From Idea to Scalable Deployment: Building an AI Agent Application on Kubernetes

Building the Foundation with Infrastructure as Code

Instead of manually creating servers or clusters, we relied on Terraform. This approach allowed us to define infrastructure in a declarative way, ensuring that environments can be recreated consistently. Whenever changes are needed, we simply update the code, keeping the whole setup version-controlled and predictable.

Kubernetes as the Core Orchestrator

The AI agent was deployed on a Kubernetes cluster. This wasn’t just a default deployment — we configured: 

• Cluster networking with load balancers and ingress controllers 
• Namespace and RBAC policies for secure multi-tenant operations 
• Pod-level scaling rules for handling different traffic patterns 

By leveraging Kubernetes, we ensured the AI agent could scale horizontally as demands grew.

Helm for Repeatable Deployments

To simplify and standardize application rollouts, we packaged deployments into Helm charts. This enabled us to: 

• Reuse templates across environments 
• Quickly roll back when needed 
• Keep configuration clean and modular 

In practice, this meant the same AI agent could be deployed to dev, staging, or production with minimal changes. 

Service Mesh with Linkerd

Running microservices in Kubernetes comes with a challenge: how do you monitor and secure traffic between services? We introduced Linkerd, a lightweight service mesh, to handle this. With it, we gained: 

• mTLS encryption by default between services 
• Traffic insights at a per-service level 
• A foundation for progressive delivery strategies (like blue-green or canary deployments) 

Observability with Prometheus

An AI system without monitoring is like driving blindfolded. To make the system observable, we configured Prometheus to scrape and collect metrics from Linkerd. This gave us real-time dashboards with: 

• Service response times 
• Request success/failure rates 
• Resource utilization trends 

This visibility helped us identify bottlenecks and optimize performance early. 

CI/CD Pipeline on GitHub Actions

Deployment speed was a key priority. We created a GitHub Actions pipeline that automated:

• Code build and test runs 
• Docker image creation and push 
• Helm-based deployments into Kubernetes 

With this pipeline, every code commit triggered a deployment process, reducing manual work and ensuring consistency across environments.  

Security & IAM Considerations

Security wasn’t an afterthought. We configured IAM roles and RBAC policies in Kubernetes to restrict permissions. Each service had just enough access to do its job, following the principle of least privilege. This reduced the attack surface and kept the environment compliant with security best practices.

The Impact – Why This Matters

By combining Terraform, Kubernetes, Helm, Linkerd, Prometheus, and GitHub Actions, we created an environment where: 

• Deployments are automated 
• Scaling is seamless 
• Monitoring is built-in 
• Security is enforced at every layer 

The AI agent now runs in a self-healing, observable, and production-ready system.