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 announcementAI agent deployment and infrastructure
Your agents, running where your data lives
Agents that go live in your own cloud account, built to pass your security review, with load issues found before deploy and your team running them.
50+ workflows agentified since 2017
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AI agent implementation for a global automotive enterprise.
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Supply chain intelligence agents reducing manual coordination overhead.
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~20% → ~40% conversion to a final saleThree-agent product advisor guiding customers through print-order configuration.
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Up to 1 hr → ~2 min to create a workflowText-to-workflow agents building validated node graphs inside the platform.
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42% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmarkHIPAA-compliant guideline-executing triage — 42% to 98% on the open 50-scenario benchmark.
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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.
Why agent deployments stall
The demo ran on one laptop, one API key.
A notebook agent hits security review, data residency and real traffic at once.
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Where it stallsAPI keys in a developer's .env file.
What we do Secrets in your vault, scoped per service.
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Where it stallsThe agent runs on a vendor platform.
What we do In your cloud account or private network.
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Where it stallsNo rollback plan, nobody on call.
What we do One-step rollback, runbooks for your on-call.
How we deploy
AI agent infrastructure you run yourself
Environment review first, on-call handover last.
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Your environment, mapped first
We start with your cloud: network, identity, data residency and the security review.
- Design ready for security review
- Model choice per workload
- Running costs estimated upfront
ARIJ's agent runs inside ARIJ's own Moodle and answers only from ARIJ's knowledge base. - 0203
Built to take real traffic
We start from our open-source template, the same layout we run in our own systems.
- Containers in your CI/CD
- Queues and workers that scale
- One interface for any model
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Run by your team
Each release keeps the last one ready, and we rehearse runbooks with your team before handover.
- Infrastructure code in your repository
- Rollback plans for data migrations
- No license or platform fees
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.
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
Agents our clients run
Engineering workflows built in minutes, validated before they run
The agent drafts each workflow and checks it in several steps before it runs, so engineers skip the manual setup.
Up to 1 hrto prepare a workflow by hand
~2 minto create one with the agent
A print-order advisor that lifted conversion to a final sale
Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations.
~20% → ~40%conversion to a final sale
96%+web-to-print orders handled end to end
Clinical triage that executes the guideline, step by step
The model answers the assessment questions. The guideline decides the disposition, the same way every time.
42% → 98%accuracy, raw LLM vs guideline-executing
96%weighted F1 across 566 scenarios
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.
Technology
The AI agent infrastructure stack
Picked for your environment, with the model as a replaceable part.
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- vLLM
- llama.cpp
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- Docker
- Kubernetes
- FastAPI
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- AWS
- Azure
- GCP
FAQ
Questions about deploying AI agents
What does AI agent deployment and infrastructure include?
It includes running the agent in your cloud account or private network (VPC): containers and CI/CD, secrets in your vault, hosted or open-weight model hosting, queues and scaling workers, rollback and runbooks, and on-call handover to your team. The infrastructure code stays in your repository.
Can AI agents run in our own cloud or on-premises?
Yes, in your cloud account or a private network (VPC), under your identity, logging and data residency rules. A fully on-premises setup, with an open-weight model on your own hardware, is scoped per project and tested on your cases first.
Should we use a hosted model or an open-weight model?
Use a hosted model to start quickly and for hard tasks; use an open-weight model on your hardware when data must stay inside your network or volume is high enough for it to cost less. We decide per workload, often with both in one system, and test the choice on your own cases.
How do you handle secrets and access to our systems?
Keys and credentials go into your secrets vault, never into the code or the repository. Each service gets only the access it needs, and the agent's calls to your systems go through the identity and network rules your security team already applies.
What happens when a release goes wrong?
The previous release stays ready, so rolling back is one step, and we rehearse it with your team before go-live. Alerts and runbooks tell the person on call what to check and when to roll back.
How is this different from your LLMOps service?
This service sets up where and how the agent runs: environment, containers, secrets, model hosting, scaling and handover. LLMOps covers the ongoing release of prompt and model changes on top of that, with evaluation gates, staged rollouts and cost tracking.
Can you deploy an agent another team built?
Yes. We review the code and its dependencies, package it for your environment, move secrets out of the code and add the pipeline, monitoring and runbooks it lacks. If it needs changes to run reliably under load, we say so before deploying it.
Who operates the agent after handover?
Your team. Infrastructure code, pipelines and runbooks live in your repositories, on-call moves to your people, and there is no license or platform fee to Vstorm. We can stay on for support if you want us to.
Deployment and infrastructure
Decide where your agent will run
Infrastructure code in your repository, rollback rehearsed with your team before handover.
From the blog
Reading on running agents
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.
Agentic AI
Lesson 3: System access is a hard constraint
Why system access (VPNs, API keys, ERP exports) belongs on the critical path of an agentic AI project, with a named owner and deadline.
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.