LangSmith for AI Agents: Evaluate, Secure, and Ship

About this course

Building an AI agent is only half the job. The other half is proving that it works, keeping it safe, and getting it into the hands of real users. This course covers that second half using LangSmith and production engineering practices, so your agent can move from a demo to a live application with confidence.

What you will learn

Trace every run of your agent in LangSmith and find the exact step where a failure happens

Build an evaluation pipeline with datasets and automated scoring to measure real improvement

Identify security risks specific to agents, such as prompt injection and unsafe tool use, and add guardrails against them

Understand what separates a prototype from a production system and add the missing pieces

Package your agent as a usable application and deploy it to a live environment

What is inside

The course opens with a short refresher on agent fundamentals, framed around the failure patterns you will learn to detect. From there it moves through three practical stages: measuring agent quality with LangSmith, locking the agent down with a threat model and a reusable security checklist, and shipping it as a complete production-oriented build. Each stage includes hands-on work and closes with a written reference you can return to at any time.

Who this course is for

This course is for developers who already have a working agent and need a reliable way to evaluate, secure, and deploy it. It suits AI engineers preparing to launch, backend engineers responsible for LLM features in production, and team leads who need a repeatable approval process before anything goes live. You should have built at least a simple agent with LangChain or LangGraph before starting.

Why take this course

It is short and focused on one outcome: an agent you can trust in production. There is no filler and no long theory marathon.

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