Every tool in this category now claims multi-agent support. The claim is close to meaningless on its own, because running several agents at once is easy and the failure modes arrive later: two agents editing the same file, a run that stalled an hour ago and told nobody, a queue of finished work nobody has reviewed, and no way to answer what an agent actually did last Tuesday.
A multi-agent collaboration platform is the layer that makes those problems visible and owned. This article lists the features that decide whether a platform does that, what each one fails like when it is missing, and how to test it in an afternoon. Use it as an evaluation checklist rather than a product tour.
TL;DR
Eight features matter: agents as real assignees, isolated execution, run state separate from task status, an attention queue, an explicit review gate, provider neutrality, scoped credentials, and an evidence trail. Everything else is packaging. Test them by running three agents at once on one repository and watching which of the eight you find yourself improvising.
1. An agent is an assignee, not a chat panel
The first question splits the category. Can an Agent hold the Assignee field on a unit of work, or is the AI a panel that helps a person?
An assignee claims the Task, reads its context, executes somewhere real, and returns a result. Assistance drafts descriptions, summarizes threads, and triages backlogs for the person doing the work. Both are useful. Only the first changes who does the work, and AI agent project management turns on the distinction.
Without it, the tracker holds intentions and your terminals hold reality, and the two drift within a day.
2. Isolated execution per run
Two agents in one working directory is not a coordination problem, it is a data-loss problem. There is no lock and no queue: whichever process saves last wins, and the damage ranges from a silent clobber to a semantic break that compiles. What happens when two coding agents edit the same file walks the failure step by step.
The platform should give every run its own workspace by default. Sharkly gives each Task an isolated directory, and repository-backed work can use a separate worktree per run. A shared local directory is also legitimate when a run needs an existing environment, caches, and installed tooling, in which case only one run should hold it at a time and the rest should wait in a state you can see.
Without isolation you will build worktree scripts yourself, then discover you also need to schedule them.
3. Run state that is separate from task status
These are two different facts and platforms that conflate them mislead everyone. A Task’s workflow status is a team agreement: Backlog, Todo, In progress, Ready for review, Done. A run’s state is an execution fact: queued, dispatched, waiting for a local directory, running, completed, failed, canceled.
A run can complete while the Task stays in review. A Task can sit in progress while its run failed twenty minutes ago. If the platform shows one number for both, the board lies. Coding agent observability covers which signals are worth an alert.
4. An attention queue that pulls, not a feed that scrolls
With three agents running, the only question that matters at any moment is which one needs a person. A platform earns its place by answering that without you going to look.
The useful form is narrow. Sharkly’s Inbox carries three signals: waiting for your reply, waiting for your review, and failed. Slack notifications can be muted per group while the Inbox still records everything, which matters because the alternative is a chat channel that everyone silences by week two.
Without an attention queue you become the polling loop, which is the real cost described in too many terminals.
5. An explicit review gate
Agents should be able to finish. They should not be able to decide that finished means accepted. The platform needs a status that means “the agent is done and a person has not looked yet,” and the workflow should keep the final move for a human.
In practice that is one rule: the Agent can reach Ready for review, a person moves it to Done. Human ownership of agent work explains why that boundary holds even as trust grows, and human review is the bottleneck explains why the gate needs to be designed rather than assumed: parallel execution with serial review buys you very little.
Without the gate, “done” stops meaning anything and someone reconstructs the truth from diffs.
6. Provider neutrality
Teams already run more than one runtime, and the best one changes every few months. A platform that binds you to a single vendor’s agent is a bet on that vendor staying ahead.
Look for the ability to register several runtimes, route a Task to whichever fits, and add one the vendor does not ship. Sharkly detects supported tools on a connected Computer and reports each usable tool as a Runtime, and a runtime it does not list can be added as a custom ACP runtime with its own startup command and a test-connection step. Routing then becomes a per-task decision rather than a platform migration, which is what which coding agent should get this task is about.
The related question is who pays for tokens. A platform that resells model usage is making a margin on a thing you already buy. Sharkly does not sell tokens: runs bill against the Claude Code, Codex, or API plan attached to the Runtime.
7. Scoped credentials and permissions
An agent with unrestricted access to a host will eventually use all of it. The permission surface has three layers and a gap in one undoes the other two: what tools the runtime may call, what the host and its credentials expose, and what the task scope allows. Coding agent permissions breaks down each layer.
Concretely: credentials should be projected per run rather than inherited from whatever shell started the daemon, install tokens should be short-lived and single-use, and agents should never authenticate as you. If a platform cannot answer “what could this agent reach,” that answer is everything.
8. An evidence trail you can read later
The last feature is the one nobody evaluates and everybody needs by month two. When a change ships, you should be able to open the work item and see what was asked, which runtime ran it, what it did, what it verified, what it was unsure about, and who accepted it.
That means agent output persisted as comments on the Task, an execution log with tool calls and trigger source, and links to the resulting pull request. It is what makes reviewing agent work at the task level possible instead of re-deriving the change from a diff.
The afternoon test
| Test | What to watch |
|---|---|
| Start three agents on one repository | Does each get its own workspace, or do you create worktrees by hand? |
| Make one task ambiguous | Does the run stop and ask, and does that reach one place you check? |
| Let one run fail | Does the failure surface with enough detail to decide retry or reassign? |
| Finish all three | Is there a status between finished and accepted, and who can set it? |
| Add a second runtime | Can you, without leaving the platform? |
| Ask what agent X did last week | Can you answer from the tool alone? |
Six answers is a platform. Three is a dashboard with an agent feature.
What to ignore
Agent counts in marketing copy. Running 100 agents is a scheduling fact, not a capability, and the constraint was never how many you can start. Model benchmarks published by the platform, unless the methodology is published too. Chat interfaces as the primary surface: talking to a Crew is useful for thinking, and a Task is a commitment with a repository, a Computer, and a reviewer. Making every message a task is how boards turn into noise.
FAQ
What is a multi-agent collaboration platform?
A layer above the coding agents you already run that holds the work: tasks, assignment, isolated execution, run visibility, blockers, results, and human review, across more than one agent and more than one person.
How is it different from an orchestrator?
An orchestrator decides what runs next and in what order, usually inside one automated pipeline. A collaboration platform assumes people are in the loop and models the handoffs between them and the agents. Orchestration is one feature of the larger thing.
Do we need one for a single agent?
Probably not. One agent, one repository, one person is a terminal and a tracker. The equation changes at the point where two agents can touch the same code or a second person needs to see the work.
Does it replace Claude Code, Codex, or Gemini CLI?
No. Those execute. The platform manages what happens around execution: assignment, isolation, review, and the record. Provider neutrality is the point, not replacement.
What is the cheapest way to evaluate one?
Run the afternoon test above on a real repository with a low-risk task, and keep your current tracker in place while you do. Getting started with Sharkly is one Computer, one Agent, and one task that changes no files.
