We won the Open Agentic AI Innovation Award at the first AAIF Technical Excellence Awards.We won the Open Agentic AI Innovation Award Read why

Multi-agent orchestration with one supervisor in charge

A supervisor agent that hands work to specialists

For Yeeld we built a 6-agent supervisor architecture in which a lead agent splits each request and calls only the specialists it needs.

Book a free consultation How we build agents
account-requests

  1. Account managerYour team

    A customer wants their order history, a forecast for next quarter and a reply today. Can the agents split it?

  2. Supervisor agentAgent

    1. router.plan three tasks, three specialists
    2. agents.call data, forecast and reply agents

    Split into three tasks and sent to the data, forecast and reply agents. The other agents stay idle.

  3. Supervisor agentAgent

    1. results.merge history, forecast and draft
    2. checks.validate figures match the source data

    One reply built from the three results. It includes a forecast for a customer, so it needs your sign-off.

    Needs a personSend a forecast to a customerDraft reply with the order history
    Approved by Account manager

  4. Reply agentAgent

    1. crm.log_reply reply and each agent's trace

    Reply sent from your mailbox. The steps of every agent are saved in one trace.

Example data. An agent at work in a team channel, with a person approving the step you choose.
Mixam: conversion to a final sale from about 20% to about 40%
Where agents help

One agent doing everything gets slow and hard to trust.

A supervisor splits the work across specialist agents, and code keeps each one inside its job.

  1. 01

    Where it stallsOne prompt tries to cover every task and drifts.

    What we do Each specialist agent has one job, its own tools and its own tests, so changing one leaves the others intact.

  2. 02

    Where it stallsEvery request runs the whole system and costs too much.

    What we do The supervisor calls only the specialists a request needs. The other agents stay idle.

  3. 03

    Where it stallsWhen an answer is wrong, nobody sees which step failed.

    What we do Every handoff, tool call and result is traced, so you see which agent did what.

What we build

What we build for multi-agent orchestration

Whatever comes in, every project is built from the same parts below, set up for your files and your systems.

Work we split across agents

  • Research and reporting
  • Order and quote flows
  • Document review
  • Customer conversations
  • Data analysis
  • Code and QA tasks
  • Compliance checks
  • Back-office processing

How we build it

  • Supervisor and specialist design

    Which agents you need, what each one may do and how the supervisor routes work between them.

  • Specialist agents with their own tools

    Agents for data, documents, actions or conversation, each with its own tools, prompts and limits.

  • One reply from several agents

    The supervisor calls only the agents a request needs and merges what they return into one answer.

  • Tests and traces for every agent

    Each agent is tested on its own cases, and every run is traced from the request to the answer.

Not seeing your case? Tell us the process you want agents to share and we start from that.

Tell us about your workflow
How we build agents

Agents that work as part of your team

They work in the tools your team already uses, take on whole tasks and stop for a person where you decide.

  1. Routines: agents that start on a schedule, on new GitHub issues or on an API delivery.
  2. Connected systems: GitHub, Linear, Notion, Slack, Jira and other tools an agent may call, each connected with its own sign-in.
  3. Approvals log: each action that waited for a person, with who asked, who decided and when.
Routines: agents that start on a schedule, on new GitHub issues or on an API delivery.
Connected systems: GitHub, Linear, Notion, Slack, Jira and other tools an agent may call, each connected with its own sign-in.
Approvals log: each action that waited for a person, with who asked, who decided and when.

Screens from the workspace our agents run in, with example data.

Where it runs

Teams and industries that use it

Each page shows where agents fit in that team or industry.

Engineering in the open

We help build what agentic AI runs on

Open standards, open-source tooling and published research, all public, so you can check our engineering before you hire us.

Shaping agentic AI

Agentic AI Foundation

Vstorm was the first agentic AI engineering company to join the Agentic AI Foundation and received its 2026 Open Agentic AI Innovation Award.

Read the announcement

Open source

We build open-source agent tooling that other engineering teams install

10M+ PyPI downloads
4,000+ GitHub stars
33 public repositories

Framework contributors

When the framework breaks, we fix it at the source

We maintain our own open-source agent framework and contribute fixes to the frameworks our agents run on, through official partnerships with their teams. Breaking changes reach us before they reach your codebase.

Research

Our engineers and PhDs publish peer-reviewed research

Peer-reviewed papers on agent architectures, evaluation and AI security, and an open benchmark anyone can rerun against our results.

Track record

Frontier AI teams now build on what we shipped first

Our client agents were in production before most of today's agent frameworks had a stable release. You get a team that already knows what breaks between a promising demo and a system people rely on every day.

Case studies
All case studies
Engagement models

Ways to start

Each path starts with a free call with a founder or senior AI engineer. A build goes ahead only after its Proof of Value meets the agreed threshold.

Start with one workflow

We map one workflow with your experts and agree a success threshold before any build.

Best whenYou want the success test agreed before you commit to a build.

Book a free consultation
  1. Process redesign and build Best whenYou want one team accountable for the outcome.
FAQ

Multi-agent orchestration, answered

What is multi-agent orchestration? +
Multi-agent orchestration is how several AI agents share one job. A supervisor agent reads the request, splits it into tasks, sends each task to a specialist agent with its own tools and combines the results into one answer. Code sets the rules: which agent may do what, how many retries it gets and when the work stops for a person.
When do you need more than one agent? +
When one agent has to cover tasks that need different tools, data or tone, and its prompt keeps growing. Typical signs are answers that drift, tool calls that fail more often as tools are added and a cost per request that keeps rising. If one agent with a few tools does the job well, keep one agent, because every extra agent adds handoffs to test.
Have you built multi-agent systems before? +
Yes. For Yeeld, a UK fintech, we built a 6-agent supervisor architecture on LangChain and LangGraph: a supervisor, 2 coaching agents with opposite attitudes, an assistant that answers from Yeeld's own documents and a financial agent on banking APIs. Only the agents a request needs run. For a US manufacturer, a main agent hands database questions to a SQL sub-agent that checks each query.
How do you keep agents from looping or contradicting each other? +
Each handoff has a typed input and output, so a specialist cannot pass on a result the next step cannot read. Retries are capped in code, and a run that hits the limit stops with a clear error. When agents disagree on a fact, the supervisor sends the question to a person. Every step is traced, and failures go back into the test set.
How do you know it works before the full build? +
We agree a success threshold with your team before the build and test the agent on your own cases in a Proof of Value. It measures accuracy and running costs, and the build goes ahead only when the threshold is met. Corrections from your team go back into the test set, so every later version is checked against them.
Who owns the system, and which models does it run on? +
You own the code, prompts, evaluation sets and rules by contract. The agents run in your environment on hosted or open-weight models. The model sits behind an interface in code, so you can change it when accuracy, speed or your hosting rules change, without rebuilding the agent.
Start with one workflow

Test one workflow on your own data

A Proof of Value shows accuracy and running costs before the full build.

Book a free consultationRead case studies
From the blog

Reading on multi-agent systems

All articles
Agentic AI

Single agent vs multi-agent systems: when one agent is not enough

One agent or several? Deterministic orchestration beats both when the flow is predictable. Pros, cons and the architectures, with production examples.
Engineering

From Search and Summarize to Multi-step Reasoning: Understanding Agentic RAG

n this article, we perform a detailed churn investigation scenario to illustrates the distinction between traditional RAG and Agentic RAG
Agentic AI

One Dashboard, Full Visibility: Why We Use Logfire in Our Stack

When people ask us about Logfire, they usually think AI agents. Monitoring PydanticAI workflows, tracking token usage, debugging LLM calls.