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Technical AIintermediateVideo + quiz

Building Your First Production Agent

Build a tool-using agent with the Claude API, then make it safe to run: failure handling, step and spend caps, a gated MCP connection, trajectory evals and a staged release.

5 learners enrolled17 lessons · 1.8 hours
Watch the first lesson freeWelcome · 3 min

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What you will be able to do

Design tools that Claude calls correctly and run the tool-use loopHandle tool failures and stop runs with step and spend capsSave state so that an interrupted agent run can resume where it stoppedConnect an MCP server safely and give the agent delegated access under its own identityTest an agent with trajectory evals before each releaseShip with traces, canaries and a kill switch, then version and retire the agent

Skills you will gain

  • Tool-Call Loop Design
  • Agent Runtime Selection
  • Silent Failure Detection
  • Fail-Plausible Output Mitigation
  • Circuit Breaker Configuration
  • Token Budget Enforcement
  • Rate-Limit Guardrails
  • MCP Protocol Integration
  • MCP Security Controls
  • Tool Poisoning Prevention
  • Multi-Level Agent Evaluation
  • Trajectory-Level Tracing
  • Deployment Gate Design
  • Observability Stack Selection
  • LangSmith
  • Langfuse
  • Arize Phoenix
  • LangGraph
  • Claude Agent SDK
  • OpenAI Responses API

About this course

Build a tool-using agent with the Claude API, then make it safe to run: failure handling, step and spend caps, a gated MCP connection, trajectory evals and a staged release.

Technical AI

Curriculum

6 modules · 17 lessons · 1.8h
WelcomeFree preview3 min

Requirements

How to read Python
What an API call is
You have made one Claude API call
Python 3
Node.js 20 or later for the lab
A code editor
A free Groq key for the lab, no card needed
Optional: Claude API credit, which is paid
The lab also runs with no key in mock mode

Common questions

What prior experience do I need before taking this course?
You need to already be comfortable calling a REST API and reading JSON responses in Python or TypeScript, and you should have sent at least one prompt to an LLM API such as Claude or OpenAI and read the response. No prior agent-building experience is required. The course does not cover basic prompt engineering or general LLM concepts — it starts where API familiarity ends and production engineering begins.
Does this course cover both Claude and OpenAI agents, or is it specific to one provider?
The course covers both. Module 1 compares the Claude Agent SDK and the OpenAI Responses API side by side, including the August 2026 Assistants API sunset and current GPT-5.6 and GPT-6 Astra pricing. Modules on failure handling, cost guardrails, and evaluation apply to any tool-calling agent regardless of provider. The MCP module uses Anthropic's built-in connector as the worked example but the protocol concepts apply across providers.
How long does the course take to complete?
The course has 14 lessons totalling 105 minutes of narrated content across 6 modules. Learners who work through the scripts and apply the patterns to their own projects should budget 2 to 3 hours in total. There are no live sessions and no fixed schedule — all content is self-paced.
Does this course include hands-on labs or a final assessment?
The course is structured as 14 lecture lessons — narrated scripts that walk through concepts, code patterns, and design decisions. It does not include separate lab exercises or a final quiz. Each lesson's learning outcomes are specific and measurable, and the scripts include concrete code patterns you can apply directly to your own agent projects.
What topics are not covered in this course?
This course does not cover basic prompt engineering, RAG or retrieval pipeline design, multi-agent orchestration patterns beyond single-agent loops, fine-tuning, or general LLM evaluation for non-agentic applications. It also does not cover cloud infrastructure provisioning, Kubernetes, or general backend engineering — it assumes you can already ship a service and focuses specifically on the agent-layer engineering decisions that differ from standard API development.

What you walk away with

A Yallo AI academy certificate of completion, issued when every lesson is complete and the final assessment is passed at 70% or aboveA public verification page, so a stranger can check the certificate without asking you for a filePDF download, and a one-click share to your LinkedIn profile

Yallo AI academy certifies completion of

Building Your First Production Agent