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Enterprise AI & Agentic AI Governance · Success story

Scaling an AI agent platform from one server to enterprise-grade

A self-recovering AWS architecture, centralized AI-provider governance, and automated delivery replaced a fragile single-server deployment.

Clientbairq.ai
0single points of failure
<1 mindeployment time
CentralizedAI credential governance
Continuousarchitecture and security checks

Selected AWS services

Core services in the architecture.

A focused view of the AWS services most representative of this engagement. The complete technical scope is described below.

  1. 01Amazon ECS
  2. 02Elastic Load Balancing
  3. 03AWS Secrets Manager
  4. 04AWS CodePipeline
  5. 05Amazon CloudWatch
  6. 06AWS Config
01

The challenge

bairq.ai’s agent platform ran on one server, leaving the service vulnerable to a single failure. Deployments were manual and credentials for external AI providers were not governed centrally.

02

What CloudVests delivered

CloudVests rebuilt the platform on a multi-zone, self-recovering AWS architecture, centralized credential governance across AI providers, and automated the release workflow.

Amazon ECS distributed the agent services across multiple Availability Zones behind health-aware load balancing. AWS Secrets Manager centralized AI-provider credentials and removed secret material from deployment artifacts and host configuration.

AWS CodePipeline created a repeatable release path, while CloudWatch and AWS Config exposed runtime health and configuration drift for continuous review.

03

The outcome

The platform gained resilient operations, controlled model-provider access, continuous architecture checks, and rapid, repeatable deployments.

The modernization created explicit boundaries between agent logic, provider credentials, deployment automation, and infrastructure controls—reducing the blast radius of both application and operational failures.