An agent can solve the ticket, pass the tests, and still leave the codebase harder to change. It invents an abstraction that matches nothing else, duplicates logic that already exists three files away, and hides both inside a diff too large to review well. Nobody wrote obviously bad code. The debt got in anyway.

This is technical debt when AI coding agents write a growing share of the codebase. The session names three recurring forms: prompt drift, where changing instructions produce inconsistent implementation choices; phantom abstractions, which look reusable but encode assumptions the system does not share; and unreviewable diffs, where volume defeats meaningful human review.

A before-and-after code walkthrough will trace one agent-generated change from passing tests to measurable maintenance cost. We will compare the result with Martin Fowler's technical debt quadrant, then use churn, duplication, ownership, and diff-risk signals to catch what static analysis misses. We will also define review boundaries based on change size, reversibility, and blast radius instead of one flat rule.

Attendees will leave able to recognize these debt patterns early, measure their spread, design smaller reviewable agent tasks, and explain why compiled and tested is necessary—but not the same as maintainable. This is not an argument against AI-assisted coding. It is a field guide for teams already living with agents in the codebase.

Audience: software engineers, tech leads, and engineering managers using AI-assisted coding tools. Format: 50-minute technical session with a code walkthrough; no live demo required. Vendor-neutral.