The Done-for-You AI Employee Evaluation Checklist

A new category of software spent the last year renaming itself. What used to be called an automation, a workflow, or an assistant is now an employee. Marblism sells AI employees that run your business. Lindy sells AI employees that handle your inbox. Clawbot sells AI employees for customer support. Zapier Agents positions itself as the automation layer that finally acts like a teammate. Each landing page shows a friendly avatar with a name, a job title, and a starting salary that happens to be billed monthly. The pitch is the same one every time, which is that you hire this thing, onboard it for a weekend, and on Monday morning a task that used to eat half a human is gone.

Most founders buying into this category right now are doing so on vibes. They watched a demo video, felt the relief of not having to write another job description, and put in a credit card. Two weeks later the tool is either an expensive Zapier with a face or it is genuinely moving work, and the founder cannot tell which because nobody taught them what to look for. The SERP for any of these products is dominated by the vendors themselves and by affiliate roundups that rank features without ever asking whether the thing actually replaces labour. This article is the checklist those roundups refuse to write. Eight questions, grouped across the five things that actually matter, that you walk through before you commit to any AI employee product, whether off the shelf or built custom.

What The Agent Actually Does And What It Actually Costs

The first question is the one almost every vendor blurs on purpose. An AI employee product will tell you it handles inbox triage or drafts sales replies or processes refunds, but the real question is what percentage of that work ships without a human looking at it first. There is an enormous gap between a tool that reads an email, proposes a reply, and waits for you to hit send, and a tool that reads the email and sends the reply itself while you sleep. The first one is a drafting assistant and saves maybe thirty percent of the time a real employee would. The second one is genuine labour replacement and is also, by the way, where the category gets genuinely scary. Ask the vendor for a written breakdown of every action the agent takes and whether that action is autonomous, requires approval, or happens only after an explicit human command. Most vendors cannot produce that list in under an hour, which tells you something about how clearly they have thought about it. Lindy will show you this breakdown if you push. Zapier Agents will tell you it is whatever you configure, which is technically true and practically useless because defaults are what actually run. Marblism leans heavily on autonomous and should be stress-tested accordingly.

The second question is what a day of work from the agent actually costs. Pricing pages for AI employee products are designed to make you feel like you are hiring someone for a tenth of what a human costs. Nineteen dollars a month, ninety-nine dollars a month, three hundred dollars a month. Compared to a forty-thousand-dollar virtual assistant that sounds like a miracle. The trick is that the unit they bill on almost never maps cleanly to a day of work. Some vendors bill per task, where a task is defined by them and can mean anything from one email to a multi-step workflow that would take a human forty minutes. Some bill per message or per execution, where a single real-world outcome might fire ten executions. Some bill a flat monthly fee that caps at a usage level you will blow through in the first week if the tool is genuinely useful. Take whatever work you expect the agent to do, estimate how many of the billable units that represents under the vendor's definition, and multiply out. Then compare to what the same work would cost a human contractor at a real hourly rate. If the tool ends up at more than forty percent of human cost for the same output, you are not hiring an employee, you are buying a marginal automation with a nicer website, and you should either negotiate a custom plan or walk.

How The Agent Handles Your Data And Your Mistakes

The third question is about data. An AI employee is only useful if you give it access to real context, which means real data. Real data means customer records, billing information, contracts, internal Slack, calendar details, and in many cases the contents of your inbox. Before any of that gets connected you need a clear answer on three points. First, what data does the agent read at each step of each workflow, meaning is it pulling whole threads or specific fields. Second, where is that data stored after the agent reads it, meaning does the vendor persist it in their own database, send it to a model provider, and if so which provider and under what retention. Third, is any of it used for training, by the vendor or by the underlying model. Most of the venture-backed AI employee products sit on top of OpenAI or Anthropic and inherit those data-handling terms, which are generally fine for business use but not automatic. A vendor who cannot answer these three points in a single paragraph of plain English is a vendor whose legal page you are going to have to read yourself, and who you probably should not trust with your customer list.

The fourth question is the one that separates toys from tools, and the one almost no evaluation article asks. What happens when the agent is wrong. AI employee products will be wrong. They will email the wrong person, refund the wrong charge, escalate the wrong ticket, and occasionally do something genuinely embarrassing. The only question that matters is what the product does structurally about it. A good product has three layers. It has a log, meaning a complete audit trail of every action the agent took with timestamps and inputs, so that when something goes wrong you can find the exact decision that caused it. It has a rollback, meaning for reversible actions like sending an email or creating a record there is a single click that undoes the action rather than forcing you to clean it up manually. And it has a kill switch, meaning a way to pause the agent globally while you investigate without having to unplug individual integrations. Fire-and-forget agents, which is most of the category today, have none of these. They run, they do things, and when something goes sideways you find out from a customer. Ask the vendor to walk you through a real incident post-mortem from the last sixty days. If they cannot, assume they are not logging anything useful.

How Deep The Integrations Go And How Many Humans Can Use Them

The fifth question is about integration depth, which is where vendors oversell the hardest. Every AI employee product claims to integrate with everything. The claim is almost never a lie, but the depth varies by two orders of magnitude between products. At the deep end you have agents that connect through real OAuth flows, authenticate as a specific user or service account, and use first-party APIs with proper permission scopes. These integrations tend to be stable, auditable, and revocable with a single click in the third-party app. At the shallow end you have agents that screen-scrape or browser-automate their way through the interface a human would use, logging in with stored credentials and clicking buttons. Screen-scraping works until the target app changes a button, and then your AI employee silently stops doing half its job. Worse, stored credentials in a third-party vendor are exactly the kind of surface area a security team will make you delete later. Before signing up, ask for a list of integrations you care about and for each one ask whether it uses a first-party API with OAuth or an automation layer that logs in on your behalf. Lindy and Zapier lean heavily on first-party APIs. Some of the newer players are mostly browser automation in a trench coat, and the demo videos look identical to the real thing.

The sixth question is whether the agent works for a team or only for a solo founder. The second product most AI employee tools build, usually six months after launch, is the team version. The first version almost always assumes a single user, a single inbox, a single calendar, and a single set of credentials. That is fine while you are testing on yourself but breaks the moment you try to share the agent with an assistant, a co-founder, or a new hire. Before you commit, check four things. Whether multiple humans can see the same agent and its history, or whether the agent is locked to the account that created it. Whether permissions can be scoped so that a junior person can run the agent without seeing every email it has ever touched. Whether the billing seat model actually matches how you want to use the tool, meaning does adding a second human double your cost for no extra throughput. And whether there is a handover story, meaning if the person who configured the agent leaves the company, can someone else take over without rebuilding everything. Products that get this wrong are still useful for solo founders. They become a liability the moment you start hiring.

How You Leave And Who Answers When It Breaks

The seventh question is about exit. Every AI employee product is a startup, and most of them will either get acquired, pivot, or quietly stop shipping within three years. Some will be fine, some will not. The question you ask on the way in is the one you will be glad you asked on the way out. What can you take with you. At minimum you want to be able to export the logic of your workflows in a portable format, even if that format is just a clear text description of the steps. You want to be able to export the history of everything the agent did, so that if you rebuild on another tool you still have the audit trail. And you want to be clear about what cannot be exported, which is usually the trained behaviour of the agent itself, meaning any fine-tuning or preference data you built up over months of use. The deeper the agent has been embedded in your operations, the more painful leaving becomes. Vendors who have thought about this will show you the export flow on request. Vendors who have not will talk about partnerships and ecosystem, which is the sound of a company that wants you locked in.

The eighth question is the most boring and the most predictive. Who provides support when the agent breaks. When your AI employee stops working at three in the afternoon on a Tuesday, who do you talk to, how fast, and do they have the power to fix it. A lot of these products are small teams with Discord-based support, which is fine for a prosumer tool and not fine for anything you have put on the critical path of your business. Ask three specific things. What is the median response time during business hours, and what is the ninety-fifth percentile. Is there a named human who owns your account once you hit a certain spend, or are you always funneled into a shared queue. And when the fix requires a change to the product itself rather than a tweak on your side, what is the typical turnaround. The answers will tell you whether you are buying software or whether you are buying a partnership. For anything that touches customers or revenue, you want the second.

Running The Checklist Against Your Actual Shortlist

Take the eight questions above, put them in a single document, and run your shortlist through them before anything else. Not the feature comparison, not the pricing comparison, not the demo video. These questions. A product that scores cleanly on unsupervised behaviour, real cost per day, data handling, error recovery, integration depth, team support, export path, and human support is a product you can put in front of real work. A product that dodges two or more of them is a product that will embarrass you in the first quarter. The category is young enough that most vendors are still figuring out the answers themselves, which means the ones who answer crisply and in writing are signalling something important about how seriously they take the word employee.

The other path, and the one most founders underestimate, is to stop shopping. Off-the-shelf AI employees are optimised for the median buyer, which means they do a decent job at a generic task and a mediocre job at yours. If the work you want to offload is the thing that actually makes your business different, a custom-built agent on your own stack will beat any vendor on the list, usually at a lower run-rate cost once you stop paying per-task fees. That is the conversation WitsCode has with most of the founders who land on this page. Bring the checklist, bring the shortlist, and let us build the version that actually answers every question on it.

→ Book a WitsCode AI employee custom build