Empower Team Wide Vibe Coding with LLM Gateway and Security-First MCPs

Scaling AI-assisted development from a few enthusiasts to 50+ software engineers isn't just about API keys - it's about governance, trust, and standardized workflows. How do you prevent "shadow AI" and budget chaos while granting safe access to production context?

At Bowtie, we adopted a layered security-first approach: First, control the traffic; second, secure the tools; third, standardize the behavior.

This session covers our journey building a production-grade Vibe coding platform:

Layer 1: The AI Gateway (LiteLLM on AWS Fargate + Amazon RDS)
We established a centralized choke point for all AI traffic. This enables cost attribution, DLP detection, and usage visibility. Crucially, we enforce this at the network level - blocking direct access to non-official API providers to ensure all usage is visible and governed.

Layer 2: Security-First MCP servers
We treat AI agents as "CLI versions" of our internal web apps. By building custom Model Context Protocol (MCP) servers that reuse existing permissions and authentications - using Amazon Cognito for internal APIs and OAuth for official SaaS MCP servers - the AI acts as a delegate of the developer with human approvals. No new service accounts, no "god-mode" bots - if you can't do it in the existing user interfaces, the Agent can't do it via MCPs.

Layer 3: Custom Skills for Standardization
Beyond just tools, we write custom Skills to guide the model's behavior, ensuring generated code aligns with our engineering standards and SOPs (e.g. grab a ticket, fetch knowledge base, apply a fix, close the ticket) before a PR is even opened.

Walk away with an architectural blueprint for democratizing AI access that satisfies the strictest security requirements while giving developers the friction-free vibe coding experience they crave.