path. If your experiment platform already writes a read-out you trust against a fixed plan, you may not need this agent.

Three ways it fails, and the control for each

A/B test read-outs fail in well-documented ways, and the Experiment Reader's controls run as rules before the model writes a word.

How to evaluate it

Evaluate the Experiment Reader by replaying past experiments whose conclusions an analyst has reviewed, then seeding faults it must catch.

• Disagreement with analyst review. Sort each case by kind: wrong direction, overstated certainty, missed guardrail, missed validity failure.
• Blind review. For a sample, analysts write their conclusion before reading the agent's. Otherwise agreement may only show that people anchor on the first read-out they see.
• Seeded faults. Alter historic data to add a sample ratio mismatch, a guardrail breach and a metric missing from the plan. Each must appear in the output.
• Language checks. The American Statistical Association says a p-value does not measure the size or importance of a result 3. Test that "not significant" never becomes "no effect", and that "significant" never becomes "important".
• Read-outs delayed. Record why. A late read-out is acceptable, and one on unfinished data is not.

Where it runs

Aggregates make an external model workable for the Experiment Reader where the contract permits, and a private model suits regulated teams. The sensitive part is often the plan, which describes what you may launch next. The boundary architecture patterns cover model placement. AI-assisted review of reports in PrivyCDP comes with limits on data access and review of results.

Related

• AI agents for product and growth teams: the playbook this read-out job belongs to.
• Weekly reporting summaries: the closest worked use case, with its locked figures table.
• Rail card template: the place to bind a read-out to its plan version.

Go back to the AI agent rails hub to see how read-only jobs fit the wider method, and download the rail card template for your own experiment programme.