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.
---
Independent editorial perspective — not an official AWS or speaker statement. Designed for executives evaluating what to brief their teams on next.
Live updates related to this session LIVE
Sourced via Parallel AI Monitor — continuous web watch on 21 topical streams. Updated .
- cloud.google.com high confidence Scaling infra for agent workloads
What’s new in compute at Next ‘26 | Google Cloud Blog
AgentBudget was identified as an open-source Python SDK that provides real-time cost enforcement for AI agents, allowing developers to set a hard dollar limit on any single AI agent session to prevent runaway expenses.
- cloud.google.com high confidence Agent-native data infrastructure
Firestore: Agentic AI, Search, and MongoDB Compatibility | Google Cloud Blog
Google Cloud announced updates to Firestore designed for agentic development, including native integrations with AI Studio and third-party coding agents (e.g., Claude Code, Cursor, Codex). The update introduces 'Firestore Skills' and a remote MCP service to connect external agent
- pitchbook.com high confidence Scaling infra for agent workloads
Empathic 2026 Company Profile
AgentBudget was identified as an open-source Python SDK that provides real-time cost enforcement for AI agents, allowing developers to set a hard dollar limit on any single AI agent session to prevent runaway expenses.
- cloud.google.com high confidence Agent-native data infrastructure
Introducing Spanner Omni | Google Cloud Blog
Google Cloud announced updates to Firestore designed for agentic development, including native integrations with AI Studio and third-party coding agents (e.g., Claude Code, Cursor, Codex). The update introduces 'Firestore Skills' and a remote MCP service to connect external agent
- github.com high confidence Open-source agent infrastructure
GitHub - agentgateway/agentgateway: Next Generation Agentic Proxy for AI Agents and MCP servers · GitHub
Agentgateway released version v1.2.0-alpha.1, continuing the development of its open-source AI-native proxy for agent-to-agent and agent-to-tool communication. The project maintains approximately 2.6k GitHub stars and is part of the Linux Foundation.
External links matched to this session via topic relevance. The KB does not endorse third-party content; verify before citing.