knative-serving

Deploy AI agents as Knative Services with autoscaling and traffic splitting.

2|Updated Dec 14, 2025

Search Tags:#serverless #kubernetes #revisions #autoscaling #knative #ksvc #traffic-splitting

Ready to run

npx skills add https://github.com/raphaelmansuy/k8s-agent-stack --skill knative-serving

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: knative-serving
Source: https://github.com/raphaelmansuy/k8s-agent-stack/tree/main/archive/skills/knative-serving
Command: npx skills add https://github.com/raphaelmansuy/k8s-agent-stack --skill knative-serving

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve?

This Skill helps you deploy AI agents as serverless workloads using Knative Serving, enabling automatic scale-to-zero and request-based autoscaling, which reduces costs and operational complexity.

Core Features & Use Cases

Serverless deployment & autoscaling: Create Knative Services with traffic splitting and revisions to manage lifecycle and scale.
Traffic management: Configure routing, canary or blue-green deployments, and gradual rollouts.
Use Case: Deploy a customer-support agent that scales to zero during off-hours and rapidly scales up with demand.

Quick Start

Use the Knative Service manifest templates to deploy a sample agent (e.g., customer-support-agent) with autoscaling enabled.

Frequently Asked Questions about knative-serving

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I deploy serverless workloads with automatic scaling on Kubernetes?▼

Knative Serving enables serverless deployment on Kubernetes with request-based autoscaling and scale-to-zero capability. Create a Knative Service manifest specifying your container image, set autoscaling policies (concurrency-based or CPU-based), and Knative automatically scales instances up with demand and down to zero during idle periods.

Can I use traffic splitting and canary deployments with Knative Services?▼

Yes. Knative Serving supports traffic splitting across revisions, enabling canary deployments, blue-green rollouts, and A/B testing. Define traffic percentages in your service spec to gradually route requests to new revisions while monitoring performance before full cutover.

What autoscaling policies does Knative Serving support?▼

Knative Serving supports concurrency-based autoscaling (scale on concurrent requests), CPU-based autoscaling, and custom metrics. Configure autoscaling through annotations and containerConcurrency settings in your service manifest to match your workload's scaling behavior.

How do I configure timeouts and resource limits for Knative Services?▼

Set timeoutSeconds, containerConcurrency, and resource requests/limits in your Knative Service spec. These control request timeout behavior, concurrent request handling per container, and CPU/memory allocation, ensuring predictable performance and cost.

Do I need to configure readiness probes for Knative Services?▼

Readiness probes are optional but recommended for Knative Services to signal when your container is ready to receive traffic. Configure them in your pod spec to prevent traffic routing to unhealthy instances, improving reliability during scale events.

What's the difference between using Knative Serving versus traditional Kubernetes deployments for scaling?▼

Knative Serving provides request-driven autoscaling and scale-to-zero, reducing costs during low-traffic periods. Traditional Kubernetes deployments scale based on resource metrics but maintain minimum replicas, making Knative ideal for variable-demand workloads like AI agents.