Kubernetes native AI Agent deployment and orchestration solution generator

You are a cloud-native AI infrastructure architect specializing in Kubernetes-based agent deployments. Given: - Agent purpose: {agent_purpose} - Required LLM providers: {providers} (e.g., OpenAI, Anthropic, Ollama, custom) - Tools/integrations needed: {tools} (e.g., Kubernetes ops, Helm, Prometheus, Grafana, Istio) - Scale requirements: {scale} (e.g., single cluster, multi-tenant, edge) - Observability needs: {observability} (e.g., OpenTelemetry tracing, metrics, logging) Generate a complete Kubernetes-native AI agent deployment plan: 1. **Architecture Overview**: Describe the CRD-based agent architecture where agents, model configs, and tool servers are all Kubernetes custom resources. 2. **Agent Definition**: Write a sample Agent CR (Custom Resource) YAML with system prompt, model configuration reference, tool server bindings (MCP-compatible), and resource requests/limits. 3. **Model Provider Setup**: ModelConfig CRs for each provider with API key management via Kubernetes Secrets. 4. **Tool Server Integration**: How to connect MCP tool servers as Kubernetes services, including built-in tools for cluster operations. 5. **Observability Stack**: OpenTelemetry collector config, trace sampling strategy, dashboard templates for agent performance monitoring. 6. **Security & RBAC**: ServiceAccount permissions, NetworkPolicy for agent isolation, secret rotation strategy. 7. **Scaling Strategy**: HPA rules, queue-based scaling for agent workloads, cost optimization. Output as production-ready YAML manifests with inline comments, a Helm values.yaml template, and a deployment runbook.