Structured Debugging

When debugging hard issues, the natural instinct is to form a theory and immediately apply a fix. This fails more often than it works. The fix addresses the wrong cause, adds complexity, creates false confidence, and obscures the real issue. Worse, after several failed attempts you lose track of what's been tried and start guessing randomly.

This methodology replaces guessing with a disciplined cycle that converges on the root cause. Each iteration narrows the search space. It's slower per attempt but dramatically faster overall because you stop wasting runs on wrong theories.

The Cycle

1. Hypothesize

Before touching code, write down what you think is happening and why. Be specific about the expected state at each step in the execution path.

Bad: "Something is wrong with the wait loop." Good: "The leader hangs because hasActiveTeammates() returns true after all agents have reported completed, likely because terminal status isn't being set on the agent object after the backend process exits."

For bugs you expect to take more than one round, create a side note file for the investigation in whichever location the project uses for such notes.

Write your hypothesis there. This file persists across conversation turns and even across sessions — it's your investigation journal.

2. Design Instrumentation

Add targeted debug logs or assertions at the exact decision points that would confirm or reject your hypothesis. Think about what data you need to see.

Don't scatter console.log everywhere. Identify the 2-3 places where your hypothesis makes a testable prediction, and instrument those.

Prefer logging values (return codes, payload contents, stream types, message bodies, env state) over presence checks ("was this function called?", "was this branch taken?"). Code-path traces tell you what ran; data traces tell you what it ran on. Most non-trivial bugs are correct code processing wrong data.

Ask yourself: "If my hypothesis is correct, what will I see at point X? If it's wrong, what will I see instead?"

3. Verify Data Collection

Before running, confirm that your instrumentation output will actually be captured and accessible.

Common traps:

• stderr discarded by 2>/dev/null in the test command
• Process killed before flush (logs lost)
• Logging to a file in a directory that doesn't exist
• Output piped through something that truncates it
• Looking at log files from a previous run, not the current one

A test run that produces no data is wasted.

4. Run and Observe

Execute the test. Read the actual output — every line of it. Don't assume what it says.

When the data contradicts your hypothesis, believe the data. Don't rationalize it away. The whole point of this step is to let reality override your theory.

5. Document Findings

Update the side note with:

• What the data showed (quote specific log lines)
• What was confirmed vs. disproved
• Updated hypothesis for the next iteration

This is critical for not losing context across attempts. Hard bugs typically take 3-5 rounds. Without notes, you'll forget what you ruled out and waste runs re-checking things.

6. Iterate

Update the hypothesis based on the new evidence. Go back to step 2. Each round should narrow the search space.

If you're not making progress after 3 rounds, step back and question your assumptions. The bug might be in a layer you haven't considered.

Failure Modes to Avoid

These are the specific traps this methodology is designed to prevent. When you notice yourself drifting toward any of them, stop and return to the cycle.

Jumping to fixes without evidence

The most common failure. You have a plausible theory, so you "fix" it and run again. If the theory was wrong, you've added complexity, wasted a test run, and possibly introduced a new bug. The side note should always show "hypothesis verified by [specific data]" before any fix is applied.

Blaming external systems

"The model is hallucinating." "The API is flaky." "The library has a bug." These conclusions feel satisfying because they put the problem outside your control. They're also usually wrong.

Before blaming an external system, inspect what it actually received. A model that appears to hallucinate may be responding rationally to stale data you didn't know was there. An API that appears flaky may be receiving malformed requests. Look at the inputs, not just the outputs.

Inspecting code paths but not data

You instrument the code and prove it executes correctly — the right functions are called, in the right order, with no errors. But the bug persists. Why?

Because the code can work perfectly while processing garbage input. A function that correctly reads an inbox, correctly delivers messages, and correctly formats output is still broken if the inbox contains stale messages from a previous run.

Always inspect the content flowing through the code, not just whether the code runs. Check payloads, message contents, file data, and database state.

Reframing the user's report instead of investigating it

When the user reports a symptom your own run doesn't reproduce, the contradiction is the evidence — the two environments differ in some way you haven't identified yet. The wrong move is to reframe their report ("they must be on a stale SHA", "they must be confused about what they saw", "must be a flake") so that your run becomes the ground truth. Once you do that, every later piece of evidence gets bent to defend the reframing, and the actual bug stays hidden.

The right move: catalogue what differs between their environment and yours (TTY vs pipe, terminal emulator, shell, locale, env vars, prior state, build artifacts) before forming any hypothesis. For ambiguous symptoms ("no output", "it's slow", "it's wrong") ask one disambiguating question first — e.g., "does does it hang or exit cleanly?" That prunes the hypothesis space before any test run.

Losing context across attempts

After several debugging rounds, you start forgetting what you already tried and what you ruled out. You re-check things, go in circles, or abandon a promising line of investigation because you lost track of where it was heading.

This is why the side note file exists. Update it after every run. When you start a new round, re-read it first.

Persistent State: A Special Category

Features that persist data across runs — caches, session recordings, message queues, temp files, and database rows often cause "impossible" bugs. The current run's behavior is contaminated by leftover state from previous runs.

When behavior seems irrational, always check:

• Is there persistent state that carries across runs?
• Was it cleared before this run?
• Is the system responding to stale data rather than current data?

This is easy to miss because the code is correct — it's the data that's wrong.

When to Exit the Cycle

Apply the fix only when you can point to specific data from your instrumentation that confirms the root cause. Write in the side note:

Root cause: [specific mechanism]
Evidence: [specific log lines / data that confirm it]
Fix: [what you're changing and why it addresses the root cause]

Then apply the fix, remove instrumentation, and verify with a clean run.

Worked examples

• examples/headless-bg-agent-empty-stdout.md — pipe-captured runs all passed; the user's TTY printed nothing. The contradiction was the bug. Illustrates reproduction contradiction is data and instrument data, not code paths.