How AI Employee Tools Handle Identity and Review
Some AI employee tools give agents their own professional identity. A survey of the field, with identity and trust as the test.
Founder, Task Machine
Some AI employee products give each agent a name, profile picture, email address, phone number, and personality, then give that agent a role in existing work channels. That can make a role easy to understand, but it also makes identity and disclosure part of the product decision rather than a detail to settle later.
When you shop for AI employee tools, ask who owns the agent's identity and which actions need review. A worker with its own professional identity and a worker acting through your accounts can both be useful. Neither arrangement tells you on its own what customers will be told or whether outgoing work requires approval. Those boundaries depend on how you configure and operate the tool.
The two identity models
An AI employee tool has to answer a question a human hire never raises: who does the outside world think it is?
The professional-identity model gives each agent a role and its own contact channels, sometimes including an email address and phone number. Delos offers workers with separate professional identities. This can make a worker easier to assign to a standing responsibility and reach through existing channels. Whether external contacts know they are interacting with AI is a separate disclosure decision, not an inherent property of the model.
The account-owned model keeps agents behind accounts you control. You can place approval gates before customer-facing work goes out. Its advantage is that the operator can see and review work under the company's own identity, rather than managing a separate contact channel for each worker.
A separate professional identity needs an explicit policy for customer communication and review. Decide when the agent identifies itself as AI, which messages require approval, and who is responsible for its actions. Check the rules that apply to your use case and market rather than assuming that a name or an email address answers those questions.
The field, surveyed
| Tool | Identity model | What it is best at | The tradeoff |
|---|---|---|---|
| Delos | Professional identity. Named role-based workers with their own email, phone, and digital computer, plus memory, working in Slack, Teams, and more than 3,000 tools | Role clarity and fast setup: Delos says a first worker can be deployed in under five minutes, and the published Team plan is €50 per month for 40,000 credits | A strong fit when one role should own a goal and its own email and phone presence matters. Less suited when the process crosses several roles and gates |
| Duet | Agents you teach once and share with clients | Build an agent from your checklists and templates, then give each client its own workspace with that agent, on plans from $20 a month after 7 days free | A strong fit for consultants packaging their method for clients. Less of a system for recurring, verifiable operations inside your own company |
| Viktor | AI employee living in Slack and Microsoft Teams | Work across 3,200+ tools from the chat platform you already use, free to start with Team at $100 a month | The lowest-friction option if your company runs on Slack or Teams. The chat surface is the whole product, so structure and run history are not the point |
| Convey | Named digital teammates at enterprise scale, such as AI Larry and Finance Frank, with their own identities and access | Teammates learn a process when someone describes it or shares their screen, with SOC 2 Type II certification and a $38M Series A led by Andreessen Horowitz | Impressive and well-funded, but sold to enterprise operations teams with specialists who configure each teammate. Not shaped for a small operation |
| Task Machine | Account-owned. Agents act through accounts you own, with approval gates you configure for client-facing work | Recurring operations you can verify: explicit workflows with approval steps and verifier checks, a step-by-step run history, autonomy set per kind of work | Control-first by design. More setup than hiring a persona in five minutes, and it expects you to review what needs judgment |
Prices and features were checked against each vendor's own pages on 6 October 2026.
Two of these deserve their credit stated plainly. Delos has broad built-in channel presence, including a separate phone identity plus Slack and Microsoft Teams, and it emphasizes the speed from signup to a working agent. Convey's demonstration-based learning is a distinctive mechanism. Each buyer still needs to decide how agent identity and disclosure should work for their customers.
Why transparency needs machinery, not policy
Deciding to use your own accounts with review gates is easy. Operating it is the hard part, because a policy of "a human reviews customer-facing work" collapses without a system that enforces it. Someone has to know which outputs are waiting, catch the ones that failed a check, and keep the reviewing from becoming a full-time job of watching agents work.
That is the shape Task Machine is built around. Agents and humans work as one team, and recurring work runs as explicit workflows where you place Human approval and Ask human steps and verifier checks exactly where judgment or checking belongs. Everything that needs you lands in one inbox, an email awaiting approval, a check that failed, a question an agent cannot answer alone, so review is a queue you clear each day. Every run keeps step-level logs, which means when a customer asks what happened, you can read the actual steps instead of trusting a summary. Autonomy is a level you set per kind of work, so internal research can run unattended while you set customer-facing work to wait for approval. Agents run in Task Machine's Cloud by default, with an optional Local Worker for work that needs your own machine, and act through accounts you own, under your company's real name.
The operational difference is that you control the identities and the approval boundaries, and can inspect what happened when a run completes.
Who should not pick Task Machine
If a specialized worker with its own email and phone is the operating model you want, Delos covers that role more directly than Task Machine. If you want to package your method as an agent your clients can use, Duet is the stronger fit, and if you just want an agent inside Slack or Teams, Viktor is simpler than anything else here. If you are an enterprise department, Convey is built for your procurement process and Task Machine is not.
Task Machine is for the operator who wants agents to work through accounts they own, with explicit approval gates for client-facing work and runs they can verify.
The direct comparisons are at Task Machine vs Delos, Task Machine vs Duet, and Task Machine vs Viktor. If account-owned work with explicit review gates fits your operation, start a 7-day trial.