[100% Off] Langsmith For Ai Agents: Evaluate, Secure, And Ship
Trace and evaluate your AI agents in LangSmith, add security guardrails, and deploy a production-ready application
What you’ll learn
- Set up tracing in LangSmith so you can see every step your agent takes and pinpoint exactly where a run went wrong,Build an evaluation pipeline with datasets and automated scoring to measure whether your agent is actually improving,Identify agent-specific security risks such as prompt injection and unsafe tool use
- and put guardrails in place against them,Take an agent from a local script to a packaged
- deployed application ready for real users,Read an agent trace step by step
- find the failing node
- and confirm the fix with a re-run,Apply a reusable security checklist before letting any agent act on real data or systems,Understand what separates a prototype from a production system — configuration
- error handling
- observability — and add each piece,Recognize the failure patterns behind most broken agents and design evaluations that catch them early
Requirements
- Working knowledge of Python (functions
- classes
- environment variables
- basic packaging),A working agent to evaluate — you should have built at least a simple agent in LangChain or LangGraph before,An API key for an LLM provider such as OpenAI or Anthropic (free tiers are enough),A free LangSmith account (created in the course),A computer with Python 3.10+ and a code editor installed
Description
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. You get tracing, evaluation, guardrails, and a deployment path, all applied to a real agent, in a few hours of focused work.
Enroll now and take your agent from working demo to production-ready application.
