THE DOOD FIELD GUIDE / IMPLEMENTATION

Evaluate an AI workflow before giving it more access

Build a small test set, review uncertain answers and measure useful outcomes.

Mrunal WalkeBy Mrunal Walke

FOLLOW THE HANDOFF

01Bound the task
02Test the failures
03Review before action
Open a worked example ↗

Illustrative prototype: classify support enquiries into three service queues. An uncertain or out-of-scope request goes to a coordinator. The assistant does not change customer records or send an unreviewed reply.

Define an output someone can check

A task such as classify an enquiry is easier to evaluate than a broad instruction to improve sales. Specify the allowed categories, required evidence and fallback. Decide whether the output is a draft, recommendation or action. Those categories need different approval boundaries.

Include difficult examples

Collect representative, permitted examples with ambiguous wording, missing references, conflicting information and requests outside scope. Record an expected outcome and why. Keep a separate evaluation set so tuning on known examples does not become the only measure of quality.

Inspect error cost as well as accuracy

A wrong routing decision and an unauthorized disclosure do not have the same consequence. Define what must always escalate and what a reviewer can correct. Track unsupported answers and missed escalation, not only successful responses. Access should be limited to what the task requires.

Expand only after a reviewed pilot

Record the version of prompts, source material and rules used for evaluation. Assign a person to review failures, maintain approved information and pause the workflow. Compare handling time with the time spent reviewing and correcting output. A faster draft is useful only if the total process improves.

Can we start with a fully autonomous agent?

Start with a bounded prototype and review gate. Broader actions require confirmed permissions, evaluated behavior and a recovery plan; they are not assumed in the initial offer.

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