Knative
infrastructureAdopt
Knative is our core serverless platform for deploying AI agents and automation workflows. It provides scale-to-zero capabilities that dramatically reduce infrastructure costs while maintaining enterprise-grade reliability.
Why Knative is crucial for AI agents:
- Scale-to-Zero: Agents scale down to zero when idle, reducing costs by 70-80%
- Event-Driven: Native support for event-driven agent workflows and triggers
- Auto-Scaling: Intelligent scaling based on request volume and custom metrics
- Container Native: Deploy any containerized agent or workflow without vendor lock-in
- Production Ready: Battle-tested in enterprise environments with comprehensive monitoring
AI-specific benefits:
- Cost Optimization: Pay only for actual agent processing time, not idle resources
- Burst Scaling: Handle sudden spikes in agent requests automatically
- Multi-Tenancy: Isolate different agent workflows and teams safely
- GPU Support: Scale GPU-enabled agents for intensive AI workloads
- Cold Start Optimization: Optimized for AI container startup times
Integration with our platform stack:
- Kubernetes Foundation: Built on our existing EKS infrastructure
- Istio Service Mesh: Automatic mTLS and traffic management for agent communication
- Prometheus Monitoring: Track agent performance, costs, and resource usage
- External Secrets: Secure injection of API keys and credentials for agents
- ArgoCD GitOps: Automated deployment and configuration management
Deployment patterns we use:
- Stateless Agents: ReAct agents that process individual requests
- Event Processors: Agents triggered by business events (document uploads, user actions)
- Scheduled Workflows: Cron-like agents for periodic automation tasks
- Multi-Agent Systems: Coordinated agents with event-based communication
Best practices:
- Optimize container images for fast cold starts
- Use health checks and readiness probes for reliable scaling
- Implement circuit breakers for external service dependencies
- Monitor token usage and costs with custom metrics