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 announcementAgentic architecture design
An agent only where a step needs one
A workflow that runs with fewer agents to pay for and watch. Which steps get one and what each may touch is settled before you pay for the build.
50+ workflows agentified since 2017
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~20% → ~40% conversion to a final saleThree-agent product advisor guiding customers through print-order configuration.
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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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Up to 1 hr → ~2 min to create a workflowText-to-workflow agents building validated node graphs inside the platform.
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Supply chain intelligence agents reducing manual coordination overhead.
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AI agent implementation for a global automotive enterprise.
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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 architectures age badly
Teams pick the pattern before mapping the steps.
The bill comes later: model calls where lookups would do, state in prompts, no approvals.
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Where it stallsOne pattern covers the whole workflow.
What we do Each step: code, model call, agent or person.
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Where it stallsPermissions get decided mid-build.
What we do Permissions and approvals designed before any code.
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Where it stallsThe design is judged by the demo.
What we do Measured per step; rejected options written down.
How we design it
A design your team can defend
Written by the engineers who would build it, on your systems.
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Every step mapped and assigned
Rules stay in code, a model classifies, an agent takes open-ended work, costly errors go to a person.
- Step-by-step workflow map
- Error cost per step
- An owner for every step
42% for a raw LLM, 98% when the guideline sets the disposition, on the open 50-scenario benchmark. - 0203
The trade-offs, written down
Every choice is recorded with the options it beat, so your team can defend it.
- Decision record with trade-offs
- A reference design
- Model and cost per step
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Testable from sprint one
An evaluation plan sets test cases, metrics and thresholds per step, and every sprint is measured against it.
- Model you can swap
- Design files in your repository
- Evaluation plan per step
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
Agent architectures 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
What the design chooses between
Picked per step and recorded, with the model kept replaceable.
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- Single agent
- Multi-agent
- Deterministic workflow
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- Pydantic AI
- LangGraph
- Plain Python
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- RAG
- Qdrant
- pgvector
- PostgreSQL
- Redis
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- Self-hosted
- Your cloud
FAQ
Questions about agentic architecture design
What is agentic architecture design?
It is the set of decisions made before an agent system is built. They cover which steps run as code, model calls, agents or people, how the agents are orchestrated, where retrieval fits, how tools are typed and permissioned, what state the system keeps and which model runs each step. The output is a decision record, a reference design and an evaluation plan.
What does agentic architecture design include?
A step-by-step map of one workflow, with the cost of an error at each step. Every step then gets a decision: plain code, a model call, an agent or a person. The design then settles orchestration, retrieval, tool permissions, state, memory, human checkpoints and the model per step, with expected cost and latency. You get a decision record, a reference design and an evaluation plan with metrics per step. The build can follow as a separate phase.
Should we use one agent or several?
Start from the work. One agent with a small set of tools is easier to test and cheaper to run. Several agents make sense when the work splits into roles that need different tools, permissions or models. Many workflows are better served by a fixed workflow that calls a model at two or three steps.
When is a deterministic workflow better than an agent?
When the order of steps is known and the rules do not change. Code is cheaper, faster and easier to audit than a model choosing the next step. An agent earns its place where the next step depends on what the system finds, such as an unfamiliar document or an exception the rules do not cover.
Do you design around one framework or model provider?
No. The design names the framework and the model that fit each step and keeps the model replaceable. The decision record explains every framework choice, so your team can revisit it later.
What do we get at the end of the design?
An architecture decision record with the options considered and why each one won or lost. Alongside it, a reference design with components, data flow and tool contracts, and an evaluation plan with test cases and thresholds per step. All three go into your repository.
Can you review an architecture we already run?
Yes. We map the system as it runs, compare it with what the workflow needs and list the changes in order of cost and effect. If the system is struggling in daily use, an AI agent audit covers the same ground and adds a fix plan.
Who builds the system after the design?
Your team, ours, or both together. The design is written so another team can build from it. When we build it, the engineers who wrote the design do the work.
Design review
Bring one workflow to a design review
We split it into steps and show where an agent pays off.
From the blog
Reading on agent architecture
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.
Engineering
Agentic RAG: Architecture, Knowledge, Access Control
How agentic RAG changes architecture, knowledge and access control: corrective vs self-reflective designs, what they cost, and where permission checks belong.