MCP Dev Summit Bengaluru 2026

Most teams treat MCP as a pipeline, and that is the problem. Pipelines fail silently. They have no concept of desired state, no reconciliation loop, and no recovery path when a model times out or a tool call returns garbage. You are essentially writing bash scripts with an LLM in the middle.

Kubernetes already solved this. The operator pattern gives you level-triggered reconciliation, retry logic with backoff, and declarative desired state baked into the control plane. Combine that with MCP's tool abstraction and you stop writing pipelines and start building platforms where AI workflows are first-class resources that the cluster actively keeps healthy. GitOps via ArgoCD means your model routing, fallback configurations, and tool permissions are version-controlled, auditable, and promotable across environments like any other workload.

We will cover: modeling MCP workflows as Kubernetes custom resources, building operators that reconcile AI workflow state including fallback model selection and tool availability, wiring ArgoCD to manage MCP server deployments and configuration drift, and the observability hooks you need to actually trust that self-healing fired correctly.