Focus on your Circle of Influence — act on what you can control, not what you can't.
In AI-assisted development, your Circle of Influence is larger than you think. You can't control whether the AI generates perfect code, but you can control how thoroughly you verify it, how many edge cases you consider, and whether you trace the impact of every change.
Reactive developers fix the bug that was reported. Proactive developers fix the bug, check every caller of the affected function, add edge case handling, and update the docs — all in the same session.
With AI assistance, being proactive costs almost nothing. An AI agent can trace all callers of a function in seconds. The question isn't capability — it's habit.
1. Every bug fix: trace ALL callers, not just the reported path.
When you fix a function, ask: "What else calls this?" A bug in parseDate() might affect 12 callers, not just the one in the bug report. Defense-in-depth means fixing the root cause everywhere.
2. Every new function: consider null input, missing file, permission denied, corrupt data.
Before marking a function complete, mentally walk through the failure modes. AI assistants are optimistic — they write the happy path. You provide the paranoia.
3. Update docs DURING feature work, not after release.
"I'll document it later" is the most common lie in software. When the AI implements a feature, have it update the relevant docs in the same session. The context is fresh, the cost is low.
4. Surface improvements proactively — don't wait to be asked.
When working on area X and you notice area Y could benefit from the same fix, flag it. Even if it's out of scope, a comment like "FYI: the same date parsing issue exists in the export module" prevents future bugs.
// REACTIVE: fix only the reported path
function parseDate(input) {
return new Date(input); // "fixed" here, but 11 other callers still break
}
// PROACTIVE: fix the function AND verify all callers
function parseDate(input) {
if (!input || typeof input !== "string") return null;
const parsed = new Date(input);
return isNaN(parsed.getTime()) ? null : parsed;
}
// Then: grep -r "parseDate(" src/ to verify all 12 callers handle null- Fix-the-line syndrome: Changing only the exact line mentioned in the bug report without checking related code paths. The AI will happily do this — you need to ask for more.
- Silent error suppression: Wrapping code in try-catch with an empty catch block. The error didn't go away — you just hid it.
- "Document later": Completing a feature without updating any docs. Later never comes, and the next developer (or your future self) pays the cost.
While running a standardized benchmark against an AI memory system, the team proactively tested not just the benchmark scenarios but also edge cases around retrieval pipeline behavior. This uncovered 2 production bugs that no user had reported — a date parser that silently returned wrong results for relative time expressions, and a search scoring function that over-weighted recency. Both would have caused subtle data quality issues. The benchmark was about measuring accuracy; the bugs were found because the team looked beyond what was asked.
| Do | Don't | Why |
|---|---|---|
| Trace ALL callers of a bug fix | Fix only the reported line | Root cause may affect 12 callers |
| Handle null, missing file, permission denied | Write only happy path | AI skews optimistic |
| Update docs DURING feature work | "Document it later" | Later never comes |
| Surface improvements proactively | Wait to be asked | A comment today prevents a bug tomorrow |
| Consider edge cases before marking done | Assume AI covered everything | AI-generated code skews toward happy paths |
"Have I checked what else this change affects? Am I reacting or preventing?"
If you're only fixing what was reported, you're reactive. If you're preventing the next 3 bugs while fixing this one, you're proactive.