analyze_anomaly's records carried only the scored numeric columns plus a score, so
"which units look unusual?" could never name a unit; and it was the only ML tool
with no quality block, so an 86%-flagged run reached the assembler as silence.
- Add optional label_columns: identifier columns carried into each record
(identifiers first), never fed to the model. Planner guidance to pass the id
column follows on Rifqi's side (decided shape (a), tool-side).
- Add the reserved quality block: metric anomaly_rate, weak only ABOVE a 0.5
ceiling. A20's flag-rate finding was re-measured, not re-assumed -- clean noise
flags 0.14-0.22 (mean 0.17, p95 0.22), a genuine 30%-outlier frame 0.30, the
demo's degenerate run 0.86 -- so 0.5 clears the noise floor and a real finding
yet still catches "most rows flagged"; the note states the rate plainly either
way. Logic in _flag_rate_quality() so both branches are unit-testable (the fit
can't be pushed past 0.5 via contamination).
PR-0 from…
[NOTICKET] data_quality: actually count the duplicate rows it advertises (D-1)
analyze_data_quality's use_when lists "duplikat"/duplicates, but the file had no
duplicated()/drop_duplicates() anywhere -- asked "ada duplikat?" the user got a
confident per-column profile with no answer, and the assembler would infer one
from distinct_rate.
Add duplicate_rows {count, rate} over the profiled columns (df[cols].duplicated(),
keep="first" = the redundant copies; a column subset is a candidate-key check) and
issues.duplicate_row_count. Always present (0 when none, never None/absent), so the
advertised claim is now backed by a real count. PR-0 from
docs/plans/PR0_PR1_TOOL_FIXES.md.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
[NOTICKET] docs: PR-0/PR-1 spec for Sofhia, and the feedback-edge before/after
Rifqi Hafizuddin+1: Claude Opus 5
9c0fba7
docs/plans/PR0_PR1_TOOL_FIXES.md — six tool fixes, split into PR-0 (two that block QA)
and PR-1 (four that block nothing). Both stay on pr/22: the scope is one directory.
Every claim was RE-VERIFIED against the code today rather than taken from the review,
which was read-only and says so. That changed two of them:
- D-6 (pareto non-additive aggs) is already DOCUMENTED at pareto.py:24 — a deliberate
trade, not an oversight. The spec says so, and narrows the ask to the two real gaps:
the output still calls it "share" with no caveat, and negatives are unguarded.
- D-5 was dropped from Sofhia's scope entirely. The docstring states the empty-set
semantics are intended and correct; the actual gap is narrower (absent column vs
empty result) and lives in task_runner, which is not her lane.
D-12 also moved out — it is in planner/examples.py, Rifqi's file.
Flagged for her to DECIDE before starting: D-3's fix may need the planner to pass an
identifier column, which would c…
30 days ago
[NOTICKET] chore: untrack a render byproduct, and note what 7ffe004a swept in
Rifqi Hafizuddin+1: Claude Opus 5
171ac0c
Two corrections to the previous commit, fixed forward rather than by rewriting pushed
history.
1. docs/testing/agentic-flow.preview.html was tracked. It is a byproduct of the offline
excalidraw preview, regenerated on every render. Untracked and ignored.
2. 7ffe004a's message describes only the _bound threshold change, but the commit also
carried two files it never mentions:
docs/reviews/AGENTIC_DESIGN_REVIEW_2026-09-18.md (559 lines)
docs/testing/agentic-flow.excalidraw (80 elements)
Neither is mine. They are from a concurrent session working in this same tree, and
`git add -A` swept them into my commit. The work is legitimate and stays; what was
wrong was attributing it to a commit about something else. Recording it here so the
log is honest about which change is which.
Worth a decision, not made here: docs/testing now holds TWO flow diagrams whose names
differ only by a hyphen --
agentic_flow.excalidraw (mine, 109 elements, "T…
[NOTICKET] slow path: don't re-trim a payload the tool already bounded (A30 follow-up)
Rifqi Hafizuddin+1: Claude Opus 5
7ffe004
Sofhia's a2089edb caps analyze_anomaly's sample at source (200 -> 50) and reports
`anomalies_total` / `anomalies_shown` beside it. That is the right layer for it: my A30
cap protects what reaches the LLM, hers protects what gets persisted to
results_snapshot and traced to Langfuse, where the 50k blob was still landing whole.
But the two stacked badly. Her cap alone brings the padb payload to 1,180 tokens --
already sane. My generic list trim then cut it to 691 and left
anomalies_shown: 36 beside a list of 20 rows + "… (+16 more)"
The count is recoverable from the marker, and `anomalies_total` was exact throughout, so
nothing was ever wrong -- but a narrative reads straight past that, and "a number in the
payload that disagrees with the payload" is the exact class of bug this round has been
chasing.
Fixed at my end, not hers: `_bound` now leaves a value alone below _MIN_TRIM_CHARS
(8,000). The bound exists to stop floods, not to second-guess a tool that already
bounded itself …
[NOTICKET] anomaly: bound the returned sample at source, not just the assembler
analyze_anomaly's `anomalies` list could carry up to 200 records (all scored
columns + score each). A30 capped the same bloat at the assembler (what reaches
the LLM), but the tool output is ALSO persisted to results_snapshot and
Langfuse-traced, so the 50k blob still landed there.
Cap _MAX_ANOMALY_ROWS 200 -> 50 (a bounded top-by-score SAMPLE, not the full
set) and add anomalies_total / anomalies_shown so "N of M" stays honest. The
exact count (n_anomalies), rate, and chart are unchanged. Defense-in-depth with
A30's assembler cap, not a duplicate.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
[NOTICKET] docs: diagram of the agentic path, drawn from the code
Rifqi Hafizuddin+1: Claude Opus 5
10f94f3
docs/testing/agentic_flow.excalidraw.
The argument the shape makes: six intents leave the router, five are shallow, and only
structured_flow descends. Everything that guards correctness -- the validator's 10
checks, the retry loop, the S1a checkpoint, the infeasible exit -- sits on that one
spine. Strip the labels and the structure still says it: a narrow entry, a diamond
fanning to six, five short stubs, one long descending spine, converging to four outputs.
Built from the code rather than memory: the six intent branches are the ones in
chat_handler (lines 399-524), the slow-path order is the coordinator's Planner ->
TaskRunner -> Assembler with the checkpoint between, and the SSE order is the contract's
sources -> status -> chunk -> done. Carries three evidence artifacts -- a real TaskList
shape with the ${t1} handoff, the SSE event order, and the 14 tool names.
The rendered PNG is deliberately NOT committed. HF's pre-receive hook rejects binaries
that have not gone through Xet, a…
[NOTICKET] docs: fill bug 14's commit hash
Rifqi Hafizuddin+1: Claude Opus 5
198b4b3
Third time this round a hash had to be written after the fact rather than guessed.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
[NOTICKET] planner: surface numeric min/max, so thresholds aren't chosen blind
Rifqi Hafizuddin+1: Claude Opus 5
9155dcb
BUG 14, found by giving analyze_segmentation its first end-to-end run — it was the
last active tool never to have executed outside unit tests.
Asked to band `pa` into low/medium/high, the planner used thirds of 0-100 and all 42
rows landed in "high". A segmentation where every row is in one bucket says nothing,
and nothing in the pipeline flagged it: the tool did exactly what it was told.
The column runs 68.8-95.9. Go has populated stats.min/max on 98 of 98 numeric columns
all along; CatalogSummary dropped them, keeping only sample values. The planner did not
guess badly — it could not see the range.
Same root cause as A28 (constant columns): the catalog knew, the prompt discarded it.
It is also the Python half of the date-range gap handed to Harry — his side populates
dates, this side renders whatever is there, and neither half is useful alone. Date
columns render automatically once he ships.
Rendered as `range=<min>..<max>`, skipped for constant columns because their marker
alrea…
[NOTICKET] docs: real commit hashes in the dry-run log
Rifqi Hafizuddin+1: Claude Opus 5
9d36f1e
Second time this round I wrote a hash from memory before the commit existed: the three
validator checks are 1bf70654, not d0d18e8. Also replaced two 'this commit' placeholders
with 59715a00 and ec492813, which only read correctly in the commit that added them.
A wrong hash in a reference doc is worse than none — it sends the reader looking for
something that was never there.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
[NOTICKET] assembler: never state the finding a failed step was meant to produce
Rifqi Hafizuddin+1: Claude Opus 5
ec49281
BUG 13, found by the certification run (2026-09-17, analysis 62c34bc6).
analyze_correlation refused a non-numeric column (eqm_no), so nothing was measured.
The reply opened:
"Ya, ada indikasi hubungan antara backlog dan bad actor score..."
(Yes, there is an indication of a relationship...)
assembler.md rule 2 already said "do not pretend they succeeded" AND "do not lead with
the failure". The model obeyed both and invented the conclusion in between. Nothing in
the rule forbade stating the very finding the failed step existed to produce.
This is the dangerous class again: the analysis failed, the narrative claimed a result,
and the hedge arrived two clauses later — where it does not help, because the reader
takes the first clause.
Rule 2 now says so explicitly: not "yes, there is an indication", not "X appears to
drive Y", and not softened with "tends to" or "seems". Name the question that stays
open, and answer only what the successful steps support.
Verified with --turns…
[NOTICKET] eval(chat_sim): --turns, so verifying a late fix doesn't cost a whole suite
Rifqi Hafizuddin+1: Claude Opus 5
0f99ba0
--max-turns only ever takes a PREFIX, so checking a fix to the last turn of an 8-turn
suite meant paying for all eight: ~290k to verify one ~30k turn.
--turns 8 runs exactly that turn. First use, verifying the bug-12 fix: 57,734 tokens
against ~290k for the full suite.
History is still built from whatever runs, so a turn that depends on an earlier one is
selected together with it (--turns 2,8). Noted in the flag's help and in the doc.
Verified on the real suite: turn 8 alone now returns the honest refusal — it names both
tables, says no foreign key is declared between them, and offers the per-table summaries
it CAN produce. No validator vocabulary reaches the user.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
[NOTICKET] planner: exhaustion refuses instead of crashing, + the dry-run findings log
Rifqi Hafizuddin+1: Claude Opus 5
59715a0
BUG 12 — retry exhaustion showed the user the validator's own words.
Asked whether backlog relates to bad actor score, the planner burned all three attempts
(cross-table select, then a join the schema does not declare) and the turn ended with
Analysis failed: planner could not produce a usable plan after 3 attempts;
last error: joins[0]: (...) is not a declared foreign key
That is internal vocabulary about an internal contract, and it reads as a broken product
rather than a limit of the data. The SAME question refused cleanly on the previous run,
so this is reachable by chance, not by input.
Two changes. On the final attempt the planner is now told that refusing is a valid
outcome — until now the retry prompt only ever said "this plan was invalid, fix it",
which pushes toward another plan even when none exists. And if it still exhausts, the
service returns the infeasible sentinel instead of raising, reusing the path that
already handles this well: the coordinator renders it…
[NOTICKET] fix(planner): three checks for plans that lie about what they selected
Rifqi Hafizuddin+1: Claude Opus 5
1bf7065
All three come from the first pa_dummy run (2026-09-17, analysis 2ad2354c) -- the
first dry-run ever to reach the SQL path. Two of them produced a confident ANSWER
rather than an error, which is why nothing caught them before: nothing was looking.
CHECK 8f -- an alias may not rename a column into another thing that exists here.
Asked whether backlog relates to bad actor score -- two tables the schema declares no
FK between, so unjoinable -- the planner emitted
SELECT badactor.bad_actor_score, badactor.ma AS "backlog" FROM badactor
and the answer read "Korelasi Pearson antara backlog dan bad actor score adalah
-0,029". `ma` is Mechanical Availability. It is not backlog, and `backlog` is a
separate TABLE it could not reach. Rather than refuse, the plan renamed a different
column to match the user's word, and the alias made the wrong answer read as the
right one: every layer downstream, the narrative included, saw only "backlog".
This is the force-mapping recipes R6/R15 exist to …
[NOTICKET] eval(chat_sim): save a per-run transcript so answers can be audited
Rifqi Hafizuddin+1: Claude Opus 5
0e8f946
The answers ARE the result. Intents, tool names and token counts only say the
pipeline ran; the answer text is the one thing that says it was RIGHT, and the one
thing a person has to read to judge. Until now it existed only in whatever terminal
the run happened in.
That cost a real diagnosis already this week: a `tail` pipe swallowed the early turns
of a run, and the router decision behind a failed turn was simply gone — the re-run to
recover it cost 8.3k tokens and was avoidable.
Each run now writes eval/chat_sim/results/transcript_<suite>_<timestamp>.json holding,
per turn: the question, the router's intent + confidence + rewritten query, the full
answer text, the tools actually called, tokens in/out and latency — plus the run's
source and suite notes, and the report markdown. Timestamped and never overwritten,
matching the convention the other eval harnesses already use for results/.
run_turn now returns a TurnRecord instead of None, and print_turn_steps hands back the
token tota…
[NOTICKET] eval(chat_sim): point the suites at Rifqi's account, unblocking the DB suite
Rifqi Hafizuddin+1: Claude Opus 5
305d629
Rifqi controls 78e8df76 and has registered a database source there: "pa example"
(source_type=schema, 78bf9364) carrying the same four tables — backlog 42, badactor 42,
pa 42, pareto 126. That unblocks the padb suite, which had been written but unrunnable.
This is the first time any dry-run can reach SqlCompiler and DbExecutor at all. Every
fix so far (A26-A31) landed on the tabular path.
Verified before spending anything, with a hand-built IR and no LLM: count -> 42 rows,
and a year/month GROUP BY -> 6 rows with real SQL
SELECT "pa"."year", "pa"."month", AVG("pa"."pa") AS "avg_pa"
FROM "pa" GROUP BY "pa"."year", "pa"."month" LIMIT 10001
so the Fernet credential path decrypts cleanly (no empty-error InvalidToken) and the
compiler emits valid SQL. Noted for the run: the DB path returns numerics as Decimal
where the tabular path returns float — a difference the analyze_* tools have never
been exercised against.
`generated` repointed to this account's copy (d7de686c). `pa` ke…
[NOTICKET] eval(chat_sim): four suites, and a refusal when a source isn't registered
Rifqi Hafizuddin+1: Claude Opus 5
2a6b79e
Adds --suite {pa,generated,padb,cycle}. Each pairs a data source with a question set,
because a question is only meaningful against the shape of the data it runs on: every
floor in the tool layer (_MIN_POINTS, _MIN_BACKTEST_POINTS, _MIN_ROWS) is a statement
about row and period counts, so wording that passes on one source legitimately fails
on another. Each suite records the counts its questions depend on.
pa the existing 7 turns, unchanged (DEFAULT_TURNS now derives from it,
so there is one source of truth rather than two)
generated PA Data Dummy Generated.xlsx, 9,600 rows -- same schema and the same
April 6-25 span as `pa`, different values. Same wording on purpose:
the question is whether A26-A31 were overfitted to one file, and three
of them were found through properties specific to that file, so any
difference in outcome should be attributable to the DATA
padb pa_dummy over Postgres -- the SQL p…
[NOTICKET] docs: correct a fabricated commit hash in A30/A31
Rifqi Hafizuddin+1: Claude Opus 5
8603efc
I wrote 1fcb8a1 into the A30 and A31 rows from memory while drafting; no such commit
exists. The real one is a6c79c9c. A wrong hash in DEV_PLAN is worse than none — it
sends the next reader looking for something that was never there.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
[NOTICKET] eval(chat_sim): mint a message_id per turn, closing the traceability gap
Rifqi Hafizuddin+1: Claude Opus 5
2a435e2
message_traceability and message_charts wrote ZERO rows on every dry-run, and I had
been reporting that as a known harness gap without proving which side was at fault.
Both Python-owned per-turn writes are gated on a message_id: the traceability flush
returns early when `pad.message_id is None`, and chart persistence is guarded by
`if pad is not None and pad.message_id and analysis_id`. POST /api/v2/chat/stream
mints one (`_mint_message_id`); this script called `handler.handle()` without it, so
the parameter defaulted to None and both writes silently no-opped.
So the tables being empty said nothing about the product. It meant chart persistence
and the traceability panel went unexercised on every run — a gap in the evidence
that looked like a result.
Minting one per turn is safe: `message_id` is plain `text NOT NULL` with no foreign
key. Only `analysis_id` references analyses(id), and setup_analysis already creates
that row.
Verified (analysis_id f4471bf1, 2 turns): 2 traceability r…
[NOTICKET] docs: A23 clean run — 6/6 turns, and the four bugs it took to get there
Rifqi Hafizuddin+1: Claude Opus 5
534b053
Fourth attempt is clean (analysis_id 85f4b41d): 6 of 6 recorded turns, zero planner
retries, zero tool errors, zero slow-path failures, every structured turn in exactly
4 LLM calls. Report v1 from 6 records with 5 embedded charts. 168,571 tokens
(in 160,278 / out 8,293) across 7 turns.
The data_quality turn -- the one that took four attempts to reach -- came back with a
real business finding: Plan_PA_Percent has a minimum of -25.0.
Records A29 (malformed plan crashed instead of retrying), A30 (stats and meta flooded
the assembler prompt, 172k -> 124k -> 29.7k on the anomaly turn) and A31 (check 7 only
spoke English, which was upstream of A29).
Stated plainly in A23 rather than left implied: message_traceability and
message_charts still write zero rows. chat_sim drives ChatHandler directly without
minting a message_id and both writes are gated on one, so chart persistence and the
traceability panel are NOT exercised by this harness. The real /api/v2/chat/stream
path mints one and doe…
[NOTICKET] fix(planner/slow path): checkable criteria in Indonesian, bound meta too
Rifqi Hafizuddin+1: Claude Opus 5
a6c79c9
BUG 7 -- check 7 only spoke English, on a majority-Indonesian product.
_CHECKABLE_TOKENS was ("rate", "count", "match", "produced", "above", "below",
"equal"). The planner writes success_criteria in the language of the question, so an
Indonesian criterion could not pass except by luck. Every Indonesian turn in the
dry-run burned a retry here, and one degenerated past that: told "not checkable"
twice, the model dropped `success_criteria` AND `expected_output` altogether and the
turn died. The field-dropping looked like a schema bug (Bug 5) but this was upstream
of it -- Bug 5's fix caught the fall, this removes the push.
Added the Indonesian equivalents. Parity, not loosening: each mirrors an English
token already trusted ("menghasilkan" = "produced", "jumlah"/"baris" = "count",
"minimal"/"lebih"/"kurang" = "above"/"below"), so a criterion that fails in English
still fails in Indonesian. Pinned by a test asserting a vague criterion is refused in
BOTH languages.
BUG 8 -- `meta` was st…
[NOTICKET] fix(slow path): retry malformed plans, bound stats in the assembler prompt
Rifqi Hafizuddin+1: Claude Opus 5
7a89a2b
Two bugs the 2026-09-17 dry-run surfaced, both found by running the thing rather than
by reading it.
BUG 5 -- a malformed plan crashed the turn instead of being retried.
Structured output came back with task t1 missing `expected_output` and
`success_criteria`. That fails inside `chain.ainvoke` -- pydantic, before
PlannerValidator ever runs -- and the call sat OUTSIDE the try. So this one failure
mode skipped the retry loop every other planner mistake gets, propagated raw, and
reached the user as their literal answer:
ERROR: Analysis failed: 2 validation errors for TaskList
tasks.0.expected_output Field required
A dropped field is exactly as retryable as a bad column_id. The call now sits inside
the loop, and the pydantic message names the missing field, so it is fed back
compacted (`loc: msg`, no docs URLs) and the next attempt is told what to fix. On
exhaustion the caller gets a PlannerError, never a raw pydantic dump.
BUG 6 -- `stats` outputs were rendered to the assemb…
[NOTICKET] fix(router): route data-condition questions to analysis, not inventory (A23 Bug 4)
Rifqi Hafizuddin+1: Claude Opus 5
e815376
"Data PA-nya bersih nggak? Ada kolom yang kosong?" routed to `check` at 0.96
confidence, so the slow path never ran and analyze_data_quality was never called. The
reply was a 36-row schema table whose "Boleh kosong" column is the catalog's declared
`nullable` SCHEMA FLAG -- confident, well formatted, and not an answer to the question
the user asked about the actual rows.
The prompt defined `check` as "what columns are in this table?", and "ada kolom yang
kosong?" is lexically a columns question. The only boundary offered was "what do I
have" -> check versus "analyze/compute/trend/correlate" -> structured_flow, and a
data-CONDITION question is neither phrasing.
Recast the boundary as EXISTENCE vs CONDITION: `check` answers what is there, from
metadata alone; the moment the answer depends on the values in the rows it is
`structured_flow`, because only computing over the data can answer it. Stated
explicitly that naming a column does not make it `check`, and that a `nullable` flag
says …
[NOTICKET] docs: diagnose the missing data-quality turn — a router miss (A23 Bug 4)
Rifqi Hafizuddin+1: Claude Opus 5
0ed7215
Re-ran turns 1-2 alone (analysis_id e3e3ccba, 8.3k tokens), this time writing the log
to a file instead of through a `tail` pipe, which is what cost me the evidence last
time.
The router classified "Data PA-nya bersih nggak? Ada kolom yang kosong?" as `check`,
not `structured_flow`, at 0.96 confidence. The slow path never ran, so
analyze_data_quality was never called and no record was written.
This is a new bug, and the most dangerous of the four, because it does not fail -- it
answers. The reply was a 36-row schema table whose "Boleh kosong" column is the
catalog's declared `nullable` SCHEMA FLAG, reading "—" for 35 of 36 columns. The user
asked an empirical question about the actual rows. The answer is confident, well
formatted, in correct Indonesian, and not an answer to the question -- while 8 of
those columns really do hold one value in every row. Nothing about it looks wrong on
stage.
Cause: intent_router.md defines `check` as "what columns are in this table?", and
"ada kolom …
[NOTICKET] docs: record the second A23 dry-run — 5/5 clean, three bugs confirmed
Rifqi Hafizuddin+1: Claude Opus 5
810f176
Re-ran the full 7-turn dry-run against PA Data Dummy.xlsx after A26/A27/A28
(analysis_id 65528bd9). Five of five recorded turns clean, both tools succeeding in
every one: trend, forecast, importance, anomaly, cluster.
All three bugs confirmed fixed on the real path, not just in tests:
- trend and anomaly reach their tools at all now (A26, the date filter)
- forecast returned a real 7-day projection, and its own caveat reads "summarized
with mean aggregation" -- the A27 alias visibly doing its job
- importance targeted `down_hour`, the RIGHT column, instead of the constant
Total_Hours. That is the half of A28 I explicitly could not guarantee, since it
depends on the model reading the new CONSTANT marker rather than on code.
Report generated: 5 findings sections, 4 embedded plotly charts.
Three things left open, recorded honestly rather than rounded off:
a) The data_quality turn produced NO record this run, though it did in run 1. I
cannot say why: I piped th…
Asked "what's driving down hours the most?", the planner targeted Total_Hours -- a
fixed 24-hour day -- instead of Total_Down_Hour, and analyze_importance correctly
refused: nothing can explain a number that never moves.
The catalog knew all along. Total_Hours carries distinct_count=1, min=24, max=24. But
CatalogSummary carried only sample_values and top_values and dropped every other stat,
so the fact never reached the prompt. The planner was not ignoring the evidence; it
never had it.
Two fixes, both deterministic:
1. The planner can now SEE it. ColumnSummary.is_constant, rendered as
"[CONSTANT: one value in every row -- cannot be a target or a grouping key]" and
ONLY when true, so a normal column costs nothing and the marker is conspicuous
exactly where it matters. A bool rather than the raw count, because constancy is
the only fact here that changes a planning decision -- a count on every column
would spend tokens saying something true but useless about the other …
[NOTICKET] fix(tools): accept `avg` wherever `mean` works (A23 Bug 2)
Rifqi Hafizuddin+1: Claude Opus 5
0f0af11
The planner and the tools disagreed about what "average" is called, and the planner
lost.
It fills in a QueryIR and a tool call from ONE prompt:
QueryIR AggFn count, count_distinct, sum, avg, min, max
tools SUPPORTED_AGGS sum, mean, count, min, max, median (+ nunique)
planner.md teaches `avg` three times because the IR requires it -- line 331 even says
"utilization, scores -> avg", which is exactly the PA case. So the planner wrote
agg="avg" into analyze_forecast and the tool raised UnsupportedAggregationError. The
dry-run lost its forecast turn that way. The planner was not wrong; it obeyed its own
prompt.
Blast radius was five tools, not one. analyze_trend, analyze_forecast, analyze_ab_test,
analyze_pareto and analyze_segmentation all take an `agg` and all five rejected `avg` --
only forecast happened to be exercised. analyze_aggregate failed differently and more
quietly: validator check 8c rejected the plan, burning a retry before execution.
Aliasing rath…
[NOTICKET] fix(query): align date filters with date32 columns (A23 Bug 1)
Rifqi Hafizuddin+1: Claude Opus 5
a6444fb
Every date filter against an uploaded FILE was broken, and had been all along.
A Parquet DATE column (arrow date32) reads back as *object* dtype holding
datetime.date; the IR carries its literal as a string. _apply_filters compared them
raw, so `>=` raised
'>=' not supported between instances of 'datetime.date' and 'str'
inside the executor -- BEFORE the planner's tools ever ran, which is why the dry-run
caveat named no tool at all. That killed 2 of 7 turns (trend, anomaly).
in/not_in were worse: .isin() raises nothing and matches NOTHING, so that filter
shape returned a confidently empty table. A wrong answer, not an error, and nothing
in the pipeline could have flagged it.
_align_temporal() now normalises both sides to datetime64/Timestamp. Three choices
worth stating, because each is a place this could have gone wrong:
- Gated on the SERIES being temporal, never on value_type alone. The planner
writes value_type; a mislabelled text column must not get dragged into da…
[NOTICKET] eval(chat_sim): retarget at PA Data Dummy for the A23 dry-run
Rifqi Hafizuddin+1: Claude Opus 5
db7d28f
The Titanic target was gone -- that user's catalog reads back ZERO sources, so the
harness could not have run at all. Retargeted at PA Data Dummy.xlsx (9,729 rows, 36
cols) under the user that owns it, and replaced the scripted turns with the A0 demo
flow, steps 1-7 (step 8, the report, is the capstone the script already runs).
The turns ask for DAILY movement, not monthly, and the reason is recorded in the file
because it is not obvious and will bite whoever reuses this: From_Date spans
2026-04-06..2026-04-25 -- 20 distinct days inside ONE month, verified through the real
query pipeline, not inferred from catalog sample values. At freq="month" the series is
a single period, under analyze_forecast's _MIN_POINTS (4), so a monthly question raises
NotEnoughDataError. At freq="day" there are 20 periods, clearing _MIN_POINTS and
_MIN_BACKTEST_POINTS (8), so the forecast can return a real MASE instead of "not
validated". The A0 flow's "12 bulan terakhir" and "next 6 months" would both have
…
[NOTICKET] planner: group-labelled recipes + the feasibility gate the eval caught (A18)
Rifqi Hafizuddin+1: Claude Opus 5
b56fb4c
Recipes, part 2 of A18. The table goes R1-R6 -> 18 rows, each labelled with its GROUP so
the tool list (organised by group, A17) and the rules (organised by recipe) stop being
two overlapping schemes the planner has to reconcile. New: R7 data-quality, R8 forecast,
R9 importance, R10 pareto, R11 segmentation, R12 clustering, R13 A/B test, R14 anomaly,
R15 method-infeasible. Plus a "telling the close pairs apart" block for the six
intra-group confusions the group hint cannot separate -- trend vs forecast, correlation
vs importance, importance vs pareto, segmentation vs clustering, A/B vs aggregate,
anomaly vs descriptive.
R15 is not a softer R6: R6 is "the column does not exist", R15 is "the column exists but
no tool does that". Numbering note -- the pre-implementation spec called the
method-infeasible recipe R12; the implemented table gives R12 to clustering and R15 to
method-infeasible. Stale references in DEV_PLAN, examples.py and groups.py corrected
rather than renumbering the live …
[NOTICKET] planner: ten exemplars, one per tool that had none (A18, part 1)
Rifqi Hafizuddin+1: Claude Opus 5
57f8cee
After A25 scoped few-shots to the visible tool set, three groups rendered ZERO examples
demonstrating their own tools -- relationship, grouping and evaluate each kept 7, none
of which called a tool from that group. forecast, data_quality and correlation had no
exemplar either, correlation never having had one. Those turns planned from DESCRIPTION
alone.
Ten new examples, N through W, one per undemonstrated tool, following the spec agreed in
DEV_PLAN before implementation:
N forecast pairs against B (trend): same retrieve, past vs project
O correlation two NAMED columns
P importance same retrieve as O -- so the contrast is the question alone:
"how do X and Y relate" -> O, "what most affects X" -> P
Q pareto arithmetic share, vs P's statistical explanation
R segmentation rule GIVEN -> explicit bins + labels
S cluster no rule -> discovered, vs R
T ab_test two named groups + a significance verdict
U anomaly …
[NOTICKET] docs: A18 exemplar spec — the ten questions, agreed before implementation
Rifqi Hafizuddin+1: Claude Opus 5
1048c6f
Writes the agreed A18 plan into DEV_PLAN before building it, so the decisions survive
independently of the conversation they were made in.
The gap it closes, measured: after A25 scoped few-shots to the visible tool set, three
groups render ZERO examples demonstrating their own tools -- relationship, grouping and
evaluate each keep 7, none of which call a tool from that group. forecast, data_quality
and correlation have no exemplar either, correlation never having had one. A hinted turn
in those groups plans from DESCRIPTION alone.
Two decisions recorded with their reasoning, because both went against an initial
proposal:
- Do NOT mirror the A0 demo questions. Sofhia proposed seeding exemplars from the demo
flow. Rejected on the A19 evidence: a question verbatim-matching a few-shot replays
that example's plan over what the catalog supports. If a demo question is also an
exemplar, the planner is maximally likely to replay fictional ids against the real
catalog, on st…
[NOTICKET] planner: unblock the ML tools A15 just activated (A24, A25)
Rifqi Hafizuddin+1: Claude Opus 5
3258aa2
A15 turned on all 18 tools. Two things would have kept the four ML tools effectively
unusable, both found while drafting the A15 handoff, both mine.
A24 -- modeling was still forbidden in CODE. prompt.py injected, on every planner call:
- modeling_allowed: False (no modeling tools exist in v1 -- do not emit modeling tasks)
Rule 5 in planner.md was rewritten to permit modeling, but this line sits in the
constraints block, is more specific, and flatly asserts the tools do not exist. The
planner would have been told to ignore forecast/anomaly/importance/cluster the moment
they went live -- four of the demo's payoffs. Constraints.modeling_allowed now defaults
True, and the rendered text follows the flag rather than asserting a fixed world. Kept
as a knob, not deleted: a caller can still force a strictly descriptive run.
A25 -- few-shots were not scoped with the registry, an A17 gap I introduced.
render_examples() took no arguments, so all 13 rendered regardless of the group hint. A…
[NOTICKET] feat(tools): activate all analytical tools (A15)
A0 decision = all 18 tools active. Moved the 8 new/renamed specs DEFERRED -> ACTIVE in registry.py; DEFERRED_ANALYTICS_TOOLS = [] (mechanism kept for future dark-shipping). ACTIVE = 14 analytics + 4 data-access; every category maps to an A17 group (verified). DEV_PLAN §0.9 A15 -> done.
NOTE: pairs with A18 (Rifqi) — the 8 newly-active tools have no planner few-shots yet (examples.py). They are visible via grouping + their DESCRIPTION, but few-shots should land before the demo.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
A0 activated every analytics tool, which puts the planner-visible set at 18 -- inside
the measured 15-20 band where tool-selection accuracy degrades, and this registry has
exactly the semantic near-duplicates that drive it (trend/forecast,
correlation/importance, segmentation/cluster). A0 chose to pay that cost with grouping
rather than by turning tools off, so nothing goes dark.
Three layers, and which does what is the whole design:
router (LLM, already runs) -> emits group_hint on its existing structured output
-> planner/groups.py (deterministic, 0 LLM) -> filters the registry
-> planner (LLM) -> plans against the slice, unchanged
The filter is deliberately NOT the planner's job: to choose a group it would have to
be shown every group, which is the thing being avoided, and a second LLM call would
break the two-call invariant for a benefit that only pays above ~60 tools. It is not
the router's job either -- the router classifies, it does not filter.
Groups are DERIVED…
[NOTICKET] fix(tools): analyze_ab_test quality — KeyError on the too-few-rows path
The quality block (added in 2082eba1) read test['p_value'] directly, but _welch_test's too-few-rows branch returns no 'p_value' key -> KeyError, so analyze_ab_test crashed on a valid 'too few rows for a t-test' case (became an error envelope via the never-throw seam instead of a graceful result). Fix: test.get('p_value'). The too-few case now returns significant=None + quality {value: None, weak: True}. Caught by a local ab_test test.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Extends the reserved `quality` convention to the tools where it means something, and
fixes a CK7 message that was wrong for half its inputs.
analyze_forecast -- the substantial one. It reported NO accuracy at all, so "how
accurate is it?" had no answer. Now backtests: hold out the last h periods (bounded by
a quarter of the series and by the fitting floor), refit, and score with MASE -- mean
absolute error over the naive "next = last" error on the training span. MASE is
scale-free (Hyndman & Koehler 2006) and, unlike MAPE, neither explodes near zero nor
punishes over-prediction asymmetrically. Both fits run in ONE run_fit call, so a
forecast still costs one thread hop and one timeout. A series too short to hold
anything out, or one that never moves, ships UNVALIDATED -- also weak, with the two
causes reported as distinct reasons rather than one wrong one.
analyze_ab_test -- free and exact: the statistical verdict IS the quality verdict. A
gap the test cannot separate from noise is we…
[NOTICKET] planner: rule 5 forbade the modeling tools we just shipped
Rifqi Hafizuddin+1: Claude Opus 5
855e406
planner.md rule 5 read "No modeling in v1. There are no modeling tools... The
product is descriptive/diagnostic only -- no predictions." Four ML tools have since
landed (forecast, anomaly, importance, cluster), so on every planner call the prompt
was instructing the model not to use tools the registry was about to offer it. The
CRISP-DM bullet said "(Never `modeling`.)" for a stage the schema has always declared
legal and the validator never restricted.
Rule 5 now:
- is phrased CONDITIONALLY ("when the Available tools list includes a modeling
tool"), so it is true both today -- all 8 ML tools are DEFERRED, none visible --
and after A0 activates some. The tool list is injected per call; rule 3 already
governs what exists.
- keeps both refusals the 2026-09-15 discussion made: no AutoML, and no
model-building as an end in itself ("membuat model, bukan melakukan analisis").
- extends the Example-K feasibility discipline to modeling: an explicit ask to
forecast or…
Renames analyze_comparison/comparison.py -> analyze_ab_test/ab_test.py. Keeps the two-group aggregate gap; adds a Welch two-sample t-test (t, p-value, significant at alpha) on the raw values and a bar chart of the two group means with 95% error bars, via the reserved 'charts' key. Registered DEFERRED via spec_from_model. Old name removed — 5 cross-ref DESCRIPTIONs + contracts.py comment repointed. DEV_PLAN §0.9 A11 -> done.
Heads-up (Rifqi): analyze_comparison renamed to analyze_ab_test. Local tool tests under tests/unit/tools (gitignored) referencing it break on pull — rename to analyze_ab_test.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
A8 and A10. Both files are INERT on purpose: not in _DISPATCH, not in either
registry list. registry.py and invoker.py are the two files every tool addition
touches, so they stay single-owner until Sofhia makes the call -- the DEFERRED
mechanism means an unregistered tool costs nothing.
analyze_importance -- rank what explains a target.
Standardised coefficients, NOT permutation importance. For a linear model the
coefficient IS the importance and comes free with the fit; permutation re-scores
once per feature, which is the E4 latency tall pole, and buys nothing here.
Ridge for a numeric target, Logistic for a categorical one, auto-detected.
Leakage-safe: the scaler is fitted on the TRAIN split only, so no test statistic
reaches the fit -- the thing R-3 and the survey literature both single out.
analyze_cluster -- discover groups nobody named yet.
KMeans with k chosen by silhouette over 2..8, every candidate score returned.
Scatter plots two REAL columns rather than PC…
[NOTICKET] docs(knowledge): bring the persistence contract current so it can be sent
Rifqi Hafizuddin+1: Claude Opus 5
ff27029
This doc calls itself "the DDL handoff" and was the obvious thing to send Harry
alongside the SQL - but it had drifted to the point where sending it would have
told him the opposite of the truth. It described the 2026-09-07 five-table shape
and knew nothing of the seven columns added 2026-09-14.
Fixed:
- Section 2 tables now match LIVE, each addition carrying a dated marker:
entries gains doc_ids/content/provenance, nullable parents with the domain
CHECK, 'document' in the kind CHECK, and the key index; documents gains
withdrawn_at and the partial live index; reviews gains decided_by and
authorised_by; jobs gains review_mode. The label column's CHANGED meaning is
called out - it carries a readable key now, not the term, and a consumer
rendering it as a display name will show GLOSSARY_PA.
- Banner no longer contradicts itself. It opened with a green LIVE line and then
carried, in bold, "Nothing here exists in dedorch yet and no ORM model has
been written against it". …
Activates + renames analyze_segment -> analyze_segmentation (file segmentation.py unchanged). Keeps edges/quantile binning; adds a per-segment bar chart (count, or the aggregate when value_column given) via the reserved 'charts' key. Registered DEFERRED via spec_from_model. DEV_PLAN §0.9 A13 -> done.
Heads-up (Rifqi): analyze_segment renamed to analyze_segmentation. Local tool tests under tests/unit/tools (gitignored) referencing it break on pull — rename to analyze_segmentation. Also a stale '# analyze_segment' comment remains in src/agents/planner/validator.py:152 (your lane) — repoint when convenient.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Renames analyze_profile/quality.py -> analyze_data_quality/data_quality.py. Keeps the per-column profile; adds an issues summary (constant + high-null columns) and a missing-rate bar chart via the reserved 'charts' key. Registered DEFERRED via spec_from_model. Old name removed — no eval/agent refs; descriptive cross-ref repointed. DEV_PLAN §0.9 A14 -> done.
Heads-up (Rifqi): analyze_profile is gone. Local tool tests under tests/unit/tools (gitignored) that reference it will break on pull — rename to analyze_data_quality.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Renames analyze_contribution/decomposition.py -> analyze_pareto/pareto.py. Keeps the share + cumulative compute; reframes to Pareto 80/20: adds categories_for_80pct and ships the pareto_chart (bars + cumulative-% line) via the reserved 'charts' key. Registered DEFERRED via spec_from_model. Old name fully removed — no eval/agent refs; 3 cross-ref DESCRIPTIONs (aggregation/comparison/segmentation) repointed. DEV_PLAN §0.9 A12 -> done.
Heads-up (Rifqi): analyze_contribution is gone. Local tool tests under tests/unit/tools (gitignored) that reference it will break on pull — rename those refs to analyze_pareto.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
[NOTICKET] feat(report): embed analyze_* auto-charts, share one chart extractor (A21)
Rifqi Hafizuddin+1: Claude Opus 5
1c36d9a
Closes the gap Sofhia flagged at handoff: an analyze_* tool's auto-chart appeared
under the chat answer and silently vanished from the downloaded report, because
report/generator._collect_charts only ever looked at render_chart's chart-kind output
and never at ToolOutput.meta["charts"].
Rather than copy the meta-charts logic into a second place, the extraction moved to
one definition -- src/charts/extract.py::chart_envelopes -- now used by BOTH consumers:
the per-turn ChartStore persistence and the report's ## EDA section. That is the actual
fix; the missing charts were a symptom of having two implementations of "what charts
does this output carry", only one of which was updated in A4.
Also tightened, and this is a small behaviour change worth naming: an envelope must
carry a `plotly` object to be emitted. The report path already required that; the
persistence path accepted any dict, so a malformed envelope could be written as a
message_charts row the frontend could not draw. Both pa…
A structured_flow turn took ~12s behind three stage pings -- "Planning", "Running N
steps", "Composing". Past Miller's ~10s attention threshold what a waiting user needs
is evidence that something is happening, not a faster answer, so the TaskRunner now
announces each wave BEFORE running it. A 4-task plan emits ~6 status events, not 3.
New src/agents/slow_path/progress.py holds the labels:
- BILINGUAL. The three existing stage pings were English-only on a product whose
traffic and eval set are majority Indonesian; they follow reply_language now, the
same "id" if reply_language == "Indonesian" test refusals.py uses.
- A bounded activity vocabulary keyed by tool ("retrieving data" / "mengambil
data"), NOT the task's objective. Plan internals are deliberately English, so
using objective as status text would show English prose to an Indonesian user.
- Unknown tools fall back to "analysing" / "menganalisis", so the six tools still
to land (DEV_PLAN 0.9) produce s…
[NOTICKET] docs: mark A7/A9 done + add forecast & anomaly to REPO_STATUS
DEV_PLAN §0.9: A7 analyze_forecast + A9 analyze_anomaly -> done (dae102f6, 9f3267d5). REPO_STATUS: forecast + anomaly added to the changelog header, §6 feature list, and §9 tool layer (first two ML tools, DEFERRED; first run_fit user + invoker await-coroutine note).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
[NOTICKET] feat(tools): analyze_anomaly (Isolation Forest) — second ML tool, DEFERRED
analyze_forecast: statsmodels ETS (Holt-Winters, additive trend, no AutoML), reusing analyze_trend's date parsing + resampling. Auto-chart (actual-vs-forecast line) via the reserved 'charts' key; deterministic output. Registered in _DISPATCH + registry (spec derived from ForecastInput via spec_builder) as DEFERRED — activation (ACTIVE + examples.py few-shot) follows once A0 scoping locks P1.
Wires the ML executor for the first time: the fit is offloaded via run_fit, and the invoker now awaits coroutine compute fns (inspect.isawaitable — opt-in; sync tools unchanged). This resolves the conventions §4/§6 contradiction toward 'sync unless it fits a model'; doc §6 wording needs a follow-up tweak.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
