AI coding agents are transforming how developers build and operate modern cloud-native applications. With tools such as Kiro CLI, Kiro IDE, or any MCP-compatible AI coding assistant, developers are embracing AI to move faster and scale smarter. This session explores how MCP servers help developers streamline code generation, deployment, and debugging by embedding infrastructure awareness directly into the AI assistant. Learn how Xero is leveraging MCP to speed up development, simplify operations, and deliver more reliable containerized apps at scale. Xero will also share their success story using Kiro CLI, Prometheus MCP, EKS MCP, and AWS Knowledge Base MCP to identify and resolve Prometheus cost spikesslashing costs by 40%.
What this session is about
Playbook
Editorial commentary · what to actually do about this on Monday
The concept
MCP servers embed infrastructure awareness directly into AI coding assistants. Xero's stack: Kiro CLI + Prometheus MCP + EKS MCP + AWS Knowledge Base MCP. Cut Prometheus costs by 40%.
Why it matters
AI assistants without infrastructure context produce generic answers. With MCP, they reason about *your* deployment.
The hard parts
MCP server quality varies. A poorly-configured MCP gives the agent confidently wrong context — worse than no context.
Playbook moves
(1) Inventory the MCP servers your teams use. (2) Treat them like tools — versioned, owned, documented. (3) Audit what context they expose; over-broad MCPs leak.
The surprise
The 40% Prometheus cost cut came from agents finding *cardinality bombs* humans missed. The gain wasn't AI being smarter — it was AI being patient enough to read every metric label. Use AI for tedious-but-tractable problems; that's where the 80% of the wins live.
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Independent editorial perspective — not an official AWS or speaker statement. Designed for executives evaluating what to brief their teams on next.
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