New Post: From Locked-In to Cloud-Native: Deploy Your Custom AI Agent on Google Cloud Run

At GoCloudiQ, we’re all about enabling scalable, cloud-native architectures—and that includes modular AI tools.

Mark W. Kiehl just dropped a fantastic guide that aligns perfectly with our philosophy: build once, run anywhere—without LLM lock-in.

In this article, he walks through how to:

✅ Convert a LangChain tool into an MCP-compliant AI service using Python A2A

✅ Containerize it with Docker

✅ Deploy it to Google Cloud Run with full automation via batch scripts

✅ Stay within the GCP Free Tier while scaling your AI agents

🔁 The result?

A fully decoupled, framework-agnostic GenAI microservice that can scale on demand, be accessed via HTTP, and reused across environments.

Whether you're experimenting locally or running a production-grade pipeline—this approach makes it easy.

💡 Why it matters:
We believe the future of AI tooling is interoperable, composable, and cloud-first.

Tools like Python A2A and MCP are paving the way for intelligent agents that talk to each other securely and at scale.

🙌 Big thanks to Mark for making advanced deployment workflows approachable and replicable.

.

🙌This is exactly the kind of enablement we support at GoCloud iQ.

🔗 GitHub link here: [Insert link]

#GoCloudiQ #CloudRun #GoogleCloud #AIInfrastructure #GenAI #PythonA2A #CloudNative #MCP #ScalableAI #AgentArchitecture #OpenSourceTools #LLM #CloudDeployment #BuildToScale