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The short version
AI employees, AI agents and digital workers are labels for AI software that does work, and they differ most in what you buy: a seat, a task or a finished workstream. RevSure, the agentic GTM platform built on the Full Funnel Data Graph, deploys digital workers that own GTM workstreams under human supervision. This guide sorts the labels by purchase unit, context, supervision and exit terms.
In July, an operations lead at an enterprise AI software company mentioned, almost in passing, a piece of work they had built for themselves. Asked on a weekly check-in with RevSure whether there was any MCP use case worth looking at together, they described it.
"I create an agent to look at, um, ops that didn't have marketing attribution attached that maybe we think that it should," the ops lead said. The agent works from the contact and from account-level engagement, and it uses the RevSure MCP server to pull the recent touchpoints on an account and look back over them.
Then, in the same breath, the ops lead started questioning their own design: "at that point maybe we should just create like a static model type of thing."
That second sentence is the most useful thing in this guide for anyone shopping for AI employees. The ops lead built the agent, chose its inputs, decides whether its suggestions are right and is now weighing whether it should be an agent at all. Every one of those decisions belongs to them. That is what buying, or building, an agent means. A GTM team comparing AI employees, AI agents and digital workers is really deciding how many of those decisions it wants to keep.
What are AI employees?
"AI employees" is a marketing label for AI software sold as if it were a hire: it has a name or a job title, it is described as doing a role's work, and it is usually priced per month like a salary line. The term does not describe a particular architecture. Two products both called an AI employee can be a chat assistant with a persona, a single agent that sends emails, or a coordinated set of agents running a workstream.
Search results for "AI employees" are mostly vendors inviting small businesses to "hire" a receptionist or a sales rep. The framing is useful as far as it goes: it tells the buyer to expect output.
For a B2B go-to-market team, the label leaves the important questions open. Which systems does this employee read? Who checks its work before a prospect sees it? What happens when it is wrong, and who pays for that? A human hire arrives with a manager, a job description and a probation period, while an "AI employee" arrives with whatever the contract says, so the contract deserves more attention than the name.
Agentic AI vs AI agents: what is the difference?
The two phrases are close enough to cause confusion, and the difference is grammatical before it is technical. "Agentic" is an adjective. "Agent" is a noun.
Agentic AI describes a property: AI that can pursue a goal over several steps, choose which tools to call, act and check the result before deciding what to do next. A product, a workflow or a whole platform can be agentic. RevSure calls itself an agentic GTM platform for this reason: the word describes how the whole system behaves.
An AI agent is a specific piece of software with that property: one goal, a set of tools it is allowed to use, permissions, and a trigger that starts it. The ops lead's attribution checker is an agent. It has a job (find opportunities that look like they should carry marketing attribution), inputs (contact and account engagement) and a tool (the RevSure MCP server).
For a buyer, the distinction has a practical edge. You cannot buy "agentic AI" on its own. When it appears on a product page, it tells you the system can take multi-step action, which also means it can take multi-step wrong action. It does not tell you what the product owns, what it reads or who is watching. Those answers come from the noun you are actually buying: an assistant, an agent, a platform for building agents, or a worker. RevSure's guide to AI agents versus AI assistants covers the first split in more depth; the rest of this guide is about the purchase.
What are you actually buying? The five labels compared
Strip the branding away and five questions separate the labels: what the unit of purchase is, what the software reads, who carries the work, how quality gets checked and how it is priced in principle. The table puts the five labels side by side.
Read down the "who carries the work" column and the labels sort themselves. With an assistant, the person still does the job. With an agent, the person who built it runs the job and maintains the machine that helps. With a digital worker, the job itself moves, and what stays with the team is direction and the decisions that need judgment. An "AI employee" can sit anywhere on that line, which is why the label alone settles nothing.
Who carries the work: an agent you run or a worker you supervise?
Go back to the ops lead's attribution checker. It is a good agent. It encodes a real piece of judgment (an opportunity with engaged contacts and recent account touchpoints probably deserves marketing attribution) and it runs on resolved data through the RevSure MCP server instead of raw exports. RevSure's guide to Claude connectors explains why that context matters for any agent reading GTM data.
What the agent does not do is take the work off the ops lead's desk. Look at what they still own:
- The definition. "Maybe we think that it should" is a rule someone has to write down, test and revise when the funnel changes.
- The schedule. The agent runs when its owner runs it or sets it to run.
- The judgment on each result. Each opportunity the agent flags still needs someone to decide whether attribution really belongs on it, and that reviewing stays with the ops lead.
- The architecture. The ops lead's own question, agent or static model, is a design decision. Someone who builds an agent signs up to keep making those decisions.
For a power user who wants exactly that control, building agents on a governed context layer is the right call.
A digital worker moves most of that list to someone else. A check like the ops lead's would sit inside a larger workstream, such as the Digital Marketing Analyst's job of attributing pipeline and finding where it leaks, and running it every week, keeping the rules current and answering for the result would sit with the people supervising the worker. RevSure's Digital Workers page puts the split this way: "Your team sets the strategy and approves exceptions. RevSure's own GTM engineers manage the digital worker day to day and are accountable for the completion and quality of the work it delivers."
The ops lead would still see the flagged opportunities and decide on the ones that matter. What they would stop doing is maintaining the machine. The same pattern shows up in sales: this week's post on the Digital SDR's lead ranking shows a ranked call list arriving with its reasons attached, and the rep's job becomes deciding who to call.
Neither option is free of people: an agent needs a builder and an owner, and a digital worker needs a team willing to set direction and hold the vendor to the output.
What do "AI workers", "AI coworker" and "AI teammates" mean?
These three terms usually describe the relationship a product wants to have with the team rather than a distinct technology.
AI workers is the closest synonym for digital workers: software assigned a job and expected to deliver its output. When a vendor uses the term, ask the same questions you would ask of a digital worker, starting with which workstream it owns and who answers for its quality. RevSure's definition of a digital worker sets out the parts to look for.
AI coworker and AI teammates lean toward collaboration. Products described this way usually sit inside the tools a team already uses, take requests from several people and hand work back for review. Many are assistants or agents with a shared inbox, which can be valuable and is still a different purchase from a worker that owns a workstream, because the team remains responsible for directing each piece of work.
A useful test for any of these names: ask what happens on a week when nobody on your team asks it to do anything. An assistant sits idle that week and an agent runs whatever its schedule says, while a worker still delivers its workstream, because delivery was what you bought.
What should you ask before buying an AI employee or digital worker?
Earlier RevSure guides covered how to judge the work itself, including how digital workers differ from standalone agents and where human-in-the-loop approval belongs. The questions below are for procurement: the commitments to get in writing before anyone signs.
The contract and service level
- What deliverables does the contract name, and on what cadence? "A ranked inbound list with reasons, every Monday" is a deliverable; "pipeline support" is too vague to hold anyone to.
- Who is named as accountable for completion and quality: a person, a team, or nobody?
- What happens when a deliverable is late or wrong? Look for a correction process, not only a credit.
- Is the price tied to activity, tasks or finished work? Each unit rewards different behavior from the vendor.
Data access
- Which systems will it read, and which can it write to? Get the write list in full.
- Does it read your resolved account and contact data, or each tool's raw records separately?
- Whose credentials does it use, and can access be scoped by role?
- Is your data used to train models outside your tenant? Where is it stored, and is every action logged?
Approval points
- Which actions need a person's approval before they happen: sending outreach, changing CRM records, moving budget, publishing audiences?
- Who on your team approves, and what happens when they are away for a week?
- Can you move an approval point later, as trust builds, without a contract change?
Exit
- What do you keep if you leave: the outputs, the rules and definitions the worker used, the logs?
- How is your data deleted, and is there written confirmation?
- How does the work hand back to people? A worker that has run inbound qualification for a year has encoded a lot of decisions. Ask for them in a form your team can read.
A vendor selling a finished workstream should answer these quickly, because they describe the product. A vendor selling a tool will tend to answer with features, which tells you who will be carrying the work.
Which label should a GTM team buy?
Start from the work, then pick the label. If a person on the team wants to go faster at a job they will keep doing, an assistant is enough. If an operator wants to automate a check they understand better than anyone, an agent on good context is the right tool, and the ops lead in this story is a model of how to do it well. If the team needs a workstream done every week without adding headcount or another system to maintain, it needs a worker, and it should buy one on terms that name the deliverables, the approvers and the way out.
The ops lead's offhand question, agent or static model, is a question about upkeep. Every AI purchase answers it one way or another. With an agent, the answer lands on your team's calendar. With a digital worker, it belongs to whoever signed up to carry the work, and the contract should say so.
FAQs
What are AI employees?
AI employees are AI software products sold as if they were hires, with a name or job title and a monthly price. The label describes the pitch more than the technology underneath, which can be an assistant, a single agent or a coordinated set of agents. Buyers should check what the product reads, who supervises it and what it delivers.
What is the difference between agentic AI and AI agents?
Agentic AI is a description: AI that can pursue a goal across several steps, choose tools and act on the results. An AI agent is a specific piece of software with that ability, built for one goal with defined tools and permissions. You can buy an agent, but "agentic" only tells you how a product behaves.
Is an AI employee the same as a digital worker?
Sometimes. Both are sold as doing a role's work. A digital worker, as RevSure uses the term, owns a defined workstream with named deliverables, reads shared business context and is supervised by people accountable for its output. Many products called AI employees are single agents, so the contract and the supervision model are what tell them apart.
What is an AI coworker or AI teammate?
AI coworker and AI teammate describe AI that works alongside a team, usually inside the tools people already use, taking requests and handing work back for review. Many are assistants or agents in practice. The team still directs each piece of work, which makes them a different purchase from a worker that owns a workstream.
How are AI agents and digital workers priced?
Commercial models differ by label. Assistants are usually sold per seat. Agents are often priced by usage, such as runs or credits, or through the platform used to build them. Digital workers are bought for finished work, so pricing tends to follow the work delivered and an agreed service level. Check which unit you pay for, since each rewards different vendor behavior.
What should a GTM team ask before buying an AI employee?
Ask which deliverables the contract names and who is accountable for quality, which systems the product can read and write, which actions need approval and who approves them, and what you keep if you leave, including outputs, rules and logs. Clear answers suggest a finished workstream; feature lists suggest a tool your team will run.