Agentic AI Microservices Architecture: Kubernetes Deployment Patterns
Learn proven Kubernetes deployment patterns for agentic AI microservices including pod design, service mesh, HPA scaling, and health checks for LLM agents.
Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
Learn proven Kubernetes deployment patterns for agentic AI microservices including pod design, service mesh, HPA scaling, and health checks for LLM agents.
Build a full observability stack for agentic AI with OpenTelemetry tracing, Grafana dashboards, custom agent metrics, and alerting strategies.
Master Redis patterns for agentic AI including LLM response caching, conversation sessions, pub/sub for real-time events, and agent performance optimization.
Comprehensive security guide for agentic AI covering prompt injection, tool authorization, data exfiltration, excessive agency, and mitigation strategies.
Comprehensive testing strategy for agentic AI — unit testing tools and prompts, integration testing agent loops, E2E multi-agent flows, and mock LLM patterns.
Learn how to use agent.as_tool() to turn entire agents into callable tools for other agents. Master approval workflows, output extraction, and dynamic tool enabling for multi-agent orchestration.
Design an API gateway for agentic AI with multi-model routing, API key management, rate limiting, WebSocket proxy, and health-based routing.
Design and build a multi-tenant AI agent SaaS platform with user isolation, API key management, token metering, billing integration, and scalable infrastructure using the OpenAI Agents SDK.
Learn how to implement human-in-the-loop approval gates in the OpenAI Agents SDK using needs_approval, MCPToolApprovalRequest, and RunState to control sensitive agent operations.