🤖![[Ch 7] Observability with Langfuse — Tracing Every Agent Step featured image](/blog/blog-tutorial/enterprise-ai-agent/ch7-tracing-langfuse/featured_hu_59e1e19b8eb6c33c.webp)
Add full observability to your agent with Langfuse: trace every LLM call and tool execution, track token costs, sanitize sensitive payloads before logging, and surface performance …
![[Ch 8] Building an Evaluation System for Your Agent featured image](/blog/blog-tutorial/enterprise-ai-agent/ch8-evaluation-system/featured_hu_fb673b54bad3c551.webp)
A three-stage evaluation pipeline for AI agents: automated test data generation, rule-based assertions, and LLM-as-judge scoring with DeepEval GEval — giving you a repeatable, …
Why I wrote this series, what you'll build, and how to follow along — an overview of all eight chapters covering the full lifecycle of a production AI agent.
What exactly is an AI agent, how does it differ from a chatbot or an LLM pipeline, and when should you actually use one? This chapter covers the agent loop, real use cases, and the …
A deep dive into the four core components of an AI agent system, and why Context Engineering — managing everything in the LLM's context window — matters far more than just writing …