AI Gateway Engineering is a two-day hands-on course focused on the infrastructure layer that controls, observes, and secures AI traffic. Modern AI gateways sit between applications, agents, tools, and model providers, giving teams a central place to manage routing, policy, budgets, safety controls, auditability, and operational reliability. This course covers both the conceptual architecture and the practical engineering tradeoffs required to deploy gateway capabilities in production. Participants compare gateway patterns such as provider routing, OpenAI-compatible proxying, policy enforcement, guardrails, semantic caching, MCP-aware tool access, and observability pipelines. Through labs and design exercises, attendees build gateway-backed AI workflows, enforce policies, inspect traces, control spend, and plan production-ready gateway architectures.
Day one’s labs build a gateway from the ground up. Attendees map an AI application stack to gateway responsibilities, configure model routing and fallback across providers, add budgets, rate limits, and cost visibility to live traffic, and deploy a minimal gateway using tools like LiteLLM or Envoy-based plugins to send test traffic through it. Day two shifts to production concerns. Participants enforce security and data protection policies at the gateway, create and test policy-as-code controls for teams, models, and data classes, and instrument gateway traffic to investigate a simulated operational issue. The course finishes with participants designing a production-ready architecture for an agentic application that touches tools, MCP servers, and RAG systems.
Who Should Attend
Platform engineers, AI engineers, security engineers, architects, and technical leads
What Attendees Will Learn
Upon completing AI Gateway Engineering, participants will be able to:
- Explain how AI gateways differ from API gateways, service meshes, model routers, and agent runtimes
- Route traffic across model providers, hosted models, self-hosted models, and agentic services
- Apply policies for authentication, authorization, budgets, rate limits, safety, and data protection
- Implement observability for token usage, latency, cost, model behavior, tool calls, and policy decisions
- Design reliability patterns including fallback, retry, circuit breaking, semantic caching, and failover
- Integrate AI gateways with MCP, RAG, agent frameworks, CI/CD, and enterprise platform services
- Evaluate gateway products and architectures for security, performance, governance, and operations
Prerequisites
Participants should have basic programming experience and familiarity with APIs, HTTP, cloud or platform engineering, and LLM application concepts. Prior experience with Kubernetes, observability, security, or API gateways is helpful but not required.