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 announcementKnowledge layer for AI agents
Every agent reads from the same company knowledge
When a senior expert leaves, their know-how should stay. Vstorm builds the knowledge layer your people and AI agents share: documents, data and your team's know-how in one current source that respects your permissions. Every agent reads it over MCP, with skills for how your team works.
50+ workflows agentified since 2017
-
~20% → ~40% conversion to a final saleThree-agent product advisor guiding customers through print-order configuration.
Read case study -
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.
Read case study -
Up to 1 hr → ~2 min to create a workflowText-to-workflow agents building validated node graphs inside the platform.
Read case study -
Supply chain intelligence agents reducing manual coordination overhead.
-
AI agent implementation for a global automotive enterprise.
-
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 agents answer like strangers
Each agent gets its own copy of the company.
Every new agent re-indexes the same documents, with its own permissions and its own gaps.
- 01
Where it stallsEvery new agent indexes the same documents again.
What we do We index your sources once, in one layer every agent reads.
- 02
Where it stallsAn agent shows data its user should not see.
What we do We check source permissions on every read.
- 03
Where it stallsAnswers quote last year's price list.
What we do We sync sources when they change and date every answer.
- 04
Where it stallsA senior expert leaves and the know-how goes with them.
What we do We capture procedures and decisions as wiki pages and skills everyone can read.
How we build it
Knowledge structured once, served to every agent
Sources, context layer, wiki and skills, built as one system you own.
- 0103
Where your knowledge lives
We map the sources your agents need, from documents and tickets to databases and the know-how nobody wrote down.
- Source map with owners
- Access rules per source
- Gaps to write down
- 0203
A context layer every agent reads
Content is cleaned, linked and kept current in a company wiki that models maintain and people review. Agents read it over MCP, with your permissions.
- Permission-aware context layer
- LLM wiki, reviewed by people
- One MCP server for all agents
Automotive dealer group Service cost estimates draw on a knowledge graph of past repairs, and a critic agent checks each one before parts are priced. - 0303
How your team works, as skills
Procedures your experts follow become skills: instructions, tools and examples an agent loads when the task needs them.
- Skills from your procedures
- Tested on your own cases
- Versioned with the layer
42% for a raw LLM, 98% when code applies the guideline, on an open 50-scenario benchmark.
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 systems 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.
FAQ
Questions about knowledge and context layers
What is a knowledge layer for AI agents?
It is the part of your system that holds what agents need to know about your company and serves it to all of them the same way: documents, records from your databases, decisions and procedures. Each piece carries its source, its date and who may see it. Without it, every new agent gets its own index of the same documents, with its own gaps and its own permission rules.
Is a knowledge layer the same as a context layer?
The two names describe the same job from different sides. Context layer stresses what reaches the model on each call: the right facts, within the token budget, with the user's permissions applied. Knowledge layer stresses what is stored and maintained underneath. We build both as one system: a maintained store of company knowledge and the context assembly that feeds each agent from it.
Is a knowledge layer the same as company memory or a company brain?
Company memory and company brain name the result: what the company knows stays available when people change roles or leave. The knowledge layer is the engineering behind it: connected sources, a maintained wiki, skills and permissions that people and agents read the same way. Two of our open-source projects come from this work: AgenticOS, which manages skills for a whole organization, and memv, structured and temporal memory for AI agents.
How is this different from RAG?
RAG is a retrieval technique, usually built for one application. A knowledge layer is shared infrastructure: sources are connected once, cleaned, linked and kept current, and every agent reads from it through the same interface with the same permissions. Retrieval is one of the ways agents read from the layer.
What is an LLM wiki, and do we need one?
An LLM wiki is a set of pages that a language model writes and keeps up to date from your raw sources: one page per product, policy or customer, with links between them and a source behind every statement. Agents read compact, current pages instead of hundreds of raw files. It helps when the same knowledge is scattered across many documents. People review changes to the pages that matter, and the raw sources stay the record.
How do agents get access to the knowledge layer?
Through an MCP server, so any agent or assistant that speaks Model Context Protocol can search the layer, open a page or fetch a record with one set of tools. The server checks the user's permissions on every call and logs what was read, so an agent cannot see more than the person it works for. MCP is an open standard, governed by the Agentic AI Foundation since December 2025, so the layer does not tie you to one model or agent vendor.
How do you keep the knowledge current?
Sources are synced when they change, so the layer does not wait for a nightly rebuild. Sources that cannot report changes are synced on a schedule you set. Every page and chunk carries the date of its source, outdated pages are flagged for review, and contradictions between sources go to the owner of that content. Answers show the date of what they quote, so users can see how fresh it is.
What are agent skills, and can you build them for us?
A skill packages one way your team works, such as how you handle a refund, prepare a quote or check a contract, as instructions, tools and examples that an agent loads when the task calls for it. We write skills from your procedures and your experts' cases, test each one on real examples and keep them versioned next to the knowledge layer, so a change in the procedure changes the skill.
How do you handle permissions and sensitive data?
Permissions come from the source systems and are checked when the agent reads, so a document visible only to HR stays visible only to HR. Personal data is mapped before anything is indexed, and you decide what stays out of the layer. The layer can run in your own cloud account.
Do we need a knowledge layer for our first agent?
Not always. One agent over a small set of documents can start with plain retrieval. A shared layer pays off with the second or third agent, when the same sources, permissions and updates would otherwise be built again for each one. We usually design the first agent so its sources can move into a shared layer later.
What do we own at the end?
The source map, the pipelines that keep the layer current, the wiki pages, the MCP server, the skill library and the tests, all in your repository and your cloud account. Your team can add sources, edit skills or connect new agents without us.
Knowledge layer review
See what your agents should know
Bring your agent plans and your main sources. We map what a shared layer would hold and what to connect first.
From the blog
Reading on knowledge, context and access
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.
Engineering
Why RAG is not dead: a case for context engineering over massive context windows
Retrieval-Augmented Generation (RAG) and context windows enhance AI accuracy—discover how to optimize your AI workflows effectively
Agentic AI
Lesson 2: Why data without context is worthless
Database records capture outcomes, not the reasoning behind them. Why AI projects need context capture and data experts from the architecture stage.