What AI Agents Need to Fix Bugs in One Shot

If you've handed a bug fix to Claude Code or Cursor and gotten back a patch that misses the point — or touches the wrong files entirely — the problem is usually not your prompt wording. It's the granularity of the information you handed over.

Information beats instructions

A human engineer can fill gaps in a vague bug report with experience and guesswork. AI agents are much worse at this. They build hypotheses strictly from what's in front of them, so when information is missing, they invent a plausible-sounding cause and start editing unrelated files.

The lever that actually moves success rate isn't a cleverer prompt — it's a repeatable set of inputs you hand over every time.

The five inputs that make a fix land in one shot

1. Reproduction steps

Write down where, what, and in what order — as a numbered list an agent can literally follow: "Log in, open the Kanban board from the project list, drag a card." That's the granularity to aim for.

2. Expected vs. actual, stated separately

"Expected: status changes to In Progress." "Actual: the card snaps back to its original column." Written as one sentence each. Leave this vague and the agent has to guess what "bug" even means here.

3. The exact screen and code location

Include the URL, component name, and ideally a file path. "Somewhere in the Kanban board" costs an agent a full exploration pass; "around frontend/components/kanban/BugReportCard.tsx" costs almost nothing.

4. Raw error logs and stack traces

Paste the console or network error verbatim. Summarizing it strips out exactly the line numbers and error codes an agent would otherwise use as its strongest clue.

5. Acceptance criteria

State what "fixed" means, Given/When/Then style. Without it, an agent will decide for itself that the bug is resolved — and that decision won't necessarily match what the user actually expects.

What happens when you skip these

• No reproduction steps — the agent edits several plausible-looking components at once, and the diff balloons with unrelated changes
• No expected/actual split — handed only "this looks off," the agent fixes a cosmetic detail and misses the actual logic bug
• Summarized error logs — "there's a network error" doesn't say whether it's a 408 or a 500, or which endpoint, so the agent adds a guessed exception handler
• No acceptance criteria — a fix ships without a test, and the same review comment comes back next time

A checklist before you hand it off

Before passing a bug report to an agent, confirm these five are present — it saves a round trip:

• □ Numbered reproduction steps
• □ Expected and actual behavior written as separate sentences
• □ The screen's URL or component name
• □ Raw console/network error output
• □ Acceptance criteria that define "fixed"

A report missing any of these is usually faster to send back for more detail than to hand to an agent as-is.

How Bugoon fits in

Bugoon's report form pairs an annotated screenshot with a recorded sequence of interaction steps, which fills in "reproduction steps" and "the exact screen" automatically at report time. Reports flow into GitHub Issues, so reproduction steps, screenshots, and interaction steps arrive in the same Issue an agent will read.

The Bugoon MCP server lets Claude Code or Cursor pull a bug report's details — including the annotated image and status — directly, without a human copy-pasting in between. In practice, that gets most of the five-input set in front of the agent with no manual assembly step.