AI-native Software Engineering with Claude Code
Treat Claude Code as an engineering teammate — verification loops, codebase-aware workflows, and agentic coding at scale.
Duration
3 days · 8-10 hours/week
Level
Intermediate
Delivery
Online
Status
Open for enrollment
Why this technology matters.
Claude Code engineering is the craft of building real software with AI coding agents — steering them through specs, tests, and review instead of pasting snippets and hoping. It matters now because agent-assisted coding multiplies output but also multiplies the speed at which bugs, security holes, and architectural drift can enter a codebase.
It is used by software teams to scaffold features, refactor systems, and keep velocity high while holding quality through specs, tests, and review gates. It solves boilerplate speed and large-scale edits, but it does not solve unclear requirements or bad architecture — an agent will faithfully generate the wrong system faster if the design and acceptance criteria are missing.
By the end you will be able to build a spec-driven feature with agent-generated code and tests, a reviewed pull request workflow with AI-assisted checks, and a small production service shipped start to finish with agent assistance.
Why this course exists
The gap is between a fun demo of generated code and a production codebase that stays tested, secure, and maintainable under agent-assisted velocity. The course teaches the arc from Idea to Design to Code to Test to Deploy and Operate with agents in the loop, so students learn to direct AI coders rather than inherit their mistakes.
Know exactly what you're signing up for.
Who is this for
Prerequisites
- Comfortable with a modern programming language
- Familiarity with git and terminal workflows
- Basic experience building software projects
Technologies & tools
Skills you'll gain
A 3 days arc, module by module.
- Module 01
Day 1 — Agentic coding: when loops hold and when they don't
- Module 02
Day 2 — Codebase-aware workflows and review
- Module 03
Day 3 — Capstone: an agentic coding delivery
Practical Training Flow
Learning → Guided Labs → Independent Practice → Industry Project → Capstone → Portfolio → Career Preparation. Practical hours are tracked alongside instructional hours and surfaced on the certificate.
Delivery as HIGAET Practical Training / Experiential Learning.
What you'll be able to do.
- Run verification-loop workflows that keep agentic code honest.
- Operate Claude Code across a real codebase with reviewable artifacts.
- Build codebase literacy checks that catch silent regressions.
You will build.
Every project ships as HIGAET Practical Training / Experiential Learning — portfolio-ready work, not exercises.
- Project 01
AI-assisted CLI tool build
- Project 02
Agent-driven feature implementation
- Project 03
Tested refactor of a legacy module
- Capstone
Production-quality app shipped with agentic coding workflows
Speak the language first.
- Agentic coding loops
- Workflows where the assistant plans, edits, runs checks, and revises code in repeated cycles toward a goal.
- Repository context scoping
- Selecting the right files and symbols to load into the session so the assistant works with relevant code.
- Structured task plans
- Breaking a coding task into explicit steps with acceptance checks before making changes.
- Test-driven iteration
- Writing or running tests first so each code change is verified against expected behavior immediately.
- Diff review discipline
- Reading every proposed change line by line for correctness, scope creep, and risk before accepting it.
- Long-session memory
- Notes and summaries carried across sessions so conventions and decisions persist without reloading everything.
- Tool-use permissions
- Explicit rules for which file, shell, and network actions the assistant may take without asking.
- Safe command execution
- Running shell commands with scoped paths and review, avoiding destructive or untrusted operations.
- Session handoff notes
- Brief records of goals, changes, and open items so work can resume cleanly in a new session.
Fix, check, and go deeper.
Troubleshooting & common mistakes
Assistant edits the wrong files
Stop the run, narrow the working scope to named paths, restate the target files, and require a plan before further edits.
Large context overflows the session
Summarize progress into handoff notes, start a fresh session with only the needed files, and reintroduce context on demand.
Generated code passes no tests
Run the failing test to capture the exact error, fix the smallest slice first, and rerun the suite before accepting the diff.
Risky shell command proposed
Deny the command, restrict permissions to the project directory, and approve only explicit read-only or reversible commands.
Assistant loops without progress
Interrupt the loop, restate the acceptance criteria in smaller steps, and ask for one minimal change with a verification check.
Unreviewed diff merged with regressions
Revert to the last green commit, review the diff hunk by hunk, and add the missing test that would have caught the break.
Before you move on, you should be able to
- Explain how agentic coding loops turn plans into verified changes
- Design scoped tasks with clear acceptance checks
- Build features iteratively with tests guarding each step
- Evaluate diffs for correctness, scope, and safety
- Deploy session conventions that keep long projects coherent
- Troubleshoot stalled or off-scope assistant behavior
- Document handoffs so work resumes cleanly across sessions
Start your application.
Share a few details and a HIGAET advisor will reach out within one business day with next steps.
Common questions
Ready to start AI-native Software Engineering with Claude Code?
A 3 days course — Workshops.