Build Your Own AI Agent: From Hello World to Production-Ready

Build Your Own AI Agent with Frontend Masters

TLDR: Frontend Masters released "AI Agents Fundamentals, v2" taught by Scott Moss from Netflix. The course takes you from basic LLM interactions to building a production-ready CLI agent with tool calling, human-in-the-loop approvals, and observability.

Summary:

Frontend Masters has launched a comprehensive course on building AI agents, taught by Scott Moss, Software Engineer at Netflix. The course demonstrates what's now possible: in the first five minutes, Scott builds an agent that controls a browser using plain English. No rigid scripts — the agent figures out what to do next on its own.

The curriculum covers the complete journey from a basic "hello world" LLM to production-ready agents with human oversight. The practical focus is clear: by the end of the course, you'll have built your own CLI agent that can search the web, make API calls, read and write files, run shell commands, and request approval for potentially dangerous operations.

The topics covered are comprehensive and practical:

Tool calling from scratch — Define tools with schemas, let the LLM decide when to use them, and execute the results. This is the foundation of agentic systems.

The agent loop — Handle tasks where the number of steps isn't known ahead of time. Stream responses and manage conversation flow. This is where agents differ from simple chat interfaces.

Evals (AI Tests) that actually help — Test non-deterministic outputs, track success rates, and use observability tools like Laminar to understand what's happening. Testing AI systems requires different approaches than traditional software.

Context management — When conversations get long, use compaction, summarization, and RAG to keep your agent focused. Context window management is critical for production agents.

Human-in-the-loop approvals — Add checkpoints so agents ask before taking sensitive actions. This is essential for any agent that interacts with real systems.

For teams building AI-powered features, this course covers the practical aspects that matter: not just making agents work, but making them reliable, observable, and safe for production use.

Key takeaways:

Build a complete CLI agent with web search, file system, and shell access
Tool calling patterns: define schemas, let LLM choose, execute results
Agent loop handles tasks with unknown number of steps
Human-in-the-loop approvals for sensitive operations
Context management with compaction, summarization, and RAG
Observability with tools like Laminar for understanding agent behavior

Link: AI Agents Fundamentals, v2

This article was generated from a newsletter summary. The content reflects the source material's perspective and may not represent complete analysis of all aspects of the topic.

Build Your Own AI Agent with Frontend Masters

Summary:

The topics covered are comprehensive and practical:

Tool calling from scratch — Define tools with schemas, let the LLM decide when to use them, and execute the results. This is the foundation of agentic systems.

The agent loop — Handle tasks where the number of steps isn't known ahead of time. Stream responses and manage conversation flow. This is where agents differ from simple chat interfaces.

Context management — When conversations get long, use compaction, summarization, and RAG to keep your agent focused. Context window management is critical for production agents.

Human-in-the-loop approvals — Add checkpoints so agents ask before taking sensitive actions. This is essential for any agent that interacts with real systems.

For teams building AI-powered features, this course covers the practical aspects that matter: not just making agents work, but making them reliable, observable, and safe for production use.

Key takeaways:

Build a complete CLI agent with web search, file system, and shell access

Tool calling patterns: define schemas, let LLM choose, execute results

Agent loop handles tasks with unknown number of steps

Human-in-the-loop approvals for sensitive operations

Context management with compaction, summarization, and RAG

Observability with tools like Laminar for understanding agent behavior

This article was generated from a newsletter summary. The content reflects the source material's perspective and may not represent complete analysis of all aspects of the topic.