Kubernetes serves as the robust foundation of the C3 Agentic AI Platform’s infrastructure, providing powerful capabilities for deploying, scaling, and managing containerized services. This enterprise-grade orchestration system enables the platform to deliver consistent performance, reliability, and security across diverse deployment environments.Documentation Index
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Kubernetes as the foundation
The C3 Agentic AI Platform runs on Kubernetes, an industry-standard container orchestration system that automates the deployment, scaling, and management of containerized applications. This architecture provides several key benefits:
The diagram above shows how the C3 AI Infrastructure Layer uses Kubernetes to manage resources. This containerized approach enables:
- Consistent deployment across cloud providers and on-premises environments
- Automatic scaling based on workload demands
- Self-healing capabilities that recover from failures
- Resource optimization through efficient allocation of compute resources
Core components
The C3 Agentic AI Platform on Kubernetes consists of several key components that work together:Containerized services
Each functional component of the C3 Agentic AI Platform runs as a containerized service, enabling independent scaling and updates.
Persistent storage
Kubernetes persistent volumes store application data, ensuring data survives container restarts and migrations.
Service mesh
Manages service-to-service communication, providing load balancing, service discovery, and security features.
Ingress controllers
Route external traffic to the appropriate services within the Kubernetes cluster.
Deployment models
The C3 Agentic AI Platform supports multiple deployment models to meet different organizational needs:Cloud deployments
The platform can be deployed on major cloud providers including AWS, Azure, and Google Cloud. Each deployment leverages cloud-native services while maintaining a consistent application experience:- AWS deployments use EKS (Elastic Kubernetes Service) with integration to services like S3, RDS, and CloudWatch
- Azure deployments use AKS (Azure Kubernetes Service) with integration to Azure Blob Storage, Azure SQL, and Azure Monitor
- Google Cloud deployments use GKE (Google Kubernetes Engine) with integration to Cloud Storage, Cloud SQL, and Cloud Monitoring
On-premises deployments
For organizations with specific security or compliance requirements, the C3 Agentic AI Platform can be deployed on-premises:- Uses the same Kubernetes architecture as cloud deployments
- Supports enterprise Kubernetes distributions like Red Hat OpenShift
- Integrates with existing storage and networking infrastructure
- Provides consistent management interfaces across deployment models
Resource management
Kubernetes enables efficient resource management for the C3 Agentic AI Platform:Compute resources
The platform allocates CPU and memory resources based on workload requirements:- Request-based allocation ensures each component has the resources it needs
- Limit-based protection prevents any single component from consuming excessive resources
- Quality of Service (QoS) classes prioritize critical services during resource contention
Scaling mechanisms
The C3 Agentic AI Platform scales automatically to handle varying workloads:- Horizontal Pod Autoscaling (HPA) adds or removes container instances based on CPU, memory, or custom metrics
- Vertical Pod Autoscaling (VPA) adjusts resource requests and limits based on actual usage
- Cluster Autoscaling adds or removes nodes to accommodate changing resource demands
Monitoring and health checks
Kubernetes provides robust mechanisms for monitoring the health of the C3 Agentic AI Platform:Pod health checks
Kubernetes uses two types of health checks to ensure services are functioning properly:- Liveness probes detect when a service is running but unable to make progress, triggering a restart
- Readiness probes determine when a service is ready to accept traffic, preventing premature routing
Resource monitoring
The platform includes comprehensive monitoring of resource usage:- Node-level metrics track CPU, memory, disk, and network usage across the cluster
- Pod-level metrics monitor resource consumption of individual services
- Application metrics provide insights into the behavior of C3 Agentic AI Platform components
Logging infrastructure
Centralized logging captures information from all platform components:- Container logs collect output from application services
- System logs track Kubernetes events and operations
- Audit logs record administrative actions for security and compliance
Disaster recovery and high availability
The Kubernetes architecture provides built-in resilience for the C3 Agentic AI Platform:High availability configurations
- Multi-zone deployments distribute workloads across multiple availability zones
- Pod anti-affinity ensures critical services run on different physical nodes
- StatefulSet replication maintains database and service state across instances
Backup and recovery
- Automated backups of persistent data and configuration
- Point-in-time recovery capabilities for databases
- Cross-region replication for disaster recovery scenarios
Security considerations
The C3 Agentic AI Platform on Kubernetes implements multiple layers of security:- Network policies control communication between services
- Role-Based Access Control (RBAC) restricts administrative access
- Secret management securely stores and distributes credentials
- Pod security policies enforce container security best practices
Related concepts
Observability and Monitoring
Learn about monitoring tools and practices for the C3 Agentic AI Platform.
Platform Architecture Fundamentals
Understand the architectural principles that structure the C3 Agentic AI Platform.
Platform Administration
Explore administrative tasks for managing the C3 Agentic AI Platform.