00-preface
Production AI Agents for Software Engineers
Who This Is For
What You Will Build
Course Philosophy
Technology Choices
How To Use This Course
Module 1: What Makes an Agent Production-Grade
Learning Objectives
Chatbot, Workflow, or Agent
The Core Agent Loop
Production Requirements
Failure Modes
When Not To Use An Agent
Design Exercise
Module Summary
Module 2: LLM Foundations for Engineers
Learning Objectives
Messages
Instructions Are Not Security Boundaries
Structured Outputs
Temperature
Context Windows
Latency and Cost
The LLM Adapter
Lab: Build A Fake LLM Client
Module Summary
Module 3: Tool Calling
Learning Objectives
A Tool Is A Contract
Small Tools Beat Giant Tools
Tool Input Validation
Tool Output Normalization
Tool Risk Levels
Error Handling
Lab: Design Three Tools
Module Summary
Module 4: Agent Control Loops
Learning Objectives
The Minimal Loop
Stop Conditions
Representing Steps
Invalid Model Outputs
Common Control Patterns
Guardrails In The Loop
Lab: Add A Step Limit
Module Summary
Simple Agent Project: Build A Command-Line Engineering Assistant
Project Goal
Project Files
Step 1: Create Sample Data
Step 2: Implement Tools
Step 3: Create A Fake LLM
Step 4: Build The Tool Registry
Step 5: Build The Agent Loop
Step 6: Script A Successful Run
Step 7: Test Failure Behavior
Completion Checklist
Project Reflection
Module 5: State, Memory, and Context
Learning Objectives
Context, State, and Memory
Short-Term State
Long-Term Memory
The Context Builder
Summarization Memory
Memory Failure Modes
Lab: Add Run State To The Simple Agent
Module Summary
Module 6: Retrieval-Augmented Agents
Learning Objectives
Why Agents Need Retrieval
Retrieval As A Tool
Chunking
Embeddings and Vector Search
Metadata and Filters
Reranking and Context Compression
Retrieval Pollution
Lab: Add A Knowledge Search Tool
Module Summary
Module 7: Workflow Agents
Learning Objectives
Agents Inside Workflows
Deterministic First
Human Approval Gates
Resumability
Idempotency
Queues and Background Jobs
Lab: Design A Ticket Triage Workflow
Module Summary
Module 8: Multi-Agent Systems Without The Hype
Learning Objectives
When Multiple Agents Help
Use Functions Before Agents
Supervisor-Worker Pattern
Specialist Agents
Handoffs
Multi-Agent Failure Modes
Lab: Split The Simple Agent Into Roles
Module Summary
Quizzes
Module 1 Quiz
1 attempt allowed
Module 2 Quiz
1 attempt allowed
Module 3 Quiz
1 attempt allowed
Module 4 Quiz
1 attempt allowed
Module 5 Quiz
1 attempt allowed
Module 6 Quiz
1 attempt allowed
Module 7 Quiz
1 attempt allowed
Module 8 Quiz
1 attempt allowed
Module 9 Quiz
1 attempt allowed
Module 10 Quiz
1 attempt allowed
Module 11 Quiz
1 attempt allowed
Module 12 Quiz
1 attempt allowed
Module 13 Quiz
1 attempt allowed
Module 14 Quiz
1 attempt allowed
Module 15 Quiz
1 attempt allowed
Module 16 Quiz
1 attempt allowed
Module 17 Quiz
1 attempt allowed
Module 18 Quiz
1 attempt allowed
Project Quiz
1 attempt allowed
Production AI Agents for Software Engineers
Build reliable AI agents with tool calling, bounded control loops, validation, traces, and production engineering discipline.
Stop building fragile AI demos. Learn how to design, test, and operate AI agents like real software systems.
Minimum price
$19.99
$29.99
You pay
Author earns
About
About the Course
Production AI Agents for Software Engineers is a practical course for developers who want to move beyond chatbot demos and build agent systems that can survive real-world use.
You will learn how production-grade agents are structured, how LLM calls fit into normal software architecture, how to design safe tool interfaces, and how to build bounded control loops that stop, recover, and produce useful evidence.
The first release includes modules on agent fundamentals, LLM foundations, tool calling, and agent control loops, plus a simple command-line agent project with quizzes and answers.
This course is for software engineers, backend developers, full-stack developers, DevOps engineers, data engineers, and technical founders who want a practical foundation for building AI agents responsibly.
By the end of the initial modules, you will be able to build a small but real agent that can accept a task, call approved tools, handle errors, stop within defined limits, and return an evidence-based answer.
Instructor
About the Instructor
Kevin Languedoc is a senior data engineer and software developer with deep experience in analytics systems, cloud platforms, and enterprise data architecture. He has designed and delivered end-to-end data solutions across multiple industries and teaches practical, design-focused approaches to modern data engineering.
Material
Course Material
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