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An error notebook can make an AI coding agent less likely to repeat a known mistake, but it is not a self-proving safety system. In Xin & EQ’s July 2026 account, the notebook held 266 trigger-and-action rules across two months and recorded whether selected rules appeared to intercept recurring errors. The same workflow adds a completion check: the agent must search every file it claims to have changed and repair missing edits before declaring the task finished. You can reproduce the basic check with a short prompt, or automate it with a local Python script that calls grep -cF. The reported results are one author’s engineering practice—one user and 164 sessions—not a controlled benchmark.
This article explains the rule structure, the interception figures, a runnable auditor, and the trade-offs between manual notes, prompt-only verification, and local automation.
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What is an error notebook for an AI agent?
An error notebook is a collection of dynamic lessons written as trigger-and-action rules: when a recognizable failure pattern appears, the agent performs a specified check or correction. That differs from a static preference such as “Use Kotlin.” A preference describes a standing choice; a notebook rule tells the agent what to do when a situation occurs.
The practice described by Xin & EQ is documented in their July 8, 2026 DEV Community article. It is a personal engineering system, not a generally validated intervention. Rules are divided by how often they are needed:
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| Layer | Rules reported by Xin & EQ (2026) | How it is used |
|---|---|---|
| Core | 14 | Injected at every session startup. |
| Task | 88 | Loaded according to the task type, selected with simple keyword matching. |
| Archive | 164 | Kept searchable and retrieved only when a relevant lesson is needed. |
| Total | 266 | Accumulated over two months in the author’s account. |
The separation limits prompt clutter: always-relevant rules stay visible, task-specific rules appear only for matching work, and older material remains available without consuming every session’s context. The article mentions semantic retrieval as a possible improvement to keyword selection; it does not report results from such a system.
How does the correction loop work?
The notebook turns a correction into a reusable guardrail, then records whether that guardrail appears to help. A practical loop has five stages.
- Capture the trigger. When the agent makes a mistake and you correct it, describe the observable pattern rather than the whole conversation. “Marked a file complete without checking its contents” is a useful trigger; “be more careful” is not.
- Extract one action. Convert the correction into a short imperative rule. The article’s example is: “When marking a file modification as ‘complete,’ verify with a search command that the file actually contains the modified content.”
- Place the rule. Put a broadly applicable rule in Core, a work-type rule in Task, or a rarely needed lesson in Archive. Avoid copying the same rule into multiple layers.
- Inject before work starts. Core rules are included at session startup. Task rules are selected from the requested work using keyword matching, so naming the task clearly affects what is loaded.
- Record the outcome. Note whether the rule was present, whether it intercepted the error, and whether it failed. A rule that is repeatedly ignored should be rewritten, moved, or demoted rather than blindly accumulated.
This creates a feedback loop, not a guarantee. An interception means the rule appeared to prevent or catch the specific pattern being watched; it does not establish that the entire task was correct.
What do the reported interception figures actually show?
Xin & EQ report conditional counts for three recurring lessons. The percentages describe outcomes when the relevant rule was present, not a measured reduction in all coding errors.
| Lesson being watched | Interceptions | Failures | Reported interpretation |
|---|---|---|---|
| Skipping the manual before starting work | 66 | 4 | 94% conditional interception rate (66 of 70 observed cases). |
| Equating “draft exists” with “task done” | 11 | 0 | All 11 observed cases were reported as intercepted. |
| Detouring around errors instead of fixing the root cause | 24 | 2 | 24 of 26 observed cases were reported as intercepted. |
These are the author’s figures for 2026, based on one user and 164 sessions. The article says the counts are self-reported, some logs were spot-checked, and not every count was verified. It also notes that the underlying model may have improved independently, which is a confounding factor. Treat the table as an operational log from one account, not as a comparative study or a promise that your agent will achieve the same rate.
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How do I check whether my AI agent actually changed the files it claims to have changed?
Give the agent an explicit verification contract: list each claimed file, define an expected string or pattern for that file, report present and missing cases, and fix missing changes before the completion message. This catches a common failure mode—an agent edits one file, describes edits to several, and never checks the filesystem.
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A prompt-only check
You can paste a request like this after the agent proposes that its work is complete:
For every file you claim to have changed, verify the change on disk before reporting completion. For each file, provide: (1) the path, (2) the exact string or pattern that should be present, (3) whether the file is present and readable, and (4) whether the pattern is present. Report PRESENT, MISSING_PATTERN, or MISSING_FILE_OR_INACCESSIBLE. If any expected change is missing, make the edit, run the check again, and do not claim completion until every required pattern is present. A matching string confirms presence only; it does not prove the implementation is complete or correct.The wording forces an observable test instead of accepting the agent’s narrative. Keep expected patterns specific enough to identify the intended edit, but stable enough that formatting changes do not create needless failures.
A small local auditor
The article’s demonstration uses Python and grep -cF to distinguish a missing pattern from a file that cannot be read. Save this adapted example as verify_changes.py, replace the sample checks with your own paths and strings, and run it from the project directory:
#!/usr/bin/env python3
from pathlib import Path
import subprocess
checks = [
("src/service.py", "def save_record("),
("README.md", "## Configuration"),
]
for filename, expected in checks:
path = Path(filename)
if not path.exists():
print(f"MISSING_FILE: {filename}")
continue
if not path.is_file():
print(f"INACCESSIBLE: {filename} (not a regular file)")
continue
try:
result = subprocess.run(
["grep", "-cF", "--", expected, str(path)],
capture_output=True,
text=True,
check=False,
)
except OSError as exc:
print(f"INACCESSIBLE: {filename} ({exc})")
continue
if result.returncode == 0:
print(f"PRESENT: {filename} ({result.stdout.strip()} match(es))")
elif result.returncode == 1:
print(f"MISSING_PATTERN: {filename}")
else:
detail = result.stderr.strip() or "grep failed"
print(f"INACCESSIBLE: {filename} ({detail})")Run it with python3 verify_changes.py. GNU and BSD implementations of grep both support the fixed-string count options used here, but a restricted environment may not provide the command. In that case, replace the subprocess call with Python text reading and handle PermissionError explicitly.
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A PRESENT result only establishes that the literal string occurs somewhere in the file. It does not prove the change is in the correct function, that tests pass, that a configuration value is valid, or that duplicate text has not created a false positive. Pair the check with tests, a diff review, or a parser-level assertion when correctness matters.
Which implementation level should you use?
Choose the lightest layer that gives you repeatable evidence. The three approaches differ mainly in setup effort and how much checking happens without memory or discipline.
| Approach | Setup effort | Repeatability | Automation | Best fit |
|---|---|---|---|---|
| Manual rule storage and paste | Lowest | Depends on remembering to paste the rule | None | Exploring a lesson or changing prompts frequently. |
| Verification prompt | Low | Good when the same prompt is reused | The agent performs the search and reports results | Individual tasks where installing a script is inconvenient. |
| Local auditor | Moderate | Highest; the same command can run every time | Filesystem checks run independently of the agent’s prose | Repositories with recurring edits, CI hooks, or review gates. |
Start with a prompt while you learn which claims need checking. Promote a check to a script when the same failure costs time repeatedly. Keep the expected-file list close to the task or test configuration so it changes with the code rather than becoming another forgotten document.
How should you write rules that agents can use?
Good rules are short, specific, and tied to an observable trigger. They should tell the agent what to do next, not merely describe a value you prefer.
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Use a trigger and one action
- Weak: “Be thorough with documentation.”
- Stronger: “When claiming a documentation task is complete, search each named file for the new heading and report the result.”
If a rule needs several unrelated actions, split it. A concise rule is easier to inject, audit, revise, and remove.
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Keep Core small without treating a count as a law
The account used 14 Core rules. The article also discusses an approximate engineering heuristic of keeping always-loaded instructions around a small number, but that is not a universal threshold and should not be presented as an externally validated limit. Measure your own context budget and move narrow lessons to Task or Archive when they stop applying broadly.
Make retrieval labels predictable
Because Task selection used simple keyword matching, use consistent terms for work categories such as migration, testing, or documentation. Keyword matching can miss an indirect correction expressed with different words; semantic retrieval may help, but no result for that future improvement is reported here.
Revise rules that are ignored
When a rule is present but the agent repeatedly violates it, inspect the trigger and the action separately. The trigger may be too narrow, the action may be ambiguous, or the instruction may be buried by more recent context. Rewrite or demote it; do not assume that adding another similar sentence will solve the problem.
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The notebook addresses a narrow class of repeatable mistakes. Its limitations determine where you need stronger controls.
- Indirect corrections are missed. A keyword trigger may not recognize that a new task is the same failure in disguise.
- Rules can be ignored. Presence in a prompt is not compliance. Verification must produce an observable result.
- Context space is finite. Loading too many Core or Task rules can crowd out the code and requirements the agent needs for the current task.
- Literal checks can mislead. A string can appear in a comment, a dead branch, or the wrong file location. Use tests or syntax-aware checks for higher assurance.
- Filesystem access can fail. A missing path, permissions problem, container boundary, or generated file can look like an absent edit. Report these states separately rather than treating every non-match as a code error.
- Hard constraints need enforcement outside the prompt. If an edit must never ship without a test, lint run, or schema check, enforce that requirement in scripts or continuous integration instead of relying solely on a notebook rule.
How can you adopt this workflow without creating another maintenance burden?
- Start with three failures. Choose mistakes that are concrete, recurring, and cheap to detect, such as skipping a required manual, confusing a draft with a finished deliverable, or bypassing a root-cause fix.
- Write one-sentence rules. Use the form “When X triggers, execute Y,” and include the command, file, or artifact the agent must inspect.
- Assign a layer. Put cross-project checks in Core, task-specific checks in Task, and historical or rare lessons in Archive.
- Add a completion manifest. For each task, list changed files and an expected marker, then run the prompt or local auditor before accepting the agent’s summary.
- Log outcomes. Record present, missing, intercepted, and failed states. Include the session or task identifier so you can distinguish a genuinely improved rule from a one-off success.
- Prune deliberately. Merge duplicates, rewrite rules that are too vague, and archive lessons that no longer match your toolchain. A smaller accurate set is more useful than an ever-growing prompt.
This process preserves the useful part of the notebook—specific memory tied to action—while keeping verification independent enough to catch an overconfident completion message.
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Is the reported 94% rate proof that the method works?
No. It is a conditional interception figure from one personal account, calculated only for cases in which the relevant rule was present. The article does not establish a control group, a standardized error definition, independent log validation, or a separation between notebook effects and model improvement. Use the figures to understand how the author tracked outcomes, not to forecast performance for another agent or repository.
The strongest transferable idea is narrower: turn corrections into explicit actions, load only relevant actions, and require an observable check before accepting a completion claim. Those practices are useful even when a notebook never produces a clean percentage.
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Frequently Asked Questions
Can an error notebook replace automated tests?
No. Notebook rules can remind an agent to run tests or inspect files, but they do not establish behavioral correctness. Keep unit, integration, lint, and security checks in the normal development pipeline.
How should I handle generated or binary files?
Use a tool that understands the file format or verify a checksum, metadata field, or generated-source input instead of searching raw bytes. Mark such checks separately so a text-search failure is not mistaken for a missing edit.
What should I record when a rule fails?
Save the triggering task, the exact rule text, whether the rule was loaded, the agent’s response, and the independent check result. That information tells you whether retrieval, compliance, or the check itself needs repair.
When is keyword matching no longer enough?
Move beyond keywords when the same lesson appears under varied terminology or when task names are inconsistent. A structured tag scheme or semantic retrieval can improve selection, but it should still be evaluated against missed and irrelevant rule loads in your own workload.
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