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Multi-agent consultation
We map where a multi-agent system would pay off across departments, check each use case against single- and multi-agent options, and deliver an architecture and deployment roadmap before any code.
Multi-agent system development company
Multi-agent systems for workflows
a single agent cannot own.
Multi-agent system development for workflows too broad for one agent: specialist agents run them end to end. Success metrics are agreed before the full build, and the orchestration code goes to your team.
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~20% → ~40% conversion to a final saleThree-agent product advisor guiding customers through print-order configuration.
Read case study -
2 hrs → 3 min to generate a workflowText-to-workflow agents building validated node graphs inside the platform.
Read case study -
Every inquiry answered in English and Arabic, from ARIJ's own knowledge baseBilingual English/Arabic agent inside ARIJ's Moodle environment, answering only from ARIJ's own knowledge base.
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44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmarkHIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark.
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Supply chain intelligence agents reducing manual coordination overhead.
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AI agent implementation for a global automotive enterprise.
Why multi-agent projects stall
Most multi-agent systems copy the old org chart.
Adding agents is easy. Projects succeed or stall on where the work splits, who owns each handoff and what happens when one agent gets it wrong.
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Where it stalls: One agent per department, drawn along today's org chart.
What we do: We split where the redesigned process needs another skill or check, not by team.
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Where it stalls: Agents talk to each other freely, and no one can trace a decision.
What we do: An orchestrator owns the handoffs; every step between agents is logged and evaluated.
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Where it stalls: Every specialist is tied to one vendor's model and agent platform.
What we do: Each agent's model can be replaced, and the orchestration code lives in your repositories.
Choose the architecture
Does your workflow need more than one agent?
More agents mean more handoffs to test. We recommend the simplest architecture that covers the work, and explain why the alternatives lost.
Fits whenThe work crosses teams or needs research, validation and drafting as separate steps.
Fits whenThe workflow is bounded, and one agent with a handful of tools can own it.
Fits whenAnswers must come from your documents and systems, with the sources shown.
Fits whenPeople ask in a chat, and the agents behind it do the work.
How we work
From handoffs between teams to agents that hand off cleanly
We start with the handoffs between your teams and finish when your people run the agent network themselves.
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Handoffs mapped, then redesigned
We map every handoff, queue and re-check across teams, then redraw the workflow around the work, not the org chart. If one agent is enough, we say so before any build.
- Today's handoffs and queues, mapped
- Agent roles, orchestration pattern, escalation points
- Proof of Value with agreed success metrics
A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations. - 0203
Orchestration built by core contributors
Our engineers contribute to Pydantic AI and publish open-source agent tooling, so orchestration starts from patterns we already run. Every handoff is typed and traced, and a person takes over when an agent fails.
- Specialist agents with typed handoffs
- Per-agent and end-to-end evaluation suites
- Tracing across every handoff
- Fallbacks and escalation to a person
Engineers moved from hours of tedious setup to minutes, through multi-step validation rather than a single generation pass. - 0303
A model per agent, owned by your team
Each agent's model is replaceable (hosted, open-weight or a fine-tuned small language model), chosen per step and cost. The agent network runs in your cloud or on your servers, and your team owns it.
- Orchestration code in your repositories
- A model choice per agent, replaceable later
- Runbooks, governance and team training
- Nothing to license from Vstorm
Case studies
Multi-agent and agentic systems our clients run
Engagement formats
Six things a multi-agent engagement covers
From consultation and Proof of Value through MVW, delivery, audit, deployment and scale, with ownership transferred to your team.
See multi-agent case studies 02
Proof of Value
We build a working prototype in your real operational environment, test it on live scenarios, surface integration risks early and assess viability and projected ROI before full development.
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MVW to full solution delivery
A production-ready MVP scoped to your core use case, then the full multi-agent system wired into your infrastructure, tested end to end and handed over with documentation and training.
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System audit
We review your agentic or AI infrastructure: architecture, code quality, data security, regulatory alignment and observability. You get an audit report with prioritised fixes.
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Deployment
Agents go live on your timeline, in your environment: infrastructure configured, agents wired into your tools, end-to-end tests after deployment and launch support until issues close.
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Maintenance and scalability
Monitoring, model and integration updates, infrastructure scaling and priority support, so agents that work today keep working as your workflows and data change.
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 announcementOpen source
We build open-source agent tooling that other engineering teams install
9M+ PyPI downloads
3,900+ GitHub stars
31 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.
Engagement models
Three ways into a multi-agent build
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.
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Agentic process transformation
One team maps the cross-team workflow, redesigns it for agents, engineers the orchestration and trains your people to run it.
Best whenYou want one team accountable from the first workflow to the handover.
The TriStorm methodology - 02
Multi-agent system audit
We review architecture, code quality, data security and observability of agents you already run, and rank what to fix first.
Best whenYour agents work in a demo, but handoffs break in daily work.
Stalled pilot recovery - 03
Forward Deployed Engineering
Our engineers join your team to build and run the orchestration with you, and leave the knowledge behind.
Best whenYou have an engineering team and want multi-agent experience inside it.
Forward Deployed Engineering
Technology
The stack behind our multi-agent systems
We choose the stack per project and treat each agent's model as a replaceable part of the system.
- 01 Model Chosen per agent and replaceable
- Hosted model orOpen-weight model
- 02 Orchestration Typed agents and handoffs. Vstorm contributes to the Pydantic AI ecosystem.
- Pydantic AILangGraph
- 03 Retrieval For agents that answer from your documents
- LlamaIndexQdrantpgvector
- 04 Your environment Self-hosted or cloud, where your data already lives
- Self-hostedYour cloud
FAQ
Frequently asked questions about multi-agent systems
What is a Multi-Agent System (MAS)?
A multi-agent system consists of multiple agents (large language models autonomously using tools in a loop) working together. These agents collaborate, or sometimes compete, to solve problems that are too large or too complex for an individual agent or a monolithic system to handle.
How does an agent differ from a standard program?
A standard program follows a fixed set of instructions. Agents are systems where large language models dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks.
What is the difference between centralized and decentralized MAS?
Centralized: a single leader agent collects data and tells everyone what to do. Decentralized: control is distributed — agents make local decisions and coordinate through communication, so there is no single point of failure.
How do agents communicate with each other?
Agents use specialized protocols called Agent Communication Languages (ACL), such as FIPA-ACL or KQML. Unlike simple data transfers, these messages often convey speech acts like requesting, informing, or committing to a task.
What is "emergent behavior" in MAS?
Emergent behavior occurs when simple local rules followed by individual agents result in complex, sophisticated global patterns. A classic example is flocking behavior in birds, or the way traffic jams form without a central cause.
Can agents in a MAS be competitive?
Yes. While many multi-agent systems are collaborative, working toward a shared goal, others are competitive — made up of self-interested agents. Competitive systems are often studied using game theory to predict how agents behave when their goals conflict.
What are the main benefits of using a Multi-Agent System?
Scalability — you can add more agents as the problem grows. Fault tolerance — if one agent fails, others can take over. Specialization — different agents can be designed to handle specific niches, such as one for data retrieval and one for analysis.
What is "stigmergy"?
Stigmergy is a form of indirect coordination where agents communicate by modifying their environment. Think of ants leaving pheromone trails: the next ant follows the trail not because it talked to the first ant, but because the environment was changed.
What are the biggest challenges in MAS?
Coordination is the hardest part. Communication overhead (too much talking, not enough doing) conflict resolution, and keeping the global system stable are constant hurdles for developers.
Start with one workflow
Which of your workflows needs more than one agent?
Bring one workflow that crosses teams. We map the handoffs, test the split on your data in a Proof of Value, and you decide on the full build with the numbers in front of you.
From the blog
Reading on multi-agent systems
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.
Agentic AI
Building Production-Grade AI Agents: How We Brought Deep Agent Patterns to Pydantic
Vstorm delivered pydantic-deep - a comprehensive framework for building "deep agents" that can operate in isolated environments.
Agentic AI
AI agents can act: can you still control them?
Agents that issue refunds need identity, authorisation, approval and audit before the call lands. What an agentic AI control plane is, and when you need one.