Course Description
This upper-division course is a hands-on study of the AI frontier as it stands in late 2026. That frontier is no longer API plumbing or retrieval pipelines. It is AI software engineering (AI-SWE): coordinating autonomous coding agents through a professional software development lifecycle (SDLC). Over the past year the field has moved away from hand-wired API integrations and hand-assembled RAG stacks (now commodity work, and mostly something the agents can do themselves) and toward harness engineering — configuring, extending, and orchestrating AI coding agents such as Claude Code and comparable CLI-native tools so they can carry real engineering work end-to-end.
You build this competency through four mini-projects that stack on each other: your own development environment, agent configuration and custom Skills, agent harness/hook engineering, and a spec-driven SDLC capstone. The semester ends with an original interdisciplinary research project that puts the whole toolchain to work on a question you choose.
Running alongside the technical work is a discussion of what happens (to labor, to ethics, to what we can know) when agentic systems replace or augment knowledge work. We take that up explicitly in Weeks 3, 8, and 12, and again in the lightning presentations.
Note: AI tooling changes on a timescale of weeks, not years. Expect the specific tools, readings, and technical references in this syllabus to be revised during the semester. Core learning objectives, assignment weights, and grading policies will not change without advance notice.
Course Goals and Learning Outcomes
By the end of this course, students will be able to:
- Configure a professional, reproducible development environment (shell, terminal, dotfiles) suitable for agentic software engineering, with sound secrets hygiene
- Install, configure, and work fluently in leading AI interfaces (Web UI / Desktop App / CLI) and best-of-breed developer tooling (VS Code and extensions, GitHub)
- Configure and extend an AI coding agent (CLAUDE.md, custom slash commands, custom Skills, subagents) to reliably perform domain-specific work
- Design and wire agent hooks and harness-level automation (linting, testing, notification, observability, guardrails) around an agentic coding workflow, including basic multi-agent/subagent orchestration
- Execute a complete spec-driven software development lifecycle — plan, decompose, implement, test, review, and optionally deploy — with an AI coding agent, following current industry practice (e.g., GitHub Spec-Kit, OpenSpec, mattpocock-style spec workflows)
- Evaluate the ethical, labor, security, and epistemic implications of agentic AI systems replacing or augmenting knowledge work
- Apply this agentic toolchain to an original interdisciplinary research question in a field such as medicine, law, education, or public policy
- Communicate technical work through documentation, public repositories, live demos, and a professional research poster
- Build a public professional portfolio (GitHub + digital.kenyon.edu/dh) that demonstrates this competency for graduate school or employment
Outcome 6 is assessed through the ethics discussions in Weeks 3, 8, and 12, related quiz questions, and the lightning presentation. Outcomes 1–5 map to Mini-Projects 1–4; Outcomes 7–9 map to the Final Project.
Instructor
| Instructor | Jon Chun |
|---|---|
| Department | Integrated Program for Humane Studies (IPHS) |
| Office Hours | Tu/Th, 1:30–2:30 PM, Timberlake #3, and by appointment |
Email is the best way to reach the instructor outside office hours; expect a response within one business day (longer on weekends). Questions of general interest may be answered to the whole class.
Course Site
- Materials:
https://github.com/jon-chun/theailab-net - Quizzes, grades, and submissions: Moodle
This syllabus continues the Frontiers in AI course offered since 2024.