Kagent

Kagent is an open-source programming framework and Kubernetes-native platform for building, running and managing AI agents. Instead of requiring bespoke infrastructure, the framework turns agents into standard Kubernetes custom resource definitions (CRDs) that teams can deploy, scale and secure using their existing GitOps pipelines. This allows teams proficient in Kubernetes to build custom agents without needing to learn a new operational model. Kagent provides a built-in runtime with OpenTelemetry, secure sandboxing, memory support and native Model Context Protocol (MCP) integrations to easily connect agents with external tools and model providers.

We've moved Kagent into Trial after successfully using it to support production environments. At a recent client, we transitioned a multi-agent support bot to Kagent from a previous tool that restricted connections to a single MCP server and required hosting on a personal account. Because the client's team was already familiar with Kubernetes, the migration was simple. The resulting deployment features an orchestrator and multiple specialist agents that use Kagent's built-in Agent-to-Agent (A2A) Protocol to communicate, helping developers debug incidents and solve general questions directly within team channels.

Kagent is an open-source framework for running agentic AI inside Kubernetes clusters. It enables LLM-based agents to plan and execute operational tasks such as diagnosing issues, remediating configurations or interacting with observability tools through Kubernetes-native APIs and Model Context Protocol (MCP) integrations. Its goal is to bring "AgentOps" to cloud-native infrastructure by combining declarative management with autonomous reasoning.

As a CNCF Sandbox project, Kagent should be introduced with care, particularly given the risks of granting LLMs operational management capabilities. Techniques such as toxic flow analysis can be especially valuable when assessing and mitigating these risks.