AI agent orchestration is the layer that runs a process around your agents: it hands each agent a step, keeps the case open while the step is outstanding, remembers who still owes what, chases them, applies the same rules every time, writes the result to your systems and keeps a record of all of it. Agents do the tasks. Floowed runs the process.
Agents finish tasks. Cases take longer.
Agents are now genuinely good at tasks. Give one a clear instruction and it will read a file, call an API, draft a reply, query a database or write code. The tooling around them has grown fast, and a well-built agent finishes its task in seconds.
A real operational case does not finish in seconds. Take supplier onboarding. The request arrives by email. The bank letter comes in as a photo taken at an angle. The company registry says a slightly different legal name from the one on the invoice. Finance hasn't confirmed the budget, and the contract owner is on leave until Thursday. The case is done when the last missing piece arrives, the rules are applied, and the supplier exists in the ERP with the right details. That can take a week, and most of that week is waiting.
No single agent owns that week. Each one can do its part when asked. The question is who asks, who remembers what is still outstanding, who notices that two sources disagree, and who decides the case is ready. That is the job of orchestration.
What an orchestration layer has to do
Strip away the vocabulary and an orchestration layer for agents has seven jobs. Miss one and the case falls back to a person with a spreadsheet.
| Job | What it means | What happens without it |
|---|---|---|
| Keep the case open | Case state persists for as long as the case takes: days or weeks, not one session | Context lives in a chat window or a prompt, and disappears when it closes |
| Remember who it is waiting on | Every outstanding item has a named owner (a person, a system or an agent) and a reason | Nobody can say how many cases are waiting, or on whom |
| Chase | Ask again on the channel the owner already uses, and resume the moment the answer lands | The case sits until somebody remembers it |
| Hand steps to executors | Route each step to the right agent, assistant, person or system, then check what comes back | One agent is stretched to do everything, or a person stitches the outputs together |
| Apply the same rules every time | Your rules run in a policy engine, not in a prompt | The outcome depends on how the model read the rules today |
| Write results back | The outcome lands in the systems of record: ERP, CRM, core system, ticketing | Someone re-keys the result, and the error rate comes back |
| Keep the record | What was read, who was asked, which rule and version decided it, what was written where | An auditor gets execution logs, and a guess |
None of those jobs is a task an agent is instructed to do. They are the process around the tasks. That is why adding a better agent rarely fixes a slow operation: the agent was never the bottleneck.
Multi-agent orchestration, concretely
For a technical reader, here is how Floowed does it. Floowed is an AI-native, multi-agent runtime for long-running asynchronous cases. Every case runs the same five phases: gather, interpret, chase, decide, act. Gather brings the case in, act sends the result out, and the middle three loop until the case is ready to decide.
Model-agnostic routing
No single model is right for every step. Reading a photographed bank letter, classifying an email, summarizing a contract clause and drafting a chase message have different cost, latency and accuracy profiles. The runtime is model-agnostic: it routes each step between open-weight and frontier models on cost, latency and data residency. When a better model ships, the step gets cheaper or better. Your operation doesn't change.
Long-running asynchronous cases
A case is not a request and a response. It is state that persists, suspends when it is waiting on something, and resumes when the answer arrives: an email reply, a webhook, a document upload, an agent's callback. A case can wait three days for a signature without holding a process open or losing its place. This is the part most agent frameworks leave to you, because it has nothing to do with the model and everything to do with the process.
Handing a step to an agent
Your agents, and the assistants your team already uses, can be executors inside a Floowed operation. Floowed hands the agent a step with the context it needs, the case waits, and when the result comes back it is checked against the other sources before the case moves on. An assistant is capable, but it improvises. Floowed gives it the step, holds the case open, and records what it did and the rule behind the next move.
API and MCP in both directions
Floowed exposes its operations over API and MCP, so your agents can open a case, add evidence to one or ask for a decision the way your team would. It also calls out over API, MCP and 400+ integrations to your agents, tools and systems. Orchestration that only works in one direction becomes one more silo.
A deterministic policy engine for the rules
Agents interpret. They read what arrived and work out what it says. The decision itself runs in a deterministic policy engine: given the same inputs and the same version of your rules, it gives the same outcome. We are careful about the scope of that claim. Reading a document or researching an entity is model work and is not deterministic; the rules applied to the result are. That split is what lets you put agents in a process that has to answer to an auditor.
A full record, and replay
Every case keeps what was read, who was asked and when, which rule and which version decided it, and what was written where. Because rules are versioned, you can replay a rule change on past cases and see what would have changed before you ship it.
Orchestration, automation and assistants are different layers
These three get blurred in vendor copy. They do different jobs, and a working stack usually has all three.
| Layer | What it does | Where it stops |
|---|---|---|
| Automation (Zapier, n8n, the rules inside your CRM or ERP) | Moves data when a trigger fires: route, retry, notify, log | When something is missing or unclear, or a person doesn't answer |
| Assistants and single agents | Complete a task they are instructed to do | At the edge of the job: they improvise the steps, don't hold a case open, and keep no record of which rule decided |
| Orchestration (Floowed) | Runs the case from intake to outcome across systems, agents and people | When the case is decided and the result is written back |
Automation runs a step. Floowed runs the case. It doesn't replace the automations you already have; if you're comparing the automation tools themselves, our n8n vs Zapier comparison covers that layer, and our Gumloop alternatives guide covers the AI agent builders.
Documents are one example of why this matters
Documents show the gap clearly, because they are where agents most often act on something they can't verify.
Most agent frameworks treat a document as a tool call: pass the file to a function, get JSON back, continue. The agent sees the call succeed and has no way of knowing whether the JSON reflects the page. On a clean PDF that is usually fine. On a bank statement photographed under poor light, a scanned passbook, a handwritten form or an ID from a country the model rarely sees, the output can be confidently wrong, and nothing downstream notices.
An orchestration layer treats the document as evidence inside a case, not as the end of a tool call:
- It reads the document in whatever state it arrives, and cross-checks it against every other source on the case: the registry, the system of record, the other documents.
- When sources disagree, it doesn't pick one. It raises the discrepancy, routes it to the right person or asks the sender for a clearer copy.
- When a document is missing, it chases it from whoever owes it, holds the case with the reason on the front, and resumes when the file lands. That is document collection done as part of the case, not as a separate inbox someone has to watch.
- It records what was read from which page, so the answer can be traced back to the source.
Documents are one of three input lanes. The same case might pull data from your systems over API and from external sources such as registries and bureaus, and all three feed the same decision. The orchestration is the same whichever lane the evidence came in on.
Where people fit
Orchestration is not the same as full autonomy. You choose how much Floowed decides on its own: every case, only the routine ones, or none, with every case prepared and handed to a person to decide. You can start with people deciding and widen the dial as you see the record. For how to design the review step itself, see our piece on human review gates in AI document operations.
People are also executors. When the missing piece is a judgement only your head of finance can make, Floowed asks them, in Slack or by email, and waits. That is the same mechanism it uses to wait on an agent.
What stays where it is
Your systems of record stay. Your automations stay. Your agents stay. Floowed sits above them as the operations platform: it runs the work between your systems and writes the result back into them. Your systems of record just stop being where the work happens.
Set-up is live in minutes. Describe the operation in Slack, Microsoft Teams or the Floowed Dashboard, and Floowed builds the operation: what to gather, what to check, who to chase, which rules decide, where the result goes. You can see every case and its record in the Floowed Dashboard.
If you are running agents and want something to run the process around them, start free with $80 of credits, no card and no sales call. If you'd rather walk through your own case with us first, talk to us.
Frequently Asked Questions
What is AI agent orchestration?
AI agent orchestration is the layer that coordinates agents, people and systems across a whole process. It hands each step to the right executor, keeps the case open while steps are outstanding, chases whoever still owes something, applies your rules, writes the result to your systems and keeps a record. The agents do the individual tasks; the orchestration layer runs the process around them.
What is multi-agent orchestration?
Multi-agent orchestration means different steps of one case are handled by different agents, each suited to its task, with one layer holding the case together. In Floowed, the runtime routes each step between open-weight and frontier models on cost, latency and data residency, and the case state lives outside any single agent, so it survives for as long as the case takes.
How is agent orchestration different from workflow automation?
Workflow automation tools such as Zapier and n8n move data when a trigger fires and are very good at it. They stop when something is missing, unclear or waiting on a person. Agent orchestration runs the case through those moments: it works out what is missing, chases it, resumes when it arrives and decides against your rules. Automation runs a step. Floowed runs the case.
Can I use my own agents with Floowed?
Yes. Floowed works over API and MCP in both directions. Your agents can open cases and ask for decisions, and Floowed can hand a step to your agent, wait for the result and check it against the other evidence on the case before moving on.
How do long-running asynchronous cases work?
The case state is persisted, not held in a session. When a case is waiting on a document, a reply or an agent's result, it suspends with the reason recorded. When the answer arrives, by email, webhook, upload or callback, the case resumes where it stopped. A case can wait days without losing its place.
Does orchestration make AI decisions deterministic?
Not end to end, and nobody should claim that. Reading documents and researching entities is model work and can vary. In Floowed the rules run in a deterministic policy engine: the same inputs on the same version of your rules give the same outcome, and every decision records which rule and version made it.
Where do documents fit in an agent orchestration setup?
Documents are one input lane alongside your systems and external sources. The orchestration layer reads them in whatever state they arrive, cross-checks them against the other sources, chases missing or unreadable ones, and records what was read from where. That is what stops an agent from acting on extracted data it had no way to verify.