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 integration services
Agents wired into the systems your team already uses
Less copying between systems. Agents read and write in your CRM, ERP and ticketing, each tool with its own credentials and every call logged.
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 integrations break
Integrations break on the write, rarely on the read.
Duplicates after timeouts, admin keys that can delete, changes nobody can trace.
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Where it stallsOne service account can touch everything.
What we do Each tool gets its own scoped credentials.
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Where it stallsFree text goes straight into your systems.
What we do Inputs are checked before every call.
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Where it stallsA timeout retry creates a second order.
What we do Retries do not duplicate; failures undone or flagged.
How we integrate
Each tool limited to the job it does
We start from your systems as they are, API or not.
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Your systems, inventoried
We list every system the workflow touches and rate each action by what a mistake would cost.
- Systems and access paths
- Approvals per risky action
- A route without an API
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Tools with contracts and limits
Each tool checks its inputs before the call and can touch only what its job needs.
- Typed contracts and MCP servers
- Safe retries and rollback
- Triggers from your systems
Mixam's product advisor acts through tools; conversion to a final sale rose from about 20% to about 40%. - 0303
Every call on the record
Each call is logged with its run, inputs, result and approver; credentials stay in your secret store.
- Connector code and MCP servers
- Runbooks for the next system
- Audit log your team reads
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 working inside our clients' systems
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 AI agent integration
What does AI agent integration include?
An inventory of the systems and actions the agent touches. Then typed tool contracts or MCP servers, scoped credentials per tool, retries and rollback for failed writes, event triggers, and an audit trail of every call. Connector code and runbooks stay with your team.
Which systems can you connect an AI agent to?
Any system the agent can reach through an API, a database, an event stream, files or, as a last resort, its user interface. The usual ones are CRMs, ERPs, ticketing and service desk tools, email, document stores and internal databases.
What is an MCP server, and do we need one?
The Model Context Protocol is an open standard for describing tools and data to AI agents. An MCP server wraps one of your systems so any compatible agent can use it through the same contract. It pays off when several agents or teams will use the same system; for a single agent, a direct typed tool is often simpler.
Can an agent work with a legacy system that has no API?
Yes. We look for a database view, a scheduled export or a file drop first. If none exists, the agent can work through the system's interface, with tighter checks and a person reviewing the writes, because that path breaks more easily when the screen changes.
How do you control what the agent is allowed to do?
Each tool gets its own credentials and the narrowest scope that does the job. Reading and writing are separate tools, and actions that are hard to undo, such as payments, deletions or messages to customers, can wait for a person's approval before they run.
What happens when a call fails halfway through?
Writes are made idempotent, so a retry does not create a second record: idempotency keys where the API supports them, a lookup before the write where it does not. Rate limits are respected with backoff. When a change that spans several steps fails partway, the earlier steps are reversed with compensating actions or flagged for a person. The run is logged either way.
Where does the integration run, and who owns it?
In your environment, with credentials in your own secret store. The connector code, MCP servers, tool contracts and audit logs stay with your team, together with runbooks for adding the next system.
Connect one system
Pick the first system your agent connects to
Bring the workflow and its systems; we show how each one connects.
From the blog
Reading on agents and tools
Engineering
Prompt injection is not the vulnerability. Your agent’s tools are.
Prompt injection will get through. AI agent security depends on what the agent can do when it does: break the lethal trifecta and enforce limits in code.
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.
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.