# Session Analysis: rollout-2026-01-29T20-37-57-019c0b7a-18f3-7e12-be90-2d0c46b8e7c2.jsonl

**Date**: 2026-01-29 | **Duration**: 288s | **Turns**: 6 | **Model**: gpt-5.2-codex
**Type**: bug-fix | **Outcome**: resolved | **Specificity**: 4/5 | **Struggle**: 4/10

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## Journey

The developer had an error in their ML training script (`col/src/model/training.py`) within a cold-ranker project and asked the Codex agent to fix it. They provided helpful IDE context -- the active file and open tabs -- but did not specify what the error was. The agent proceeded through a methodical investigation:

1. **Sandbox friction (turn 1):** The first `ls` command failed with a landlock sandbox panic. The agent self-recovered by re-running with escalated permissions.
2. **File inspection (turns 2-3):** The agent read `training.py` in two chunks (lines 1-200, then 200-400) using `sed`.
3. **Root cause identification (turn 4):** Used `rg` to search for `total_batches` across the file, discovering it was referenced but never defined -- a `NameError` waiting to happen. The agent also cross-referenced other files (`dataset.py`, `config.py`) to understand how batch counting should work.
4. **Multi-bug discovery (turn 5):** Beyond the undefined variable, the agent identified two additional bugs: `test_shard_dir` being improperly reset to `None` in prod mode, and `get_pos_series` listing the wrong directory for prod shards.
5. **Fix and verification (turn 6):** Applied fixes (adding a `_count_batches` helper, renaming the variable, fixing shard directory handling) and performed multiple verification passes checking indentation and edge cases.

## Prompting Assessment (4/5)

**What worked:**
> "# Context from my IDE setup:\n\n## Active file: col/src/model/training.py\n\n## Open tabs:\n- training.py: col/src/model/training.py\n- model.py: col/src/model/model.py\n- config.py: col/src/config/config.py\n- dataset.py: col/src/model/dataset.py\n- driver.py: col/src/driver.py\n\n## My request for Codex:\ncan you fix error in training.py"

The IDE context (active file, open tabs) was automatically sent, giving the agent a strong starting point -- it knew exactly which files were relevant. This is a solid use of IDE integration.

**What was vague or could improve:**
> "can you fix error in training.py"

The request does not specify which error, whether it is a runtime exception, a logic bug, or a lint warning. There is no stack trace, no error message, and no description of what the developer observed. The agent had to deduce the bug from static code analysis alone. Including a traceback would have cut investigation time significantly.

## Outcome: resolved

**Evidence:**
> "Fixed the undefined `total_batches` reference and kept the test shard path intact for `MODE=\"prod\"` so training doesn't pass `None` into the dataloader. I also corrected the prod shard listing in `get_pos_series`."

The agent identified and fixed three related bugs: the undefined `total_batches` variable causing a `NameError`, the prod-mode `test_shard_dir` being incorrectly set to `None`, and the wrong directory being listed in `get_pos_series`. It applied code changes and verified them across multiple review passes.

## Struggle Points

The main friction was the sandbox environment, not the coding task itself:

> "thread 'main' (18403) panicked at linux-sandbox/src/linux_run_main.rs:30:9:\nerror running landlock: Sandbox(LandlockRestrict)"

Every subsequent shell command required `sandbox_permissions: require_escalated`, adding latency to each tool call. The agent handled this gracefully after the first failure, but it added overhead to a task that involved 12 shell commands.

The agent also showed thoroughness that bordered on over-verification:

> "The list comprehension in get_pos_series has unindented lines matching the opening bracket's indentation, which is syntactically valid but visually misleading; this affects readability but not execution."

This level of style-checking on code the user did not ask about extended the session without changing the outcome.

## Aha Insight

The agent went beyond the single bug the user likely encountered and found a cluster of related issues:

**Evidence:**
> "I spotted likely bugs in training.py: undefined total_batches, improper resetting of test_shard_dir for prod mode, and incorrect directory listing in get_pos_series for prod. Fixing these could resolve the user's training errors before inspecting datasets."

This is a good example of an agent proactively finding related bugs during investigation. The undefined variable would have been the immediate crash, but the shard directory bugs would have caused silent data errors or a `FileNotFoundError` later in the training pipeline.

## Coach Tip

Always paste the exact error output (stack trace, error message) when asking an AI agent to fix a bug. Even a single line like "NameError: name 'total_batches' is not defined at line 315" would have let the agent skip the exploratory file reading (4 of 12 tool calls) and jump straight to the fix. The IDE context was a great start -- pairing it with the traceback would make this a sub-60-second resolution instead of nearly 5 minutes.
