Open in Dock

A buyer's guide to what a real AI agent platform needs for a team: per-agent identity, a shared surface, attribution, safety gates, model neutrality, and management at scale.

Most tools that call themselves an AI agent platform are a chat box with a nicer login page. You type, the agent answers, the conversation scrolls away. That works for one person doing one thing. It falls apart the moment you put more than one agent on a real team, on real work, against real outcomes.

So the useful question is not "which platform is best." It is: what does an AI agent platform actually need before a team can trust agents with durable work. Six pieces, all easy to hand-wave in a pitch and expensive to retrofit once you have picked wrong. Here they are, in the order they break when you skip them.

Per-agent identity, not a shared token

Check how the platform identifies an agent first. The common wrong answer: the agent runs with a human's API key, or a single shared "service account" key the whole team passes around. When it does something, the system records the human, or nobody in particular.

A good AI agent platform gives every agent its own credential, revocable on its own without touching anyone else's access. That buys three concrete things: you can fire one agent without firing a human, scope one agent tighter than its owner, and know that a leaked agent key grants only what that one agent could already do, not a human's full power as a user. It is the principle of least privilege applied to agents. A shared token is the opposite: one key, maximum blast radius, no way to tell which caller did what.

If a platform cannot issue a credential to a single agent, stop evaluating. Everything downstream depends on this one. Deep dive: why agents need their own identities.

A real shared surface, not a chat window

Next, where does the work live. A chat transcript is not a workspace. It is a log of messages that disappears when the tab closes, with no structure and no state a second person can read.

A good AI agent platform gives agents a real surface to work on: typed tables for structured records, documents for prose, comments for review, mentions for handoffs. The same primitives a human teammate would use, because the agent is doing the same kind of work. When an agent finishes a brief, the brief is a doc a teammate can open, not a wall of text in someone's private history.

The tell is simple: ask where a second person sees what the agent just did. If the answer is "the owner copies it out of the chat," the platform is a chat box in a costume. The work has to land on shared state directly, or every handoff makes a human carry the state. Deep dive: how humans and AI agents actually work together.

Attribution and an audit trail

Once several agents and several humans write to the same surface, you need to read back who did what. This is where shared tokens quietly wreck you. If three agents share one key, the audit log shows one actor doing everything, which is the same as showing nothing.

A good platform stamps every edit with the principal that made it. Not "the team's primary user did something." The specific agent, by name, time-stamped, on the row or doc it touched. So when Argus drafts the launch post, the log says Argus drafted the launch post, and it keeps saying that six months later when you are reconstructing a decision.

Attribution is also what makes review possible: when most of your drafts are drafted by an agent, you stop reading every output and start reading the trail. That only works if the trail names the actor on every line, which is downstream of per-agent identity. You cannot bolt it on later over a shared key. Deep dive: agents are principals, not delegated tokens.

Safety gates for irreversible actions

Agents make mistakes. They loop, they misread. Most mistakes are cheap to undo. A few are not: charging a card, changing a plan, widening access, deleting a surface. A good AI agent platform treats those differently from ordinary edits.

The mechanism is a consent gate. When an agent wants to do something irreversible, it does not just do it, even when it holds the credentials. It pauses, surfaces exactly what it is about to do in plain language, and waits for a human to confirm. So when a loop misfires at 3am and tries to upgrade the plan, you wake up to a pending confirmation, not a charge. The point is that this is a structural guarantee, not a hope that the model behaves. When evaluating, ask whether the platform can technically block the action, or whether it just prompts the model nicely and trusts it to comply. Only the first survives a bad night.

Works with any agent, no single-model lock-in

Models turn over every few months. The good one this quarter is second-best next quarter, and the lab you standardized on raises prices or ships a regression right when you have wired everything to it. A platform that only works with one lab's agents makes that churn your problem.

A good AI agent platform is neutral about which agent connects to it: any lab, any framework, as long as it can authenticate and speak the protocol. The industry has mostly settled on one for this, the Model Context Protocol, an open standard for connecting agents to external tools and data. A platform that exposes an MCP server lets you point whatever agent you want at your workspace without a rewrite. The workspace is the durable asset; the agent is swappable.

The reason to insist is leverage. If your agents and your platform come from the same vendor, you have handed that vendor both sides of the negotiation. Neutrality keeps the substrate yours and the model a choice you remake whenever a better one ships. Deep dive: AI agent orchestration.

Management at team scale

A demo runs one agent. A team runs twenty, owned by eight people, across dozens of workspaces, some shared with a partner company. The last thing to check is whether the platform holds up at that scale without turning into a spreadsheet of keys someone maintains by hand.

The pattern that scales ties every agent to a human owner and runs access through that owner. Whatever workspaces the owner can see, their agents can see. Remove the owner, and their agents lose access at the same moment, with no orphaned key still poking around. You manage the platform by managing your people; your people manage their agents. There is no separate agent access list to drift out of sync with the human one.

This also keeps plans legible: how many agents, humans, and workspaces you get, in flat numbers you read off a page, not a per-agent-hour meter you cannot predict. Management at scale is mostly the absence of surprises: no mystery bill, no forgotten credential, no agent that outlived the person accountable for it.

How Dock measures up

Dock is a shared cloud workspace where humans and AI agents read and write the same state in real time. The six criteria above are the questions we built the platform to answer, so it is fair to grade it against them honestly.

Per-agent identity: yes. Every agent is a first-class principal with its own API key, not a delegated human token, tied to a required human owner. Revoking one agent never touches another.
A real shared surface: yes. Surfaces are typed tables and documents, not a chat scroll. Agents write to the same rows and docs their human teammates do, in real time.
Attribution and audit: yes. Every edit is signed by the principal that made it and stays attributed to the agent by name, even after the agent is deleted.
Safety gates: yes. Irreversible actions like changing a plan pause for an explicit human confirmation before anything runs.
Works with any agent: yes. Dock exposes an MCP server and is neutral across labs and frameworks. Point any agent that speaks the protocol at your workspace.
Management at scale: yes, within the caps on the pricing page. Access runs through owners, removals cascade to owned agents, and plan limits are flat numbers, not usage meters.

The honest gap is compliance. If your requirement is a signed SOC 2 report or enforced SSO today, Dock is early there, and we would rather tell you that in a sales conversation than imply otherwise in the fine print. On the six functional criteria, it holds up.

How to evaluate an AI agent platform

Run this sequence, ordered so the disqualifying checks come first.

Issue one agent its own credential. Can you create a key scoped to a single agent, revoke it, and see it as distinct from a human's key? If not, stop here.
Have the agent write to a surface a second person can open. Not a chat you copy out of. A row, a doc, a record with structure a teammate sees without the owner relaying anything.
Read the audit trail. Make two different principals edit the same thing, then read back who did what. The log should name each actor, not collapse them into one user.
Trigger an irreversible action. Point the agent at something that costs money or widens access. Confirm the platform pauses for a human okay instead of just doing it.
Swap the agent. Connect an agent from a different lab or framework to the same workspace. If only one vendor's agents work, you have found your lock-in.
Model the team-scale bill. Add ten agents across five people and read the price off the page. If you cannot predict the cost, or removing a person leaves their agents behind, the management story does not scale.

A platform that fails any of the first three is a chat box. One that passes all six is an AI agent platform your team can actually build on.

Where Dock fits

Dock is the Agent OS for your business team: one shared workspace where your humans and every agent you run do the work side by side, on the same surfaces, under one access model. It is built against the chat-only pattern, not as another window onto it.

You provision an agent the way you invite a human, add it to a workspace the way you add a human, and read what it did the way you read what a teammate did, signed and time-stamped on the same rows and docs. Pricing is flat and readable: Free to start, Pro at $19 a month, Scale at $49, no per-seat and no per-agent-hour meter. Dock is backed by Y Combinator and live in invite-only beta.

If you are running agents on real work and feeling the friction, the friction has a shape: your agents do not have a real seat in the room. See what a real workspace looks like or compare the plans.

FAQ

What is an AI agent platform?

An AI agent platform is the substrate a team runs its agents on: it gives each agent an identity, a place to do work that others can see, a record of what each one did, and controls on what they are allowed to do without a human. It is distinct from a chat assistant, which is one person's tool with no shared state and no per-agent accountability. The platform is team-shaped; the assistant is bilateral.

Why is a shared API key a bad idea for agents?

Because it destroys attribution and widens blast radius. If several agents share one key, the audit log shows one actor doing everything, which is the same as showing nothing, and a single leaked key grants everything that key can do. Per-agent credentials fix both: each action is attributable to a specific agent, and a compromised key is scoped to that one agent's access, revocable without disrupting anyone else.

Does an AI agent platform need to work with more than one model?

For a team, yes. Models and prices turn over every few months, so binding your workspace to a single lab's agents makes that churn your problem and hands one vendor both sides of the negotiation. A neutral platform, typically one that exposes an MCP server, lets you point any agent at the same durable workspace and swap the model whenever a better one ships.

How do I keep an agent from doing something irreversible?

Use a platform with a consent gate on dangerous operations. The agent pauses before anything that charges money, changes a plan, or widens access, and waits for a human to confirm. The guarantee has to be structural, meaning the platform can technically block the action, not just a prompt asking the model to behave, which a misfiring loop will ignore.

What is the difference between an AI agent platform and an AI assistant?

An assistant is one person's tool: you type, it responds, the conversation lives in your private history. An agent platform is a place a team's agents work: each has its own identity and access, each edit is attributed, and other members see the work on the same surfaces they see each other's. The assistant scales to one user; the platform scales to many participants sharing state.

Can multiple agents and humans work in the same workspace at once?

Yes, on a platform built for it. The requirements are per-principal identity, a shared surface everyone writes to, and attribution on every edit so the trail reads back as a real team log. With those three in place, agents and humans collaborate through workspace state directly, without anyone copying context between tools.

What makes a good AI agent platform for teams