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Agentic Workflow Showcase with Claude Code

This repository contains structured exercises designed to demonstrate the shift from "Autocomplete Coding" to "Agentic Engineering" using Claude Code.

The Philosophy: Analyze -> Plan -> Approve

In this repo, we do not treat the AI as a chatbot. We treat it as a Agentic Coding Partner. Every exercise is designed to fail initially, requiring the Agent to use Plan Mode to do the following:

  1. Analyze logs and cross-file dependencies.
  2. Propose a strategic plan.
  3. Execute only after human architectural approval.

The Evolution of AI Coding

We have shifted from simple "assistance" to full "engineering partnership." This is how the workflow has evolved over the years:

  • 1990s – 2020 | Statistical Era (IntelliSense): Simple pattern matching. It finishes variable names but lacks any understanding of logic.
  • 2021 – 2023 | Predictive Era (Autocomplete): Predicting the next line of code. Fast, but context-blind, often leading to "correct-looking" bugs.
  • 2023 – 2024 | Junior Era (Simple Chat): Helpful for snippets and explanations, but impulsive. It focuses on the immediate chat request while skipping the system-wide plan.
  • 2025 – 2026 | Senior Era (Agentic Partner): System-wide reasoning using Plan Mode. It analyzes the whole project and proposes a strategy before touching a single file.

Why "Plan Mode" is the Secret Weapon

Plan Mode decouples Thinking from Doing, shifting the AI from an impulsive "Chatbot" to a strategic Engineering Partner.

  • Measure Twice, Cut Once: Scans the entire repo to identify root causes before touching a single line of code.
  • Human as Architect: You audit a technical blueprint rather than cleaning up an AI-generated mess.
  • Context Discovery: Eliminates "guessing" by forcing the agent to explore file dependencies and project structure first.
  • Efficiency: 1 minute of planning prevents 20 minutes of reverting a broken build.

Comparison: Autocomplete vs. Plan Mode

Aspect Autocomplete (Old) Plan Mode (Agentic)
Logic Impulsive (Guess & Write) Strategic (Analyze & Propose)
Human Role Janitor (Cleaning up) Architect (Directing)
Outcome Trial and Error Right-First-Time

Exercises Roadmap

The curriculum is designed to move from local compiler issues to complex system-wide architectural failures.

Difficulty Exercise Name Stack Core Learning Objective
rust-traits-mismatch Rust Fixing cross-file interface discrepancies.
docker-permissions Docker, Node.js Fixing Docker container permission errors.
middleware-ordering Node.js, Express Fixing Express middleware execution order bugs.
⭐⭐ rust-deadlocks Rust Resolving multi-threaded deadlock through locking order analysis.
⭐⭐⭐ distributed-race-condition Docker, Node.js, Redis Fixing inventory overselling across distributed API replicas.
⭐⭐⭐ rust-memory-leaks Rust Identifying and fixing reference cycles that bypass the borrow checker.
⭐⭐⭐⭐ k8s-mismatch Kubernetes, Kind, Node.js Debugging multi-layer Kubernetes networking and probe failures.

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Structured exercises designed to demonstrate the shift from "Autocomplete Coding" to "Agentic Engineering" using Claude Code.

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