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 是一个在 Kubernetes 集群中运行智能体的开源框架。它使基于大语言模型（LLM）的智能体能够通过 Kubernetes 原生 API 和 Model Context Protocol（MCP）集成，规划并执行运维任务，例如诊断问题、修复配置或与可观测性工具交互。其目标是将“AgentOps”引入云原生基础设施，将声明式管理与自主推理相结合。 作为一个 CNCF Sandbox 项目，Kagent 的引入需要谨慎，特别是在赋予 LLM 运维管理能力时所带来的潜在风险。诸如 AI 有害流分析（toxic flow analysis） 等技术在评估和缓解这些风险时尤其有价值。