Pillar guide
Multi-agent AI platforms: what they do, and how to choose one
Once you have more than one agent, the hard part stops being the agents and becomes the space between them — who hands what to whom, what context travels, and what happens when a handoff fails. That space is orchestration, and it is what a multi-agent platform sells you.
What a multi-agent platform actually does
It coordinates several agents so they can share context, hand work to each other, and complete a multi-step job reliably across your tools. That is the whole category. The value is not in having many agents — it is in the handoffs being dependable.
Which is worth saying because the count is a vanity metric. Twenty agents that cannot reach each other are twenty chat windows. Three that delegate cleanly are a team.
What is a multi-agent system?
A multi-agent system is a group of AI agents, each with its own instructions, tools and context, that work on one job by passing tasks and results between them. The term is older than large language models: it comes from distributed AI research, where it described software agents and robots coordinating on a shared problem. Today it usually means several model-driven agents, and a multi-agent AI platform is the software that hosts, connects and governs them. The system is what you build; the platform is what you build it on.
Most multi-agent systems are built from three basic coordination shapes, often more than one at once:
| Pattern | How work moves | Good for | Watch out for |
|---|---|---|---|
| Supervisor (hierarchical) | A lead agent splits the job, hands the parts to specialists and assembles what comes back | Jobs with one owner and separable parts, such as a research brief | The lead becomes a bottleneck, and every result passes through its context |
| Peer-to-peer | Agents message each other directly and reply | Standing teams where each agent owns a function, such as sales asking finance for a quote | Loops and duplicated work, unless who may message whom is controlled |
| Pipeline (sequential) | Each agent does one stage and passes its output to the next | Repeatable stages, such as draft, then check, then format | One weak stage spoils everything after it, and a fixed pipeline is close to a workflow, which may be the cheaper answer |
Olano uses the first two: a parent agent delegating to its subagents for the supervisor pattern, and peer messages along the org chart for the second. The difference, with examples, is in agent-to-agent delegation; how the work between them is coordinated, including the five finer-grained patterns Microsoft documents, is multi-agent orchestration.
Why one agent stops being enough
Three failure modes announce it, and they arrive in this order:
- Instructions start contradicting each other. The prompt has grown to cover several jobs and the guidance for one undercuts another.
- Context fills with debris. Unrelated tasks crowd the same conversation and quality drops for reasons nobody can point at.
- You cannot attribute a bad answer. With one long prompt covering everything, there is no way to tell which part caused the problem.
Splitting the work fixes all three for the same reason it does with people: a job small enough to describe precisely is a job you can supervise. More on the threshold in multi-agent vs single agent.
The three categories
Platforms in this market fall into three groups with genuinely different trade-offs. None is best in general.
- Code-first frameworks. Maximum control, no ceiling on what you can express, and you own everything: the hosting, the retries, the credential storage, the monitoring, the upgrades. Right when you have engineers and the orchestration itself is your product.
- Visual and no-code builders. Fast to a working flow and legible to non-engineers. The trade is that you are inside someone's abstraction, and the day your process does not fit the canvas is expensive.
- Managed agent platforms. The infrastructure, connections and governance come with it; you configure rather than build. You set the agents up, and nobody on your side has to operate any of it.
A fourth option is usually missing from these lists and is often correct: don't orchestrate. If the work is deterministic and the same every time, a workflow automation tool is cheaper and more reliable than agents. Automation vs agents is the distinction.
The eight questions
These separate the categories faster than any feature matrix:
- How does work move between agents? A message and a reply, or a parent handing a task to a nested specialist? Both should exist; they solve different problems — delegation.
- What context travels with a handoff, and what stays behind? Everything travelling means noise; nothing travelling means repeated work.
- What happens when a handoff fails? Retries, timeouts, dead ends. Ask for the failure story, not the happy path.
- Can you watch it happen? If you cannot see the handoffs live, you cannot tell whether the team is collaborating or one agent is doing everything.
- Who may talk to whom? All-to-all messaging produces loops and duplicated work. Permissions should be configurable, and knowing a peer exists should be separate from being allowed to message it.
- Where does approval sit? One gate at the edge, before anything leaves, beats a gate per agent.
- How is it priced? Per seat punishes you for telling more of your team about it — an odd incentive for shared infrastructure. See per seat vs usage.
- What do you keep if you leave? The configuration, the procedures, the data, the ability to point it at a different model provider — lock-in.
Where Olano sits
Managed, and opinionated about the governance rather than the plumbing. Agents get distinct jobs and an org chart with managers, reports and co-workers; work moves both as peer-to-peer messages and as parent-to-subagent delegation; the org chart is live, so you can watch a handoff happen; messaging permissions generate from the structure, and visibility is deliberately separate from permission. Approvals sit at the edge, spending caps are hard, everything is audited, and pricing is per deployment rather than per person.
What we are not: a code-first framework. Olano is configuration-first by design, so the team, its permissions and its approvals are set up rather than coded, and nobody on your side has to own the runtime.
How many agents should you actually have?
Fewer than the category implies. Add an agent when a job is genuinely distinct, not when it is large — more agents mean more supervision, not more capacity. A lead plus a handful of specialists covers most real operations. The counter-intuitive answer, and a real team if you would rather see one.
Related reading
What is agent orchestration?
How work is handed off, how context travels, and what breaks when it does not.
Multi-agent vs single agent
The three failure modes that say a single agent has outgrown its job.
How to choose a platform
The wider evaluation checklist: channels, approvals, audit, spend caps, isolation.
Alternatives
The honest list, with Olano judged on the same criteria as everyone else.
FAQ
What is a multi-agent AI platform?
Software that coordinates several AI agents so they share context, hand work to each other, and complete multi-step jobs reliably across your tools. The value is in the handoffs being dependable rather than in the number of agents — twenty agents that cannot reach each other are twenty chat windows.
What is a multi-agent system?
A group of AI agents, each with its own instructions, tools and context, that complete one job by passing tasks and results between them. The usual coordination patterns are a supervisor that delegates to specialists, peers that message each other, and a pipeline where each agent does one stage. A multi-agent platform is the software that runs and governs such a system.
What is the difference between agent orchestration and workflow automation?
Workflow automation runs the same defined steps every time and should, which makes it the better choice for deterministic work. Orchestration coordinates agents that decide what to do next, which is what you need when the cases vary too much for a flowchart. Most businesses end up using both.
Do I need a multi-agent platform, or will one agent do?
Start with one. Three signals say you have outgrown it: instructions that contradict each other, a context filling with unrelated work, and being unable to tell which part of a long prompt caused a bad answer. Until then, more agents add supervision rather than capacity.
What should I ask a multi-agent platform vendor?
How work moves between agents, what context travels with a handoff, what happens when one fails, whether you can watch handoffs live, who may message whom, where approval sits, how it is priced, and what you keep if you leave. Those eight separate the categories faster than any feature matrix.
Are code-first frameworks better than managed platforms?
They are better at different things. A framework gives maximum control and no ceiling on expressiveness, and hands you hosting, retries, credentials, monitoring and upgrades to own. A managed platform gives you the infrastructure and governance built in, and takes the operating work off your hands. Choose on whether orchestration is your product or your plumbing.
A team, not a framework
Agents with distinct jobs, an org chart between them, delegation you can watch happening, and approvals on anything that leaves. Unlimited agents and people on one deployment.