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Learn how to build production-grade AI agents with PydanticAI v1. Covers structured outputs, type-safe tools, dependency injection, MCP integration, and observability with Logfire — all with working code examples.
Learn how to build production-ready MCP servers using FastMCP 3.0 and Python. Covers tools, resources, providers, transforms, database integration, authentication, OpenTelemetry observability, and deployment.
A practical decision framework for choosing between RAG, AI agents, and MCP in production AI systems. Includes performance benchmarks, code examples, cost analysis, and migration patterns.
A hands-on guide to production-grade AI workflow orchestration — covering LangGraph checkpointing, Temporal durable execution with retry policies and Saga compensation, and the two-layer architecture for mission-critical agent pipelines.
Your AI agent forgets everything between conversations. Here's how to fix that with production-ready memory architectures using Mem0, Letta, Zep, LangGraph, and Redis — with real code you can ship today.
A practical guide to cutting LLM API costs by 60-88% using four optimization layers: semantic caching with Redis, intelligent model routing, provider-level prompt caching, and batch processing with production code examples.
A hands-on guide to building production-grade observability for LLM applications — covering distributed tracing with OpenTelemetry, cost attribution, quality monitoring with LLM-as-judge evaluation, and alerting using Langfuse, Prometheus, and Grafana.
A practical guide to building production-grade LLM tool use — from Claude and OpenAI function calling basics through parallel execution, tool search, error handling, and security hardening with working code examples.
A hands-on guide to building production-grade safety systems for LLM applications. Covers defense-in-depth architecture with NeMo Guardrails, Guardrails AI, OpenAI Guardrails, Llama Guard, and PII detection — with deployable code examples.
A hands-on guide to production prompt engineering in 2026, covering structured outputs for guaranteed schema compliance, prompt chaining, chain-of-thought reasoning, automated optimization with DSPy, and prompt version control with CI testing.
A hands-on guide to building production-grade LLM evaluation pipelines — from DeepEval test suites and custom LLM-as-judge evaluators to golden datasets, GitHub Actions CI/CD integration, and real-time monitoring with Langfuse.
Move beyond basic retrieve-and-generate. Learn how to build agentic RAG pipelines that grade their own retrieval, rewrite queries on the fly, and self-correct before answering — with production-ready LangGraph code and evaluation strategies.
A hands-on guide to building production-ready multi-agent AI systems in 2026 — covering architecture patterns, MCP, A2A protocols, framework comparisons, and best practices with Python code examples.