AgentiQX

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Low-code platform for building governed enterprise AI agents and workflows

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AgentiQX

What it is

AgentiQX is an enterprise platform for building AI agents and autonomous workflows with a drag-and-drop designer. Business analysts can assemble flows from pre-built components, while developers can add Python or JavaScript for custom logic and integrations. It has two parts: a Designer for building and testing agents and a Runtime that customers host themselves, so data stays under their own governance. Agents can be published as an API, an embedded widget or an MCP server, and the platform works with cloud, on-premise or private LLMs. Showcased use cases target banking and insurance, such as claims processing, underwriting and credit decisioning. It is offered by TransformHub through demos.

Key features

Drag-and-drop agent designer
Python and JavaScript customization
Deploy as API, widget or MCP server
Cloud, on-premise or private LLMs

What we liked

+ Customer-hosted runtime keeps data in house
+ Works across multiple model providers
+ Industry templates for finance and insurance

Worth noting

· No public pricing
· Aimed at enterprises rather than individuals

Editor's notes

More context from AI BirdView

Many agent builders assume your data can move to their cloud. AgentiQX takes the opposite position: the company provides the tooling, while customers host the runtime, datasets and models, which is a sensible fit for regulated industries such as banking and insurance. The site backs this up with detailed walkthroughs of real workflows rather than generic demos.

A visual canvas lets non-developers design flows, with code-level customization available for complex logic.
Finished agents can be exposed as an API, an embeddable widget or an MCP server for other agents to call.
Deployment is cloud agnostic, covering AWS, GCP, Azure or on-premise installations.
Ready-made solution patterns cover claims automation, motor underwriting, credit decisioning, delinquency detection and insurance support chat.
Security controls described on the site include identity and access management, network isolation and audit logging.

It is best for banks, insurers and other large organizations that want to build AI agents while keeping strict control over data and infrastructure. A practical tip: book a demo around one of the published use cases closest to your own process, since that is the quickest way to judge how much configuration your team would need.