Multi-agent systems for work too big for one agent, with every step traceable
A multi-agent system is a group of AI agents, each with its own instructions, tools and job, coordinated by an orchestrator or by defined hand-offs to complete work too broad for one agent. It beats a single agent when a task has distinct parts that need different tools, data or checks, or that can run in parallel. Sysvoy builds multi-agent systems on LangGraph, CrewAI, the OpenAI and Claude agent SDKs or plain code, with explicit hand-offs, evaluation per agent and full tracing.
One task, start to finish
An orchestrator splits the job, specialised agents work through your tools, and the uncertain cases go to a person.
- taskCheck 120 supplier contracts for auto-renewal terms
- planOrchestrator splits the job across four agents
- reader120 PDFs parsed from Documents
- analyst23 auto-renewal clauses found
- checker2 unclear clauses sent to a person
- writerSummary with 23 citations, CRM updated
- done4 min 12 s, every step logged
Where multi-agent systems development projects stall
And what we do differently.
Too many agents too early
Splitting a task across five agents before one works multiplies the failure points. We start with one and add agents when measurements say so.
Agents that loop or argue
Without stop conditions, agents pass work back and forth or re-check each other without end. Ours have explicit hand-offs, step limits and one owner per decision.
Costs nobody can trace
Every agent call adds tokens, and without traces nobody can say where the money or the error went. We trace every call and set a budget per run.
What we build
Orchestrator and specialist agents
A lead agent breaks the task down, sends each part to an agent with the right tools and data, checks the results and assembles the answer.
Research and analysis pipelines
Agents that gather evidence from several sources, correlate it and write findings an analyst can check, the pattern behind the security platform we built for a European cybersecurity company.
Document review crews
One agent extracts, another checks against policy or precedent, a third drafts the summary, and every claim links to its source passage, as every answer does on midpage.ai.
Agents that hand off to people
Defined points where the system stops for a person to approve, correct or decide, then carries on with that input.
Agent systems on your infrastructure
Agents, tools and open-weight models deployed in your cloud or on your servers when data can't leave your environment.
How we deliver
From the first workshop to production. How we work
One agent first
We build a single agent, measure it, and split it only where a separate agent fixes a measured problem: different tools, different data, or work that can run in parallel.
Explicit hand-offs and shared state
Each agent has a written contract: what it receives, what it returns and when it stops. State lives in one place, not in each agent's chat history.
Evaluation per agent and end to end
Each agent gets its own test set and the whole system gets another, so a drop in quality points to the agent that caused it.
Budgets and traces
Limits on steps, tokens and time for every run, and a trace of every model and tool call, so cost and failures show up before users notice.
A single agent compared with a multi-agent system and workflow automation
| Workflow automation | Single agent | Multi-agent system | |
|---|---|---|---|
| How steps are chosen | Fixed in advance | The agent decides as it goes | An orchestrator assigns parts to specialist agents |
| Cost and speed per task | Lowest cost, fastest | Moderate | Highest cost, more model calls |
| Debugging | Easy | Manageable with traces | Hardest, needs tracing across agents |
| Best for | Identical, high-volume tasks | One task that needs judgement | Complex work with distinct parts, tools or checks |
A good fit when
- A single agent handles simple cases but breaks when a task needs several tools and checks.
- Your process already has distinct roles, such as gather, analyse, review and approve.
- You have a multi-agent prototype that costs too much, loops, or nobody can explain.
What we build with
- Python
- TypeScript
- LangGraph
- CrewAI
- OpenAI Agents SDK
- Claude Agent SDK
- Microsoft Agent Framework
- MCP
- LangSmith and Langfuse
- Commercial and open-weight LLMs
Related work
AI security platform
AI-driven security platform and SOC tooling for a European cybersecurity company.
Client under NDAmidpage.ai
AI research platform that layers analysis over case law for litigators and legal teams.
midpage.ai ↗AI commerce assistant
Self-hosted AI shopping assistant, Typesense product search and HubSpot integration for a US B2B distributor.
US distributor
Planning a budget? Read our guides
- AI agent development costReal price ranges from a studio that builds AI agents, with the timelines, running costs and trade-offs behind them.
- RAG development costWhat a retrieval-augmented generation system costs to build and run, from a studio that ships them. Price tiers, monthly infrastructure, build vs buy and two real examples.
Common questions
Not answered here? Email contact@sysvoy.com.
When do you need a multi-agent system instead of a single agent?
When one agent has to juggle too many tools, too much context or steps that need different checks, and its accuracy drops as a result. Splitting the work gives each agent a narrower job it can do reliably. If a single agent already passes your evaluation set, keep it: it's cheaper, faster and easier to debug.
How much does a multi-agent system cost?
Most multi-agent systems cost $15,000–$40,000 and take 6–12 weeks. We often start with a single agent at $6,000–$15,000, measure it on real tasks, and add agents only where the results show they're needed. Running costs depend on model choice and volume, and we set a budget per run during design.
How long does it take to build a multi-agent system?
Usually 6–12 weeks to production. The first weeks go to mapping the workflow, building the evaluation set and getting one agent working; the rest go to specialist agents, hand-offs, integrations and testing. Larger systems run in fixed-price phases: for a European cybersecurity company, whose platform also includes AI voice agents, we delivered an MVP first and then an industrialisation phase.
Which framework do you use: LangGraph, CrewAI or something else?
Whichever fits the workflow and your team. LangGraph suits systems that need explicit control flow and saved state, CrewAI suits role-based crews, and the OpenAI Agents SDK and Claude Agent SDK suit systems built around one model provider. Sometimes plain Python or TypeScript with no framework is the simplest option, and we'll say so.
How do you stop agents from looping or running up costs?
Every run has hard limits on steps, tokens and time, and every agent has a clear stop condition. Hand-offs are explicit, so agents don't pass work back and forth indefinitely. Tracing shows the cost of each run, and an alert fires when a run goes over budget.
Can a multi-agent system run on our own infrastructure?
Yes. We deploy to your cloud account or your own servers, and use open-weight models served with tools such as vLLM or Ollama when data can't leave your environment. Agents reach your systems through scoped APIs or MCP servers, with the permissions you set.
How do you test a multi-agent system?
Each agent is tested on its own task against real examples with expected results, and the full system is tested end to end on complete cases. We rerun both on every change to prompts, models or tools, so a regression is traced to the agent that caused it.
Have something to build?
Tell us where things stand and what you need. A short message is enough to start.