Platform
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A production MCP server for one of your systems, with up to 8 tools, secure sign-in, action logging and tests in AI…
Platform
AI Agent Orchestration System
Multiple AI agents work one complex task together, each with a defined role, handing off automatically to the next step.
Complex AI tasks often need several agents, one to research, one to draft, one to check, and stitching them together by hand gets fragile fast. AI Agent Orchestration System coordinates those agents in one runtime, with defined roles and handoff rules. It tracks usage, latency and cost for every run. You own the configuration in your own version control.
- Agents hand work to each other by rules you define
- Usage, latency and cost tracked for every workflow run
- Works across model providers, with no lock-in
$6,900
20–30 working days
Optional maintenance $345/mo
What it is
AI Agent Orchestration System coordinates multiple AI agents so they work together on one complex task, handing work between agents, tools and steps without you scripting each handoff by hand. It tracks usage, latency and cost per workflow, across whichever providers your agents run on.
Why businesses buy it
Multi-agent workflows built from scripts and glue code tend to break quietly. A handoff fails, an agent loops, costs climb, and nobody can see where it went wrong. After this build, your agents run in one coordinated runtime with clear roles, explicit handoffs and per-run tracking, so the workflow is predictable and easy to inspect. You can swap a provider for one role without rebuilding the rest. The Scope Card sets the systems, controls and handoff rules in writing before you buy, so the scope is clear.
Problems this solves
Every handoff between agents is scripted and patched by hand
The Orchestration System coordinates several agents on one complex task. It hands work between agents, tools and steps, so your developers stop hand-writing and maintaining each handoff.
A multi-agent task fails and nobody knows which step broke
Each step in the workflow is tracked in the admin console or written report. When something stalls, your team can see where it stopped and which agent held it.
Running agents on several providers makes cost hard to follow
Usage, latency and cost are tracked per workflow across whichever providers your agents run on. Up to three systems or AI applications are covered in this build.
A good fit if
You already run two or more AI agents that need to hand work to each other, for example a research agent feeding a drafting agent.
Your workflow has steps where different agents specialize, such as one agent that retrieves data and another that acts on it.
You want one runtime that tracks usage, latency and cost across the whole multi-agent workflow, not just a single agent in isolation.
You need agent configuration kept in version control, so a change to the handoff logic is reviewed like any other code change.
Not the right fit if
You are running a single agent that does not hand work to other agents. See AI Agent Monitoring Platform, which watches one or more deployed agents without adding a coordination layer.
You need visibility into what one agent does internally rather than how several agents pass work between each other. See AI Agent Observability.
You need a person to approve steps before they run, rather than agents coordinating automatically with each other. See Human Approval Workflow.
Your workflow needs more than five controls configured, or a fully custom multi-agent runtime. Use our Custom AI enquiry form instead.
How it works
- We map your multi-step task into discrete agent roles. For example a retrieval agent, a reasoning agent and an execution agent, each with a defined job and a defined handoff point to the next.
- The orchestration runtime is built vendor-neutral, so it runs against OpenAI, Google Gemini, AWS Bedrock or Azure OpenAI, and can call more than one provider inside the same workflow.
- Handoffs between agents are configured with explicit rules: what one agent passes to the next, what triggers a retry, and what happens when an agent cannot complete its step.
- Usage, latency and cost are tracked per workflow, not per agent alone, using tools such as Langfuse or Grafana, so you see the full multi-agent run as one connected unit.
- Agent roles, prompts and guardrails are kept in version control, so a change to how agents hand off work goes through the same review as any other code change you ship.
- We test the orchestration against your agreed scenarios, produce a test report, and hand over an admin guide plus a recorded session covering how to adjust agent roles later.
What you provide within 72 hours
Your portal lists these the moment you order. Delivery starts the working day after everything is in and your access is checked.
- Your business information (company basics, hours and brand voice), shared during intake, or confirmation we can reuse what you gave us on an earlier order.
- The AI providers and API accounts your agents currently run on.
- Cloud or hosting access for wherever the orchestration runtime will run.
- A technical owner who can answer questions about your existing agent setup.
- Written authorization for us to test the connections between agents and systems.
- The list of AI applications and systems each agent needs to reach.
- Admin access and API keys for every connected system, shared by adding our team as a user or creating a key you control, never by email.
- One decision-maker who can approve the final orchestration rules and handoff logic.
The 72-hour rule. Send everything on your checklist, including access, within 72 hours of ordering so your build starts on time.
Timeline
Start
business information confirmed and each agent's role in the workflow agreed.
Weeks 1–2
Agent roles, prompts and handoff rules built and connected to your chosen providers.
Week 3
Systems and AI applications in scope connected, with usage and cost tracking wired in.
Weeks 3–4
The orchestration is run against your agreed scenarios and a test report is produced.
Week 4
Your review and two rounds of changes to agent roles or handoff rules.
Go-live
Live, with a recorded handoff session and the 30 days of support beginning.
| Option | Delivery time | Price |
|---|---|---|
| Standard | 20–30 working days | $6,900 |
| Express | 15–25 working days | $7,950 |
| Rush | 10–20 working days | $8,950 |
What you receive at handover
- AI Agent Orchestration System configured and deployed, coordinating agents across up to three systems or AI applications
- Workflow, prompt and guardrail configuration for agent handoffs, with up to five controls
- Usage, latency and cost tracking per workflow in an admin console or written report
- A test report with a control test log for the agreed scenarios
- An admin guide and a recorded 60-minute handoff session
- 30 days of post-launch support
Expected benefits and how to measure them
- Can let agents take on longer tasks without constant developer attention.
- Helps you find the failing step in a multi-agent task faster.
- Gives a clear view of cost and delay for each workflow.
- Workflow completion
- Compare finished runs with started runs each week, using the orchestration report.
- Time to diagnose a failed run
- Record how long it takes to find the cause of a failure, before and after launch.
- Cost and latency per workflow
- Read both from the report monthly and watch how they change as steps are tuned.
Before you start: List the multi-step tasks your agents handle and how handoffs work today. Note recent failures and how long each took to trace.
These are things to track, not promised results. Outcomes depend on your data, your team and your market.
Running costs
The build price is a one-time cost. Running the orchestration afterwards costs roughly $20 to $300 a month in your own accounts, covering hosting plus the logging and monitoring tools that track each agent's usage.
AI model usage during build, testing and afterwards is also billed directly to your own provider accounts, so you keep every provider relationship rather than routing it through us.
Ask about running costsCustom requirements
If your workflow needs more than five controls, or agents that reach beyond three connected systems, add a note at checkout. You get a written Change Quote, and your original order keeps its normal timeline. Larger or bespoke projects use the Custom AI request form instead.
Request a custom versionKeep it running
For this build we recommend monthly maintenance: monitoring, fixes and a monthly allowance of change hours, which covers new agent roles or adjusted handoff rules as the workflow evolves. It starts after the 30 days of support and can be cancelled any month.
$345/mo (5% of the build price a month, includes 3 change hours)
Optional. Starts after the post-launch support period. Cancel any month.
How maintenance worksHow this compares
| Do it yourself with a SaaS tool | Hire a custom agency | Upmark Labs | |
|---|---|---|---|
| Price | A workflow platform charges a recurring seat or usage fee | A custom multi-agent build is quoted after discovery | Ours is one fixed price, shown on this page before you buy |
| Scope | A workflow platform leaves you to wire up each agent yourself | An agency agrees scope during the project | Ours is written to your agents and handoffs before you order |
| Delivery | A workflow platform still needs real configuration before it runs | An agency build usually takes several weeks, set per project | Ours has a fixed delivery time shown before you buy, with faster options |
| Visibility | A workflow platform's metrics depend on what you instrument yourself | An agency build depends on what was agreed | Ours tracks usage, latency and cost per workflow from day one |
Questions about this product
How much does AI Agent Orchestration System cost?
The fixed price for each delivery speed is shown in the price and delivery panel on this page, so you see it before you order. It covers everything on the Scope Card: coordinating agents across up to three connected systems, up to five controls or handoff rules, per-run tracking of usage, latency and cost, and the control test log you review before launch.
What does AI Agent Orchestration System cost to run each month?
Running it afterwards costs roughly $20 to $300 a month in your own accounts, mostly hosting plus the logging and monitoring tools, such as Langfuse or Grafana, that track each agent's activity. There is no separate license fee on top of that figure. AI model usage from providers such as OpenAI or Google bills separately, directly to your own provider accounts.
How many agents can AI Agent Orchestration System coordinate?
The build covers up to five controls, policies or monitors configured across your agent workflow, which in practice covers most multi-agent setups with a handful of distinct roles. If your workflow needs more agents or handoff rules than that, we quote the extra work as a Change Quote before it starts, so nothing gets built without your sign-off first.
Does AI Agent Orchestration System work with more than one AI provider?
Yes. The orchestration runtime is built vendor-neutral, so a single workflow can call OpenAI, Google Gemini, AWS Bedrock or Azure OpenAI, depending on which agent needs which model. You are not locked into one provider, and a provider for a specific agent role can be swapped later without rebuilding the whole multi-agent workflow.
What access does AI Agent Orchestration System need to my systems?
We need admin access and API keys for every system each agent connects to, shared by adding our team as a user or creating a key you control, never by email, plus cloud or hosting access for wherever the runtime will run. A technical owner who understands your current agent setup makes the intake faster, since most questions concern existing configuration and accounts.
Can we change agent roles or handoff rules later?
Yes. Small adjustments to an existing agent's role or a handoff rule usually fit inside the monthly change hours that come with maintenance. Adding a genuinely new agent role, or connecting a fourth or fifth system beyond the three in scope, goes through a Change Quote agreed in writing before that extra work begins on your workflow.
Can we cancel the AI Agent Orchestration System maintenance?
Yes. Maintenance has no lock-in, so you can cancel any month once the 30 days of included support has ended. Unused hours do not roll over to the next month. If your workflow settles down and needs fewer changes, you can decide whether you still need it.
Who owns the orchestration configuration once it is built?
You do. Agent roles, prompts and handoff rules are kept in version control that you hold, not a platform we control, so the whole configuration stays visible and editable by your own team. We hand over an admin guide and a recorded session covering how each part works, and nothing about the setup stays locked to us.
Stop scripting agent handoffs by hand.
Order AI Agent Orchestration System and your agents are coordinated in one workflow, live on a fixed delivery date.
Works well with
