Autonomous single agents that own
one bounded workflow end-to-end.
Single-agent system development for one bounded workflow, run by an agent instead of by hand, with a go or no-go before the full build and its code, prompts and monitoring left with your team.
Bilingual English/Arabic agent inside ARIJ's Moodle environment, answering only from ARIJ's own knowledge base.
Read case study
HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark.
Read case study
Text-to-workflow agents building validated node graphs inside the platform.
Read case study
Three-agent product advisor guiding customers through print-order configuration.
Read case study
Supply chain intelligence agents reducing manual coordination overhead.
AI agent implementation for a global automotive enterprise.
Most first builds should be a single agent. We check that in the first phase and tell you when the workflow needs more.
Fits whenClear inputs, manageable data sources and a handful of tools.
Fits whenThe work crosses teams or needs several specialists in parallel.
Fits whenAnswers must come from your documents and systems, with the sources shown.
Fits whenCustomers or employees ask in a chat, and some questions need a person.
An AI agent is an autonomous software system that perceives its environment, makes decisions, and takes actions to achieve a defined goal, without being told what to do at every step.
A standard program executes the sequence you wrote. An agent runs a reason–act loop: it is equipped with tools (APIs, databases, search, code), and decides which tool to call, when, and how to interpret the result before shipping the outcome.
Kingdom buyers scoping the same one-workflow build start at AI agent development in Saudi Arabia.
A first agent that holds up on real cases earns the budget for the next. That comes down to scope, tool design and what happens after launch.
The agent gets a remit as wide as a department.
We scope one bounded workflow and mark which decisions stay with a person.
Tools are wired in at the end, around whatever the model can already do.
Tools come first: they turn a general model into a specialist for your workflow.
Accuracy is checked once, at the demo.
Monitoring and feedback run from launch, because agents drift as data and workflows change.
Scoping, build and handover stay with one team, so the agent your people inherit is the one that was tested.
We map the workflow as it runs today, every hop between inbox, CRM and spreadsheets, and drop steps that only moved data. Then we mark where a person reviews or approves the agent.
From one percent of journalist inquiries answered to all of them. Our engineers contribute to Pydantic AI, so the agent starts from typed tools, guardrails and evaluation suites we already run. Decisions it should not make alone go to a person; every tool call is traced.
Guideline-executing medical triage: the LLM answers triage assessment questions; disposition follows the guideline deterministically. The model is one replaceable part, and the agent can run fully self-hosted with no external API calls. Run traces and drift checks show when accuracy slips, and we or your people act on them.
Open standards, open-source tooling and published research, all public, so you can check our engineering before you hire us.
Shaping agentic AI
The Linux Foundation formed it around Anthropic's MCP, OpenAI's AGENTS.md and Block's goose. AWS, Google and Microsoft are platinum members. In its own words: “Together, we're shaping the future of agentic AI.”
Open source
Framework contributors
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
Peer-reviewed papers on agent architectures, evaluation and AI security, and an open benchmark anyone can rerun against our results.
Track record
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.
"The team at Vstorm was very helpful, their insight and experience helped us greatly in our project. They were very professional at every step of the way and made the whole process feel seamless."
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.
One team scopes the workflow, redesigns it, engineers the agent and trains your people to run it.
Best whenYou know which workflow should go first and want one team to the handover.
Scope one high-value workflow, see the agent work on your data in a Proof of Value, and keep the code when it goes live.
A per-workflow buy vs build framework for agentic AI: four paths, a difficulty score from 4 to 12, data sensitivity, model choice and lock-in.
One agent or several? Deterministic orchestration beats both when the flow is predictable. Pros, cons and the architectures, with production examples.
Reliable agents share one property that rarely appears in vendor decks. What we standardised across production deployments, and why it holds.