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ctxmesh

Introduction

ctxmesh is a Kubernetes-native platform for building, governing, and operating AI agents at scale. You describe an agent declaratively as a Kubernetes custom resource, and the platform provides the production substance around it: serving and autoscaling, model routing and cost governance, content guardrails, human-in-the-loop approvals, eval-gated rollout, feedback capture, and deep tracing.

Why ctxmesh

Section titled “Why ctxmesh”

Getting an agent to work is a prototype. Getting it to run safely, observably, and repeatably in production is the hard part — and it is mostly operational: which model, at what cost ceiling; what the agent is allowed to say and do; who signs off on a risky tool call; how a new prompt or model is proven before it serves real traffic; and how you see what actually happened. ctxmesh makes those concerns first-class and declarative, instead of bespoke glue per team.

What you get

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  • Agents as custom resources — an AgentDeployment captures the image, model route, tools, memory, and scaling in one spec; the platform reconciles it into a running, autoscaled service.
  • Governance — GuardrailPolicy (PII redaction, pattern denylists, an optional LLM judge), a declarative ApprovalPolicy for gating tool calls on human approval, and per-tenant budgets — all enforced fail-closed.
  • Quality & rollout — an EvalSuite gates promotion; a canary serves a candidate on a traffic split and can auto-progress (or roll back) on the online score.
  • Observability — the step → tool → model causal tree, cost, and feedback, correlated to traces, without you instrumenting your code.
  • Multi-tenancy — namespaced isolation and RBAC-scoped access, following standard operator conventions.

Who it’s for

Section titled “Who it’s for”

Platform and ML-platform teams who already run Kubernetes and want to offer agents to their organization with the same rigor they apply to any other production workload.

Next

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