Step-by-step tutorials on building voice and chat AI agents using OpenAI Agents SDK, Realtime API, function calling, multi-agent orchestration, and production deployment patterns.
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Learn how to manage AI agent configuration with Kubernetes ConfigMaps and Secrets — including environment injection, volume mounts, secret rotation, and best practices for API key management.
Configure Kubernetes Horizontal Pod Autoscaler for AI agent workloads using CPU, memory, and custom metrics. Learn KEDA integration and scale-to-zero patterns for cost optimization.
Use Kubernetes Jobs and CronJobs to run batch AI agent workloads — including parallel document processing, scheduled report generation, and completion tracking with backoff policies.
Implement service mesh patterns for AI agent architectures using Istio and Linkerd — including traffic splitting for canary deployments, automatic retries, circuit breaking, and observability.
Learn how to use Kubernetes Persistent Volumes, PersistentVolumeClaims, and StorageClasses to manage stateful AI agent workloads including vector stores, conversation logs, and model caches.
Design Kubernetes Network Policies to secure AI agent communication — including namespace isolation, egress restrictions to LLM APIs, and deny-all defaults with explicit allow rules.
Build Helm charts for AI agent deployments — including chart structure, values files, Go templates, dependencies, and chart repositories for reusable, parameterized Kubernetes manifests.
Build a Kubernetes Operator for AI agent lifecycle management using Custom Resource Definitions, reconciliation loops, and status management to automate agent provisioning and scaling.
