Several agents, one operation:
coordinated instead of colliding
A business running several AI agents, one for support, one for reporting, one for data entry, usually ends up with agents that do not know about each other, duplicate work, or leave a task half-done at the boundary between them. We build an orchestrator agent that routes work to the right specialist, carries state across the handoff, and keeps a clear audit trail of which agent did what.
The process today
A business that has added AI agents one at a time, a support bot here, a reporting script there, a content pipeline somewhere else, usually ends up with agents that operate in isolation. A customer’s request might get answered by the support agent without that agent knowing a fulfilment agent already flagged the same order as delayed, so the customer gets two different, slightly contradictory answers from the same business within an hour.
The second cost is duplicated or dropped work at the boundary between agents. A task that should flow from a qualification agent to a scheduling agent can stall if nobody built the handoff, or get picked up by both if nobody built the conflict check, and either failure mode is invisible until a customer or a manager notices something went wrong.
The third is that nobody has one place to see what every agent across the business actually did today. Each agent might log its own actions, but without a shared view, diagnosing why an outcome happened means checking several systems and reconstructing the sequence by hand.
What the agent does
We build a router agent that sits in front of your specialist agents, decides which one should handle an incoming piece of work based on rules your team defines, and hands it off with the full context attached, so the receiving agent does not have to re-ask the customer or re-derive what already happened. State carries through the whole chain: if a task passes through three agents before it is done, all three see the same history.
Where two agents could plausibly act on the same task, a conflict resolution rule decides which one takes it, and anything the rule cannot clearly resolve routes to a human instead of letting both agents act independently. Every handoff and decision is written to a shared audit log, so a manager can see the exact sequence of agents and actions behind any outcome.
Typical integrations: whatever messaging, CRM, or internal tools your existing or planned specialist agents already use, with the orchestrator layered on top as the coordination point.
What stays with humans
Defining what each specialist agent is responsible for, and what counts as a conflict between them, is a decision your team makes and can revise at any time. Anything outside the scope the orchestrator and specialists were built for escalates to a person rather than being forced through the nearest matching agent. Reviewing the orchestrator’s routing decisions periodically, and adjusting the rules as your operation changes, stays an ongoing task for your team, with the audit log supplying the data to do it from.
Guards
Every routing decision, handoff and specialist action is logged centrally, so nothing happens across the agent system without a traceable record. Rate limits and budget caps apply per agent and across the whole system, so a malfunctioning agent cannot run away with cost or volume. The orchestrator runs in a shadow mode against real incoming work before it is allowed to make live routing decisions, and a kill switch can pause any single agent or the entire system in one message.
Price and timeline
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| Single automation | from $1,500 | Router agent, 2 to 3 specialist agents, shared audit log | 2 to 4 weeks |
| Department package | from $4,500 | Full orchestration across 5+ agents, conflict rules, dashboard, escalation design | 6 to 10 weeks |
Running cost is usually $50 to $200 a month in model usage depending on task volume and agent count, with a budget cap set before launch.
Related
This pairs well with agent approval queue and audit log for the governance layer on top of the orchestration, and with agent cost and quality monitoring for keeping the whole system healthy over time. See the AI agents service page and the automation-everything overview for full package details. For real builds on multi-agent systems and multi-channel sales coordination, see the ProBay own marketplace AI agent team case study and the seven-channel AI sales agent case study.
Running several agents that do not talk to each other? Get in touch and we will map how your current agents and processes actually connect.
Tired of doing this by hand? We can take the whole routine off your team, not just this step: Routine takeover, from $400 →
FAQ
How much does it cost to set up multi-agent orchestration?
From $1,500 for a router agent and two to three specialist agents working together, live in 2 to 4 weeks. Larger operations with five or more agents and complex handoff rules usually run $3,000 to $6,000.
How long before it is live?
2 to 4 weeks, depending on how many existing agents or processes need to be wired into the orchestrator and how much state needs to pass between them.
Do I need to already have AI agents in place?
No. We can build the specialist agents and the orchestrator together, or wire an orchestrator around agents and scripts you already have running.
What happens when two agents could both act on the same task?
A conflict resolution rule your team defines decides which agent takes it, or routes it to a human if the rule does not clearly resolve it, rather than letting both agents act and create a duplicate or contradictory outcome.
Is the whole system auditable?
Yes. Every handoff, decision and action across every agent is logged in one place, so you can trace any outcome back through the exact sequence of agents that touched it.