How to Ship AI Agents With Zero Infrastructure

How to Ship AI Agents With Zero Infrastructure

Why we build creative tech for real people.

Many tech teams have brilliant ideas for AI agents, only to become bogged down by infrastructure, security, and complexity. Connecting language models (LLMs) to databases, authentication, and REST endpoints often takes months and significant resources.

But what if there was a better, simpler way?

Real Agents Without the Infrastructure Headache

Typically, AI demos showcase conversational interactions but fall short when it comes to real-world capabilities like securely fetching data, executing actions, and seamless integration with existing systems.

Normally, achieving this requires extensive backend servers and infrastructure.

I’ve developed a different approach: a fully production-ready AI agent app built on Google’s Vertex AI Agent Engine and Agents ADK, delivering:

  • Multi-Step Reasoning: Complex tasks broken down logically.
  • Live Tool Execution: Securely calling APIs, performing database operations, and retrieving data.
  • Scoped and Secure Calls: Each call scoped to authenticated users through JWTs, eliminating service-wide keys.
  • Serverless Scalability: Completely infrastructure-free, running on Vertex AI and Supabase Edge Functions.

How It Works

  • Agent as an App: Deployed via Vertex AI’s ADK with built-in state management and secure context.
  • ToolCards (Dynamic Tools): JSON-based tool definitions quickly integrated through a custom wrapper, making external JSON ToolCards compatible with Google’s ADK.
  • Auth Seeding (Secure Context): Only necessary JWT tokens and user data injected into the agent’s context per request.
  • Supabase Edge Functions: Securely execute immediate responses to the agent’s requests directly from the database.

MCP Compatibility Without the MCP Server

This setup follows the Model Context Protocol (MCP) principles, typically used to standardize secure tool and data access for AI agents.

The innovation here? Achieving MCP compliance without deploying dedicated MCP servers:

  • JSON-defined tools with clear MCP-like schemas
  • JWT-based security and scoped tool invocation
  • Standardized MCP-style interface, but executed entirely serverlessly

Alignment with Google’s A2A Protocol

Google’s Agent-to-Agent (A2A) protocol complements MCP by enabling standardized communication between agents.

My approach aligns naturally with A2A, making it easy to extend this agent app to:

  • Publish agent capability cards (similar to ToolCards)
  • Facilitate standardized inter-agent communication and orchestration

Real-World Results

  • Implementing new tools takes under 30 minutes
  • Infrastructure costs approach zero with no servers to maintain
  • Currently, the system securely manages real-world data and actions in production

Ideal Use Cases

  • AI SaaS Teams: Quickly deploy sophisticated AI copilots.
  • Mobile Apps: Enhance user interactions with intelligent, secure, and responsive AI features directly within mobile experiences.
  • Platforms: Seamlessly integrate agent-driven workflows and automation across large-scale platforms.
  • Startups: Rapid launch and scaling without infrastructure overhead.

What’s Next?

I’m offering this system as a high-impact service: a secure, scalable AI agent that integrates directly into your product, follows MCP principles, and requires zero backend infrastructure.

It’s built to help teams deploy real agentic workflows — fast, customized, and production-ready.

Interested?

  • Seeking early access or a demo?
  • Recruiting for expertise in secure, scalable AI agent solutions?
  • Curious about simplifying your agent deployment strategy?

Drop a comment or send me a DM. I’d love to share insights, demos, or discuss potential collaboration.

Let’s build smarter, simpler, and more secure AI agents — without the infrastructure hassle.