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How to evaluate AI assistants and agents
including this one.

The questions worth asking, the answers that should worry you, and how to design a pilot that proves something. We wrote it knowing you’ll point it at us.

6 minute read

First, work out what you’re buying

Three different things are sold under similar words, and each is judged differently. Decide which you need before the first call.

An assistant

Answers questions from your documents and drafts text, and a person carries the result elsewhere. Judge it on sources, permissions and refusal. The risk is a wrong answer that looks right.

Agents

Take actions in your business apps. Judge them on who answers for each one, what decides how much each does alone, and the record. A wrong action is harder to undo than a wrong answer.

Automation

Fixed flows with a model in one step. Judge it on reliability and upkeep. It breaks when the input varies, and in most inboxes the input always varies.

Many products now sell an assistant and agents together, Xalios among them. Judge each half on its own terms. A strong assistant tells you nothing about how the agents are overseen.

Questions to put to every vendor

In order: sources and refusal, permissions, who answers for each agent, how autonomy moves, the record, and where data lives. Each is phrased so that a vague answer shows as one.

“Ask it something our documents don’t answer.”

It should refuse and say why. An answer with no source, or with a source that doesn’t say what the answer claims, is the failure you’re buying protection from.

“What are your correct-refusal and wrong-refusal rates?”

On questions like yours, with the dataset named. A vendor that measures only accuracy counts every refusal as a failure, so the product is tuned to guess.

“Show me what happens when someone asks about a file they can’t open.”

In the product, with a real restricted file. It should refuse. Watch for a summary of the file that leaves out its name.

“Who answers for this agent, and where is that written down?”

Expect a named person in the business, recorded in the product. If the answer is “the IT team”, nobody who does the work is answering for it.

“Show me where I set what it does alone, and what changes that.”

In the product, not on a slide. Ask for the thresholds in numbers, whether edited approvals count, and what makes a task step back. A prompt field is advice the model can ignore.

“What can it never do alone, whatever its record?”

Expect a list your team writes and a spending cap nobody can switch off. “It depends on configuration” means anyone with access can change it.

“Pick a decision from last month and tell me why it happened.”

Live, in their own demo account, with the sources, the check that let it run and who approved it. How long it takes tells you what your audit will cost.

“Where is our data, and who else processes it?”

Ask for the region by name, whether you get a database of your own, and whether sign-in goes only through your identity provider. Then ask for every subprocessor and what reaches each one. If that list needs a follow-up email, nobody is keeping it.

“Which certifications do you not hold yet?”

A straight list with dates is a good sign. “SOC 2 ready” with no audit date should be treated as a no.

“What happens to the memory if we leave?”

Exportable in a usable form, or a lock-in you accept knowing it’s there.

Answers that should worry you

An accuracy figure on its own. Ask for the dataset, the definition of correct and the refusal rate that goes with it.

An assistant that always has an answer. Some of those answers are guesses.

“Fully autonomous” offered as a setting chosen when the agent is built, with nothing that pulls it back after mistakes.

Approval counts offered as proof of quality, with no spot checks or seeded mistakes behind them.

A claim that prompt injection has been solved. Nobody can know that.

A demo on made-up documents, and reluctance to try yours in the room.

Connectors “on the roadmap” for the system your work depends on.

No named reference for the same kind of work, and nothing offered in its place. A new vendor can still offer a paid pilot with exit criteria written in advance, and a dated audit.

A business case built on cutting staff. The people asked to review the agent’s work are the ones it threatens, and their approvals stop meaning much.

Pricing models, and what each one costs you later

Per seat

Predictable. Cost follows headcount rather than the work done, and reviewers may need seats of their own just to approve items.

Per resolution

Charges for each answer given or case closed. It pays the vendor more for answering than for refusing, which is the wrong incentive for work you have to answer for.

Per item handled

Charges for each item the product works on, whatever the outcome. Ask what counts as an item and whether a retry counts twice.

Platform fee

Often hides the real number in implementation services. Ask for the total first-year figure, people included.

Whichever model, ask two things: what year two costs at three times the volume, and what it costs to stop. Our own answer is on the pricing page.

Designing a pilot that proves something

Many pilots are designed to succeed, which is why so many end without a decision. A useful one can fail on criteria everyone signed before it started.

One stream of work

One kind of work with real volume, such as a few hundred items a month across a handful of request types. One person responsible for it, and a definition of correct that two people would agree on.

Baseline first

Measure today’s time per item and error rate before anything is switched on. Without a baseline there is no result.

Your own questions

A few hundred real questions, each with an approved answer. Score accuracy, correct refusals and wrong refusals. Include the scanned attachment and the supplier who emails a photo.

Seeded restricted documents

Put documents among the sources that the test askers must not see, then ask about them. Any leak fails the pilot.

Seeded known-bad drafts

Slip drafts with known mistakes into the review queue. The share your reviewers catch tells you whether their approvals mean anything.

Reviewer minutes

Time spent in review per item, week by week. If it stays flat, the checking is eating the time the agent saved.

Every item approved

Start with a person approving every item. How much the agent does alone should then move on the pilot’s evidence, by rules agreed in advance. At low volume it will barely move, so judge the answers and the fixed limits instead.

Written exit

The numbers that mean yes and the numbers that mean no, signed by the person responsible and the vendor before day one.

Thirty days

Long enough for the exceptions to show up and, at real volume, for autonomy to move. Short enough that the person who started it is still there to decide.

Scorecard

Score each vendor out of five, weight what matters to you, and keep the notes.

The weighting is worth arguing about internally. For work someone will have to answer for, autonomy and the record usually matter most. They are also the two most likely to be scored on a promise, so score them only on what you saw working.

Sources and refusal

Every claim cited, and a refusal that says why

Permissions

Never shows a person what they may not see

Who answers

A named person in the business for each agent

Autonomy

Thresholds in numbers, and a step back after mistakes

The record

One decision traced end to end, live

Where data lives

Named region, its own database, named subprocessors

Score us first, starting with our security page

Bring the list. We’ll answer it in order.

Including the questions we come out badly on. If we’re the wrong fit for where you are, a short call is the cheapest way for both of us to find out.

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