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Tools June 19, 2026 explainer 4 min read

Building AI Agents with Claude Code: A Practical Tutorial Roundup

From beginner-friendly agent creation in 14 minutes to production multi-agent systems with 72 evals, Claude Code offers a complete toolkit for building AI agents. Here are the workflows, architectures, and patterns that actually work.

By IA al Día

There is a gap between using Claude Code as a basic coding assistant and building real AI agent systems with it. Four recent tutorials — from beginner to production-grade — bridge it. Here is what they cover and what you can learn from each.

1. Production Multi-Agent System for Insurance Claims

Creator: Maryam Miradi (PhD, 20+ years in AI) Video: 3JUI7uoJzUE

This is the most comprehensive production tutorial. It builds a 5-agent insurance claims system using real car damage images, repair estimates, and insurance policies. The architecture:

  1. Intake Agent — Validates claim completeness
  2. Damage Evidence Agent — Compares vision analysis against customer statements
  3. Policy Agent — Verifies coverage, deductibles, and exclusions
  4. Risk Agent — Scores anomalies and flags risk patterns
  5. Payout Agent — Calculates final financial recommendations

Tech stack: Claude Code CLI, LangGraph for orchestration, Pydantic for typed state, Gradio for the dashboard, Pytest for evaluation.

Key takeaway: The tutorial includes 72 evals that caught a real production bug in the Risk Agent — the agent appeared to work but was silently disabling late-reporting checks. After fixing, all 72 tests passed. This demonstrates the importance of systematic evaluation over “looks correct” testing.

The entire system is packaged as a reusable Claude Skill, meaning you can deploy the same pattern for different domains.

2. Beginner AI Agent in 14 Minutes

Creator: Mikey Vibe Coding Video: 3MX7rVeVIYw

This tutorial targets developers with zero agent-building experience. It builds a restaurant customer service agent that handles bookings, orders, and customer inquiries across WhatsApp and Telegram.

4 core capabilities framework:

  • AI personality and tone configuration
  • Business knowledge base (upload documents, menus, policies)
  • Conversation flow with human escalation logic
  • Task execution (automated booking, order processing)

Key takeaway: The barrier to agent building is lower than most developers think. With Claude Code and a clear prompt structure, a functional multi-channel agent can be built in minutes — not days.

3. Claude Code Custom Skills

Creator: Bhumananda Barik Video: Lj8cAYukeQg

This tutorial focuses on Claude Code’s Skills system — reusable instruction bundles that extend Claude Code’s capabilities for specific tasks.

What you learn:

  • How to create a skill to automate repetitive codebase analysis (e.g., tree visualization)
  • How to share skills across projects
  • How skills reduce token usage by avoiding re-explanation of context

Key takeaway: Skills are the fastest way to move from “prompting Claude every time” to “Claude knows my workflow.” They are Claude Code’s equivalent of IDE macros — but smarter.

4. Production Deployment on Google Cloud

Creator: RK Tech Edge Video: Hxii9Mr4WIc

Part of Google’s AI Agents Intensive course, this tutorial covers taking AI agents from local development to production.

Topics covered:

  • Spec-Driven Development (SDD) methodology
  • Deploying agents to Google Cloud Run
  • Monitoring and observability for agent systems
  • Event-driven agent architectures

Key takeaway: Local agent prototypes are easy. Production agents require deployment pipelines, monitoring, and observability — exactly like traditional software, but with additional complexity from LLM behavior.

Patterns That Emerge

Across all four tutorials, several recurring patterns stand out:

  1. Typed state layers — Every production system uses Pydantic or similar for structured state management. Agents without types fail silently.
  2. Evaluation infrastructure — Evals are not optional. The production tutorial caught a bug that manual review missed.
  3. Modular agent design — Single monolithic agents fail; multi-agent systems with clear boundaries succeed.
  4. Reusable skills — Packaging agent workflows as Skills reduces friction and enables sharing.
  5. Observability first — Agent systems need audit trails, confidence scores, and monitoring from day one.

Getting Started

If you are new to building agents with Claude Code, start with the beginner tutorial (14-minute agent), then explore custom skills, and graduate to multi-agent systems once you understand the patterns. The production insurance claims tutorial is the gold standard for architecture and testing rigor.