Multicloud AI & ERP / Agentic AI & Cloud AI Infrastructure
Why it matters
Business teams know their processes best. Giving them safe tools to build agents spreads AI value quickly, but without guardrails it also spreads data exposure, duplicated agents and support headaches.
How it works
Maker workflow with IT guardrails
A business team describes the agent in plain language in a low-code builder, connects approved data sources such as SharePoint, Drive, CRM or tickets, and IT governance approves, limits and monitors it before it is published in chat, Teams, Slack or a portal.
AWS, Azure and Google Cloud compared
| AWS | Azure | Google Cloud | |
|---|---|---|---|
| Low-code agent builder | Amazon Quick | Copilot Studio | Agent Designer in Gemini Enterprise |
| Workplace assistant | Amazon Quick Suite | Microsoft 365 Copilot | Gemini Enterprise |
| Automation | Quick Automate | Power Automate | AppSheet, Workspace Flows |
| Data controls | Source permissions, admin scopes | DLP policies, environments | Admin controls, connector permissions |
| Agent inventory | Usage insights | Agent 365 | Agent gallery |
What makes each cloud different
AWS
Amazon Quick, which brings together the former Q Business and QuickSight, lets teams build agents, research and automate workflows over connected data while respecting source permissions.
Azure
Copilot Studio builds agents with natural language and Power Platform connectors, published into Teams and Microsoft 365 Copilot, with data loss prevention policies controlling which connectors can mix.
Google Cloud
Gemini Enterprise gives employees agents and search, with Agent Designer for no-code building, an agent gallery for sharing and a path to full ADK code when needed.
Design decisions that matter
- 01Approved connectors only
Makers choose from a governed list.
- 02Environments by sensitivity
Separate personal, team and production.
- 03Register and review agents
Central inventory with owners.
- 04Promote winners to IT
Popular agents get proper support.
Common mistakes we see
- No DLP policies. Agents mix confidential and public data.
- Unlimited publishing. Hundreds of overlapping agents.
- Blocking makers entirely. Shadow AI tools appear instead.
A practical 30-day plan
- Week 1
Set environments, connectors and DLP policies.
- Week 2
Train a pilot group of makers.
- Week 3
Build and publish the first agents.
- Week 4
Review usage and promote the best agents.
How NTS helps
NTS sets up governed low-code AI programs with Amazon Quick, Copilot Studio and Gemini Enterprise, covering environments, data policies, maker training and the path from prototype to supported agent.
What you get
- A governance model for Copilot Studio, Quick Suite or Gemini Enterprise agents
- Data access controls and connector policies
- Templates for common business agents
- Training for business makers
Typical outcomes
- Business teams solving their own problems
- No shadow AI with uncontrolled data access
- Measurable time saved across departments
How we work with you
- 1Assess
A focused review of your low-code AI and workplace agents across AWS, Azure and Google Cloud. We look for the gaps we find most often: no DLP policies, unlimited publishing and blocking makers entirely.
- 2Design
A target architecture and roadmap built on the decisions that matter most: approved connectors only, environments by sensitivity, register and review agents and promote winners to IT.
- 3Build
Hands-on delivery alongside your team. We set environments, connectors and DLP policies, train a pilot group of makers, build and publish the first agents, and finally review usage and promote the best agents.
- 4Run and improve
We review new agents, update policies and share best practices across makers.
Why teams choose NTS for low-code AI and workplace agents
- Empower without chaos
Business teams build agents within safe boundaries.
- Data protection first
We control which data agents can reach.
- Faster adoption
Templates and training get people productive quickly.
Book a free 30-minute review
Talk to an NTS architect about your low-code AI and workplace agents on AWS, Azure and Google Cloud.