[NOTICKET] slow path: don't re-trim a payload the tool already bounded (A30 follow-up)

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 and described what it did. A tool's self-description should not be
invalidated by the prompt builder.

The A30 protection is unchanged for tools with no cap of their own: a 2,000-row payload
still trims to 20 items and stays under 1,500 tokens. Both directions are pinned by
tests.

Checked her caveat about reading `len(anomalies)` for a count: nothing on this side
does. The only `len(anomalies)` is in charts.py:309, and it is a different variable --
a DataFrame slice of the full frame inside anomaly_scatter_chart -- so the chart still
plots every anomaly and her cap does not reach it.

One test moved to the new intent: test_nested_lists_are_trimmed_too used a small
payload that now sits under the threshold, so it was rewritten to use one large enough
to trigger trimming. Its intent (trimming reaches nested structures) is unchanged.

Suite 883 passed, 7 skipped. ruff clean on touched paths; `import main` OK.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

docs/reviews/AGENTIC_DESIGN_REVIEW_2026-09-18.md

@@ -0,0 +1,559 @@
+# Agentic design review — the DS agent against the 2026 state of the art
+
+<!-- doc-status -->
+> 📎 **REFERENCE — point-in-time review, 2026-09-18, branch `pr/22` @ `10f94f32`.** An outside-in
+> critique of the `structured_flow` spine and the analytical-tools surface, benchmarked against
+> published work. Findings are cited as **D-n** below. Nothing here is a decision — §9 lists the
+> options; the team picks. Live state: [REPO_STATUS.md](../../REPO_STATUS.md) · live plan:
+> [DEV_PLAN.md](../../DEV_PLAN.md) §0.9 · the runs that informed it:
+> [AGENTIC_DRY_RUN_LOG.md](../testing/AGENTIC_DRY_RUN_LOG.md).
+
+**Reviewer:** Claude Opus 5, on Rifqi's brief ("be objective, don't assume, read the docs, search wide").
+**Read-only.** No code was changed, no tests were run, nothing was executed against a database.
+
+---
+
+## 1. Executive summary
+
+The architecture is **unusually strong on trustworthiness and unusually weak on analytical depth.**
+That is the inverse of the field's normal failure mode, and it is a defensible trade for an
+enterprise product — but three of its load-bearing assumptions are unverified, and the tool layer's
+statistics are the thinnest part of a product called an "AI data scientist."
+
+| Axis | Grade | One line |
+|---|---|---|
+| Safety / guardrails / degradation | **A** | Better than >90% of surveyed DS agents |
+| Determinism & auditability | **A−** | Records-based reports, no code execution, full provenance — undercut by one row-ordering flaw (D-2) |
+| Flow architecture (plan → run → check → compose) | **B** | Right shape; the missing feedback edge is the field's most-replicated finding |
+| Tool selection design | **B−** | Group-first is directionally right, validated by 7 ad-hoc questions |
+| Tool granularity / variety | **B** | 18 is a sensible count; the *contents* skew shallow |
+| **Statistical rigour** | **C** | The actual data-science layer is the weakest part |
+| **Evaluation** | **C−** | 10 of 14 analysis tools have zero eval coverage; no answer-quality metric exists |
+| Context engineering | **B−** | Scoping was smart; the absolute budget is still large and retries triple it |
+
+**If only three things get done:** fix D-1 and D-3 (silent wrong answers on questions the demo
+suites actually ask), score `group_hint` in `eval/intent` (the riskiest component is currently
+unmeasured), and stand up a faithfulness check over archived transcripts (it would have caught
+three of the fourteen known bugs, offline, for pennies).
+
+---
+
+## 2. Method — what this review is based on
+
+So the claims are auditable rather than taken on trust.
+
+**Code read end to end.** `main.py` → `api/v2/chat.py` → `agents/chat_handler.py` →
+`agents/orchestration.py` → `agents/planner/groups.py` → planner (`service` · `prompt` · `inputs` ·
+`validator` · `examples` · `schemas`) → slow path (`coordinator` · `task_runner` · `checkpoint` ·
+`assembler` · `prompt` · `progress`) → tool layer (`contracts` · `spec_builder` · `registry` ·
+`invoker` · `data_access` · `ml/executor` · `analytics/charts` · `agg_vocab`) → query pipeline
+(`ir/models` · `ir/validator` · `operators` · `executor/db` · `executor/tabular` ·
+`compiler/pandas` · `compiler/sql`) → outputs (`charts/extract` · `charts/store` ·
+`report/readiness` · `report/generator`). All 14 analytics tools read; forecast, importance,
+cluster and anomaly read in full.
+
+**Prompts read in full:** `intent_router.md`, `planner.md`, `assembler.md`.
+
+**Docs read:** `CLAUDE.md`, `REPO_STATUS.md`, `DEV_PLAN.md` (§0.7, §0.9 A0–A31, §1/§1.5, §5, §6),
+`API_CONTRACT_BE_PYTHON.md`, `docs/README.md`, `ANALYTICAL_TOOLS_CONVENTIONS.md`,
+`AGENTIC_DRY_RUN_LOG.md`, `SPINE_V2_PLAN.md`.
+
+**Measured, not recalled** (reproduction commands in Appendix A): per-scope planner prompt budgets,
+tool counts per group, eval dataset sizes and tool coverage, latest eval scores.
+
+**External sources:** 16, listed in §10. Peer-reviewed benchmarks and surveys are separated from
+vendor engineering posts, and the distinction is kept in the text.
+
+---
+
+## 3. Where the design is genuinely ahead
+
+**You are on the right side of the accuracy debate for enterprise analytics.** The declarative
+IR + catalog + whitelist approach is not a compromise — it is what the 2026 evidence favours for
+governed data. dbt's April 2026 benchmark found semantic-layer grounding lifted Claude Sonnet 4.6
+from 90.0% → 98.2% and GPT-5.3-Codex from 84.1% → 100%, against text-to-SQL accuracy of ~40%
+ungrounded. Raw enterprise text-to-SQL collapses: Spider 2.0 execution accuracy is **~21%** versus
+~90% on academic Spider. One sentence from that literature could have been written about the
+dry-run log: *semantic-layer failures are typically refusals; text-to-SQL failures are confident
+wrong numbers.*
+
+**The safety posture is top-decile.** The 45-agent survey of LLM-based DS agents found **over 90%
+lack explicit trust and safety mechanisms**. This repo has an input guard, an `out_of_scope` intent,
+five query-defense layers, PII suppression, never-throw seams, deterministic refusals and tenant
+scoping. That is not common.
+
+**The `quality`-block + CK7 convention is good design.** The tool owns the verdict; the checkpoint
+only surfaces it, so a new ML tool is covered with no checkpoint change. This is the
+"contract between deterministic systems and non-deterministic agents" pattern Anthropic's
+tool-writing guidance describes, arrived at independently.
+
+**Auto-charts ("sepaket") beat a separate viz-planning step.** CoDA pays ~15 LLM calls for what
+this does in zero.
+
+**The dry-run discipline is better than most teams'.** Nine runs, fourteen bugs, nine of them
+silent wrong answers — and the *pattern* was written down, not just the fixes. "881 passing unit
+tests caught none of these" is exactly right, and matches DABstep's finding that hard analysis
+tasks resist single-shot validation.
+
+---
+
+## 4. Five structural divergences from current standards
+
+### A. No replanning — the most-contradicted design choice
+
+**Current design.** INV-6: the plan is static, no mid-run LLM. The retry loop hardens only the
+*initial* plan; S1a can flag or fail but not repair. S1b is gated behind "meaningful
+repair-candidate hit-rate."
+
+**External evidence** — consistent and not close:
+
+| Source | Finding |
+|---|---|
+| Data Interpreter ablation | Adding dynamic planning: comprehensive score **0.37 → 0.85**; open-ended completion 0.46 → 0.97 (+112%) |
+| DS-STAR (planner + verifier + replanning) | DABstep hard-level **12.70% → 45.24%** with Gemini-2.5-Pro; ablations name the verifier and the loop as critical |
+| DABstep authors | *"Hard tasks require iterative data filtering and cross-referencing, which single-shot code cannot handle."* Best model: 76.39% easy → **14.55% hard** |
+| BLADE | ReAct agents beat one-turn generation on coverage of expert analysis decisions |
+
+**The fair counter-argument.** The two-LLM-call invariant is a real product constraint (cost,
+latency, traceable determinism), and gating on telemetry is the right instinct.
+
+**But the gate is mis-specified.** CK1–CK7 only see failures the checkpoint knows how to name. Nine
+of fourteen dry-run bugs were confident wrong answers that no CK fires on, so a low
+repair-candidate rate is evidence the checkpoint is narrow, not that repair wouldn't pay.
+**Re-gate on the bug ledger already collected:** how many of the fourteen would one bounded re-plan
+have caught? (Review estimate: bugs 1, 2, 9, 11, 12 — five of fourteen — plus every future instance
+of those classes.)
+
+### B. The group filter is the highest-leverage unverified component
+
+**Measured.** explore 9 tools · relationship 9 · timeseries 8 · grouping 8 · evaluate 8 ·
+two-group 11 · fail-open 18. The documented figures are accurate.
+
+**Three problems.**
+
+1. **Fail-open covers absent hints, not wrong ones — and wrong is the failure that matters.**
+   `groups.py` states that a wrong hint is unrecoverable, but the only mitigation is asking the
+   router to return null when unsure. There is no verification and no re-scope path. When the hint
+   is wrong the planner's two options are both silent: substitute a visible-but-wrong tool, or emit
+   a **false** `infeasible_reason` that reads to the user as "your data can't answer that." A false
+   refusal is indistinguishable from a true one in the logs.
+2. **`group_hint` accuracy is measured by seven ad-hoc questions.** `grep -rn "group_hint" eval/`
+   returns nothing. The intent eval (52 cases) scores intent only and sits at **52/52 = 100%** — a
+   saturated benchmark with no remaining discriminative power. Bug #4, the most dangerous of the
+   fourteen, was found in production, not by it.
+3. **The evidence says 8 tools is still above optimum, and grouping is the weaker mitigation.** In
+   the chance-corrected study, Claude selected the gold tool **93.1% at adaptive depth K≈2.2 vs
+   87.1% at fixed K=5** — degradation is measurable in single digits, far below the "15–20 tools"
+   band this design targets (that figure traces to vendor posts, not a study). RAG-MCP's
+   retrieval-based selection moved accuracy **13.62% → 43.13%** while halving prompt tokens. The
+   industry pattern is "shrink first, plan second," and retrieval shrinks harder than static groups.
+
+### C. Every turn starts from zero — there is no working memory
+
+**Verified:** `build_planner_prompt()` receives context, catalog, tools, query, constraints, errors,
+language and the domain card. **No history, no prior results.** Same for the assembler. The only
+carrier of conversational context is the router's one-line `rewritten_query`.
+
+Consequences: "now break that down by site" re-plans from scratch on a paraphrase; the agent cannot
+build on its own previous *numbers*; the assembler can repeat or contradict a previous turn with no
+mechanism to notice. The field's productive systems are stateful and notebook-centric
+(DatawiseAgent, Data Interpreter, EvoDS with explicit context management).
+
+The plumbing already exists — `report_inputs` persists every run's findings and `results_snapshot`
+per `analysis_id`. A capped "what we have established in this analysis so far" block is the cheapest
+capability upgrade available.
+
+### D. There is a catalog, not a semantic layer — the biggest accuracy lever
+
+The catalog carries names, types, samples, `distinct_count` and (since bug 14) min/max. A semantic
+layer carries **governed metric definitions, certified join paths, and one canonical name per
+business concept.** The gap is exactly where the worst bugs live:
+
+| Known bug | What a metric layer does to it |
+|---|---|
+| #10 `SELECT badactor.ma AS "backlog"` | "backlog" resolves to one definition, or to nothing |
+| #14 banding `pa` into thirds of 0–100 when it runs 68.8–95.9 | The metric carries its own domain/range |
+| #3 targeting a constant `Total_Hours` as a driver | Not a metric; never a target |
+| Open: `is_bad_actor` explaining `bad_actor_score` | Derived-from relationships are declared, so leakage is detectable |
+
+The `knowledge_domain` card (K5) is the right idea and is already threaded ahead of the catalog in
+the planner prompt, but it is fed by an admin review pipeline and is empty for most scopes.
+**Four of the fourteen known bugs are metric-layer bugs wearing a planner costume.** Investment here
+has better expected return than any new tool.
+
+### E. The agent never asks a clarifying question
+
+The planner has two outputs: a plan (assumptions recorded silently) or a refusal.
+
+**Ambig-DS** (2026) benchmarks this for DS agents specifically and finds agents *predominantly rely
+on silent assumptions rather than seeking clarification*; it recommends proactive clarification.
+**"Knowing but Not Showing"** finds models recognise ambiguity but rarely surface it. **SAGE-Agent**
+achieves **7–39% higher coverage** on ambiguous tasks while asking **1.5–2.7× fewer** questions
+using structured-uncertainty-guided clarification.
+
+The dry-run log already contains the case, in the team's own words: *"Planner will not band without
+thresholds… Defensible but unhelpful — an analyst would use terciles and say so."*
+
+**Counter-evidence worth respecting:** InteractComp found the best model reaches only 13.73%
+accuracy when interaction is available versus 71.50% given full context — clarification is hard to
+do *well*. A narrow version (offer 2–3 concrete interpretations, not open questions) is the safe
+entry point.
+
+---
+
+## 5. The tool surface: count, granularity, variety
+
+**Eighteen is a defensible count.** Granularity and coverage are where to push.
+
+| Issue | Assessment |
+|---|---|
+| **Inconsistent granularity** | `analyze_descriptive` is a multi-metric EDA sweep; `analyze_pareto` is one arithmetic operation. Anthropic's guidance favours consolidating related operations into high-leverage tools — `pareto` / `segmentation` / `aggregate` are arguably one tool with a mode |
+| **Namespacing 80% done** | `analyze_*` / `check_*` / `retrieve_*` is good practice. `render_chart` breaks it and is load-bearing for `readiness._is_analysis_tool`, which special-cases it by string — fragile |
+| **Real coverage gaps** | No duplicate detection (D-1). No distribution/normality check. No missing-data *mechanism* analysis. No cohort/retention. No period-over-period as a first-class tool (the prompt tells the planner to emit two queries and compare by hand). No "explain this number" drill-down |
+| **Intentional gaps are correct** | No AutoML, no regression-as-a-tool, no text analysis. The survey's finding that agents over-index on modeling and neglect business understanding supports this scoping |
+| **`BusinessContext` is a stub** | The survey's headline capability gap is that agents *"neglect business understanding."* The slot is wired through the planner and empty; the domain card partially fills it. Worth naming as deliberate debt |
+
+The three-part when / when-not / output descriptions with explicit confusable-pair callouts are
+better than most MCP servers ship. Keep them.
+
+---
+
+## 6. Statistical rigour — the weakest layer
+
+All code-verified.
+
+- **No sample sizes reach the user.** `analyze_correlation` returns `{method, columns, matrix,
+  pairs}` — no `n`, no p-value, no CI ([relationship.py:132](../../src/tools/analytics/relationship.py)).
+  Bug #10 was a reported Pearson of −0.029 on 42 rows. Even with the alias fixed, that should never
+  have been narratable. The assembler cannot caveat what it is not told.
+- **No multiple-comparisons control anywhere.** With no `column_ids`, `analyze_correlation`
+  correlates every numeric pair and returns `pairs` **sorted by |corr| descending** — handed to an
+  LLM instructed to lead with the strongest finding. Twenty columns is 190 tests; at α=0.05 that is
+  ~9 expected false positives, returned pre-ranked. This is a named hazard in interactive EDA and
+  the mitigations (BH-FDR, or simply reporting n and p) are cheap.
+- **Significance conflated with importance.** `analyze_ab_test` reports a p-value and no effect
+  size, and `quality.weak = not significant` — so a trivially small difference on a large sample is
+  marked *strong* evidence. The inverse of the error already guarded against.
+- **The A/B test does not test the reported quantity.** The headline uses `agg` (default `sum`); the
+  Welch test always compares **means**. `_MIN_TEST_N = 2` permits a t-test on two observations per
+  group, with no normality check and no non-parametric fallback.
+- **Forecasting never models seasonality.** `_ets()` hardcodes `seasonal=None`. For daily equipment
+  data with weekday structure, or monthly data with annual structure, that is the wrong model class
+  — and MASE against a naive-1 baseline will not reveal it. (The MASE backtest itself is good work;
+  this is about the model, not the metric.)
+- **Ranked importance ignores collinearity.** Ridge distributes weight among correlated predictors,
+  so the ranking is least stable exactly when features are related — which is always, in operational
+  data. No VIF, no correlation pruning, no warning. The open `is_bad_actor` item is this problem.
+- **Holdout scores on ~5 rows.** `_MIN_ROWS = 20`, `_TEST_SIZE = 0.25` → the r² ≥ 0.3 `quality` gate
+  can be decided on a five-row test set, single split, fixed seed. CK7 treats that number as a
+  verdict.
+
+---
+
+## 7. Defects found
+
+Ranked by expected harm. **Read-only review — none of these were reproduced by execution.**
+
+| # | Severity | Defect | Where |
+|---|---|---|---|
+| **D-1** | **High** | **`analyze_data_quality` advertises duplicate detection and computes none.** Its `use_when` names *"duplicates/unique … duplikat"* and `analyze_descriptive` routes duplicate questions to it, but the tool returns only nulls/distinct/outliers. *"Ada duplikat?"* gets a confident profile that does not answer the question, and the assembler will infer from `distinct_rate`. The dominant silent-wrong-answer class | `analytics/data_quality.py:103` |
+| **D-2** | **High (latent)** | **Statistics computed on an arbitrary, non-random, non-deterministic 10k slice.** SQL always emits `LIMIT 10001` with **no `ORDER BY`** unless the IR asks. Above 10k rows every `analyze_*` sees an undefined subset — and Postgres does not guarantee stable order, so two identical runs can return different rows and therefore different means, correlations and clusters. That contradicts the "deterministic, repeatable" family-B promise in the conventions doc. CK3's wording ("the first 10,000 rows") describes a guarantee SQL does not make. Untested: the largest registered source is 9,729 rows | `compiler/sql.py:314`, `slow_path/checkpoint.py:94` |
+| **D-3** | **High** | **`analyze_anomaly` cannot name what is anomalous.** Flagged records contain only the scored numeric columns plus a score. The `padb` suite's literal question is *"Which units look unusual?"* — unless the unit id is numeric and passed in `column_ids`, the answer structurally cannot identify a unit. Compounded by `contamination="auto"` flagging 55% of rows on that suite, with **no caveats field and no `quality` block** — the tool with the worst known behaviour is the one exempted from CK7 | `analytics/anomaly.py:151,161` |
+| **D-4** | **Med-High** | **A numeric target with ≤20 distinct values is silently treated as classification.** `classify = numeric_target.isna().all() or numeric_target.nunique() <= _MAX_CLASSES`. An integer downtime-hours column with 15 distinct values becomes a 15-class LogisticRegression, `pd.factorize` destroys the ordering, and `quality.metric` flips from r² to accuracy with no user-visible signal. Easy to hit on small operational tables | `analytics/importance.py:218` |
+| **D-5** | **Medium** | **A typo'd value-handoff alias fails silently and asymmetrically.** `_column_values` returns `[]` for an unknown column; `in` → 0 rows (CK2 catches it), **`not_in` → matches everything**, with no check anywhere. An anti-join with a mistyped alias returns the whole table as "customers who never ordered" | `slow_path/task_runner.py:210` |
+| **D-6** | **Medium** | **Pareto accepts non-additive aggregations.** `SUPPORTED_AGGS` includes `mean`/`median`/`min`/`max`, then computes `share = val / total`. "Share of the total of the averages" is meaningless, and negative values (the demo file has `Plan_PA_Percent` min −25) push cumulative share above 1 or below 0. No guard | `analytics/pareto.py:25,127` |
+| **D-7** | **Medium** | **`direction` and `slope` can disagree in `analyze_trend`.** `direction` comes from `last − first` (two endpoints); `slope` is OLS over all points. A noisy declining series with a high final point reports `direction="up"` beside a negative slope, and the assembler sees both. No reconciliation, no R² | `analytics/temporal.py:235,243` |
+| **D-8** | **Low-Med** | **The recipe table is not scoped with the registry.** A17/A25 scope the tool list and the few-shots; `planner.md`'s 18-row recipe table is static, so an `explore` turn still reads R8-forecast and R12-clustering. Creates pull toward invisible tools (validator rejects → retry → ~14k tokens) and contradicts the scoping premise | `config/prompts/planner.md` |
+| **D-9** | **Low-Med** | **Analysis-tool outputs are never trimmed in the persisted record.** `_trim_for_snapshot` only trims when `out.rows is not None`; `analyze_*` tools put their payload in `value`, so a 5,000-row merge or pareto result is persisted whole into `report_inputs` jsonb. A30 fixed the *prompt* side, not the *storage* side | `slow_path/assembler.py:166` |
+| **D-10** | **Low** | **Explicit `k` is silently overridden in `analyze_cluster`.** If every candidate fit is degenerate, the fallback uses `_K_MIN`, ignoring a user-specified `k` | `analytics/cluster.py:120` |
+| **D-11** | **Low (doc)** | **The "3 LLM calls" figure is wrong.** SPINE_V2_PLAN §0 says 3 (router + planner + assembler); the input guard is a fourth, and the dry-run log says "exactly 4 LLM calls." Cost models built on §0 understate by 33% | `docs/plans/SPINE_V2_PLAN.md` |
+| **D-12** | **Low** | **Example P teaches a degenerate importance plan.** It retrieves `quantity` + `revenue`, then ranks importance with a single candidate feature — a "ranking" of one. As an exemplar it teaches the wrong retrieve width for the tool | `planner/examples.py:1149` |
+
+---
+
+## 8. Measurements
+
+### 8.1 Planner prompt budget (measured 2026-09-18)
+
+| Scope | Tools | Registry | Examples | System | Catalog (demo) | **Per call** |
+|---|---:|---:|---:|---:|---:|---:|
+| `timeseries` | 8 | 1,847 | 5,618 | ~5,970 | ~1,366 | **~14.8k** |
+| `explore` | 9 | 2,355 | 6,701 | ~5,970 | ~1,366 | **~16.4k** |
+| `grouping` / `evaluate` | 8 | ~1,860 | ~4,650 | ~5,970 | ~1,366 | **~13.8k** |
+| two-group hint | 11 | 2,618 | 6,934 | ~5,970 | ~1,366 | **~16.9k** |
+| fail-open | 18 | 4,602 | 12,180 | ~5,970 | ~1,366 | **~24.1k** |
+
+*(tokens ≈ chars/4; system prompt = `planner.md`, 23,875 chars.)*
+
+Two observations. **Retries multiply it** — three attempts on a fail-open turn is ~72k tokens of
+prompt for one question. Chroma's context-rot study tested 18 frontier models and found **every one
+degrades as input length grows**, well below the window limit, with lost-in-the-middle effects
+costing 30%+ on some tasks; the retry prompt correctly appends errors at the end, but the catalog
+and 23 exemplars sit in the middle. **Exemplars are ~45% of the prompt** — and the A18 expansion
+from 13 to 23 measured 28/31 versus 29/31, i.e. no measurable eval gain, on an eval that cannot see
+most of the new tools anyway (§8.2). That is an untested ~5k tokens on every call.
+
+### 8.2 Evaluation coverage
+
+| Gap | Evidence |
+|---|---|
+| **10 of 14 active analysis tools have zero planner-eval coverage** | Asserted in `planner_dataset.json`: `analyze_descriptive`, `analyze_correlation`, `analyze_trend`, `render_chart`. Missing: ab_test, aggregate, anomaly, cluster, data_quality, forecast, importance, merge, pareto, segmentation. A15 activated 8 tools and added zero cases |
+| **`group_hint` is unscored** | `grep -rn "group_hint" eval/` → no matches |
+| **The intent eval is saturated** | 52/52 = 100% (`eval_result_2026-09-17_113055.json`). No discriminative power left; bug #4 was found in production |
+| **No answer-quality metric exists** | `chat_sim` writes transcripts for a human to read. No faithfulness/groundedness score, no numeric-claim verification, no regression signal on the assembler. Bug #13 was caught by one person reading one transcript |
+| **Tool correctness is not recorded in a shared artifact** | Verified by planted-signal spot checks in DEV_PLAN prose. (Tests-stay-local is a settled decision and is not being reopened — the point is that no shared artifact records tool correctness) |
+
+The standard practice missing here is cheap: an **LLM-as-judge faithfulness check** that decomposes
+`chat_answer` into atomic claims and verifies each against `results_snapshot` — which is already
+persisted verbatim by design (INV-4). RAGAS-style faithfulness scoring is the established form.
+**Bugs #10, #13 and #14 would all have been caught this way**, offline, at roughly one cheap model
+call per archived transcript.
+
+---
+
+## 9. Next steps — options to choose from
+
+Costed work packages. Pick per-option; nothing here is sequenced by dependency unless stated.
+**Effort** is one engineer's working time. **Token cost** is incremental paid-model spend.
+
+### Track 1 — Correctness fixes (small, self-contained)
+
+#### Option 1A — Close the two demo-path silent failures *(D-1, D-3)*
+- **Why now:** both are on questions the `pa` and `padb` suites actually ask, and both return a
+  confident answer that is not an answer.
+- **Steps:**
+  1. `analytics/data_quality.py`: add `duplicate_row_count` + `duplicate_rate` over the full frame,
+     and a `duplicate_key_count` when `column_ids` names a subset; surface both in `issues`.
+  2. Add `n_rows` and the duplicate figures to the returned dict so the assembler can state them.
+  3. `analytics/anomaly.py`: add an optional `label_columns: list[str] | None` input; include those
+     columns verbatim in each flagged record so a unit can be named.
+  4. Add a `caveats` list to the anomaly output stating that the flag rate is a parameter
+     (`contamination`), not a finding, whenever `anomaly_rate > 0.2`.
+  5. Update both DESCRIPTIONs; keep `dont_use_when` pointing at the confusable neighbours.
+- **Files:** `src/tools/analytics/data_quality.py`, `src/tools/analytics/anomaly.py`
+  (+ one planner exemplar if `label_columns` should be demonstrated).
+- **Effort:** 3–4 h · **Token cost:** 0 (local tests only) · **Risk:** low — additive fields.
+- **Verify:** local tests on a planted frame with known duplicates; `padb --turns 7 --no-report`
+  (~25k tokens) to confirm units are named.
+- **Owner note:** both files are Sofhia's under the §0.9 A-row split — needs a handoff or her lane.
+
+#### Option 1B — Make the statistics reportable *(§6)*
+- **Why now:** the assembler cannot caveat what it is not given, and bug #10's −0.029 on n=42 is the
+  archetype.
+- **Steps:**
+  1. `analytics/relationship.py`: return `n` per pair (pairwise complete observations) and a
+     two-sided p-value (`scipy.stats.pearsonr` / `spearmanr`).
+  2. Add a `quality` block: `weak = True` when `n < 30` **or** `p >= 0.05`, with a note naming which.
+     CK7 then surfaces it with no checkpoint change.
+  3. Apply a Benjamini–Hochberg correction across `pairs` when more than ~10 pairs are returned, and
+     return `q_value` alongside `corr`.
+  4. `analytics/ab_test.py`: add Cohen's d; make `quality.weak` true when *either* p ≥ α *or* |d|
+     is below a small-effect floor; raise `_MIN_TEST_N` to something defensible (≥8) or state the
+     limitation in the note.
+  5. Either compute the Welch test on the same aggregate the headline reports, or rename the
+     reported fields so the mismatch is explicit.
+- **Files:** `src/tools/analytics/relationship.py`, `src/tools/analytics/ab_test.py`.
+- **Effort:** 1 day · **Token cost:** 0 · **Risk:** medium — changes what CK7 flags, so expect more
+  "weak evidence" language in answers. That is the intent, but it will change demo copy.
+- **Verify:** planted-signal tests (known r, known n); one `padb --turns 4,8` run (~50k) to see the
+  narrative change.
+
+#### Option 1C — The remaining defects *(D-4 … D-12)*
+- **Steps:** take them in listed order; each is independent. D-4 and D-6 are the two that can still
+  produce a wrong number; D-8 and D-11 are documentation-shaped; D-9 is a storage-hygiene fix.
+- **Effort:** ~1 day for D-4/D-5/D-6/D-7; ~2 h for D-8/D-10/D-11/D-12; ~2 h for D-9.
+- **Token cost:** 0 · **Risk:** low each.
+
+#### Option 1D — Fix the ordering flaw before a real database arrives *(D-2)*
+- **Why now:** cheap insurance; expensive to discover in front of a customer.
+- **Steps:**
+  1. `compiler/sql.py::_build_limit`: when the compiler adds an implicit limit and the IR carries no
+     `order_by`, inject a deterministic `ORDER BY` over the select's first stable column.
+  2. Mirror in `compiler/pandas.py` (sort before `head`).
+  3. Reword CK3: not "the first 10,000 rows" but "a 10,000-row subset of N".
+  4. Add a `planner.md` rule: when a table's catalog `row_count` exceeds `row_budget`, either
+     aggregate in the IR or state the sampling in `assumptions`.
+- **Effort:** half a day · **Token cost:** 0, unless the planner.md change is made, which requires
+  `eval.planner` per §7B (~777k tokens/full run) · **Risk:** medium — touches the compiler, and the
+  prompt half needs an eval.
+- **Note:** steps 1–3 are code-only and need no eval. Step 4 is the one that costs tokens. They can
+  ship separately.
+
+### Track 2 — Measurement (highest leverage per hour)
+
+#### Option 2A — Score `group_hint` *(§4B)*
+- **Why now:** the component that decides whether the right tool is visible is validated by seven
+  ad-hoc questions.
+- **Steps:**
+  1. Add `expected_group_hint: list[str] | null` to the 13 `structured_flow` cases in
+     `intent_dataset.json`; null for the other 39.
+  2. Extend `eval/intent/run_eval.py` to score it as a set match, reported separately from intent
+     accuracy so the two do not mask each other.
+  3. Add 8–10 new cases specifically targeting the six intra-group confusions
+     (trend↔forecast, correlation↔importance, segmentation↔cluster) and two-group questions.
+  4. Run once, commit the timestamped result as the baseline.
+- **Files:** `eval/intent/intent_dataset.json`, `eval/intent/run_eval.py`.
+- **Effort:** half a day · **Token cost:** ~185k for one full 52-case run (measured: the 2026-09-17
+  full run was 183,540) · **Risk:** none — read-only eval work.
+- **Payoff:** converts the riskiest component from anecdote to metric, and de-saturates a benchmark
+  that is currently at 100%.
+
+#### Option 2B — Faithfulness check over archived transcripts
+- **Why now:** it is the only proposed change that would have caught bugs #10, #13 and #14, and it
+  runs on data already on disk.
+- **Steps:**
+  1. New `eval/faithfulness/run_eval.py`: read
+     `eval/chat_sim/results/transcript_*.json`, pair each turn's `chat_answer` with that turn's
+     `results_snapshot`.
+  2. Decompose the answer into atomic claims (one cheap model call per turn).
+  3. For each claim, ask the judge: supported / contradicted / not-in-evidence, against the snapshot
+     only. Score = supported / total.
+  4. Add a deterministic pre-check that every number appearing in the answer appears in the snapshot
+     (regex over numerals, tolerant of rounding) — this alone catches the fabricated-figure class
+     with no model call.
+  5. Backfill across all nine archived dry-runs to establish a baseline.
+- **Files:** new `eval/faithfulness/` (+ README per house convention).
+- **Effort:** 1–2 days · **Token cost:** ~5–15k per transcript judged; nine backfilled runs ≈ 100k
+  total · **Risk:** low; LLM-judge bias is real, so pair it with the deterministic numeral check and
+  spot-read disagreements.
+
+#### Option 2C — Eval cases for the ten uncovered tools
+- **Steps:**
+  1. For each of the ten, add one feasible case asserting `has_tool`.
+  2. For each confusable pair, add one discriminator case asserting `has_tool` on one and `no_tool`
+     on the other.
+  3. Extend the fixture catalog if a tool needs a shape it lacks (a two-value dimension for
+     `ab_test`, a category column for `pareto`).
+  4. Re-baseline and commit.
+- **Files:** `eval/planner/planner_dataset.json`, `eval/planner/catalog_fixture.py`.
+- **Effort:** 1 day · **Token cost:** ~777k per full run (measured at A18) — **ask before the
+  second run** per CLAUDE.md §6.10 · **Risk:** none, but it will likely reveal failures, which is
+  the point.
+
+### Track 3 — Capability (larger, sequence after Track 2)
+
+#### Option 3A — Hint-miss recovery *(the cheap half of §4B)*
+- **Steps:**
+  1. In `coordinator.run`, when the planner returns `infeasible_reason` **and** the registry was
+     scoped, re-plan once against the full registry before returning.
+  2. Log both outcomes with a distinguishing field so the rate of "scoped-infeasible but
+     unscoped-feasible" becomes measurable — that number is also the direct measurement of hint
+     error rate in production.
+  3. Cap at one retry; leave INV-6 untouched (this is pre-execution, like the existing retry loop).
+- **Effort:** half a day · **Token cost:** one extra planner call only on scoped refusals
+  (~15–24k when it fires) · **Risk:** low — it cannot make a feasible turn worse.
+- **Note:** this is the single highest value-per-hour item in the review. It converts the worst
+  silent failure into a recoverable one *and* instruments the thing §4B says is unmeasured.
+
+#### Option 3B — Session memory
+- **Steps:**
+  1. Read prior `report_inputs` rows for the `analysis_id` at the composition root.
+  2. Render a capped block (≤400 tokens): per prior turn, `goal_restated` + top findings + tables
+     used.
+  3. Thread it into the planner prompt (after the domain card, before the catalog) and the assembler
+     prompt (so it stops contradicting itself).
+  4. Never-throw, exactly like the domain card.
+- **Effort:** 1–2 days · **Token cost:** +~400 tokens/call, and `eval.planner` for the prompt change
+  · **Risk:** medium — it is a new prompt section; A19's collision lesson applies (prior findings
+  could be replayed as if freshly computed). Mitigate by labelling the block explicitly as *prior
+  context, not evidence for this answer*.
+
+#### Option 3C — Re-gate and scope S1b *(§4A)*
+- **Steps:**
+  1. Offline: walk the fourteen dry-run bugs and mark which a single bounded re-plan would have
+     caught. No tokens; the transcripts and the log already exist.
+  2. If the count justifies it, scope S1b to the narrowest useful trigger — CK2 (empty retrieve) and
+     tool-level `error` outputs only, one pass, failed subgraph only.
+  3. Write the INV-6 relaxation up for team sign-off with that evidence attached.
+- **Effort:** half a day for step 1; 3–5 days for the build if approved · **Risk:** high — this is
+  the invariant relaxation, and it needs the sign-off named in DEV_PLAN §6.
+
+#### Option 3D — Semantic / metric layer *(§4D)*
+- **Steps:**
+  1. Define the minimum viable metric record: canonical name, aliases, formula, unit, valid range,
+     the column(s) it maps to, and `derived_from` for leakage detection.
+  2. Source it from the existing `knowledge_domain` pipeline rather than a new store.
+  3. Extend the planner's domain card to render metric → column bindings, so an unmapped metric is
+     an explicit data gap rather than an alias opportunity.
+  4. Add a validator check: an alias in a `retrieve_data` select that matches a *known metric name*
+     must map to that metric's column. (This generalises check 8f from "collides with a real column"
+     to "collides with a real concept.")
+  5. Feed `valid range` into the segmentation/banding path so bug 14's class cannot recur.
+- **Effort:** 1–2 weeks, cross-workstream · **Risk:** medium, high value · **Dependency:** the
+  knowledge pipeline's review flow must actually be populated for a customer scope.
+
+#### Option 3E — Statistical depth (seasonality, collinearity, honest holdouts)
+- **Steps:**
+  1. `forecast`: try additive and seasonal ETS, pick by holdout MASE, report which was chosen;
+     compute MASE against seasonal-naive when a seasonal model wins.
+  2. `importance`: report pairwise correlation among the selected features, drop or merge
+     near-duplicates (|r| > 0.95), and flag any feature whose name or correlation suggests it is
+     derived from the target.
+  3. `importance`: replace the single split with repeated K-fold when rows < 200 and report the mean
+     ± spread, so the `quality` gate stops being decided on ~5 rows.
+- **Effort:** 2–3 days · **Token cost:** 0 for the tool work; a `padb --turns 3,6` run (~50k) to see
+  the narrative · **Risk:** medium — changes reported numbers on existing demo questions.
+
+### Suggested sequence, if you want one
+
+**Week 1:** 1A → 2A → 3A. All small, all independent, and together they close the two demo-path
+silent failures, measure the riskiest component, and make its worst failure recoverable.
+**Week 2:** 2B → 1B. Measurement before the statistics change, so the change is verifiable.
+**Then:** 1C and 1D as background; 3B; decide 3C and 3D with the team.
+
+---
+
+## 10. Sources
+
+Peer-reviewed / benchmark:
+[How Many Tools Should an LLM Agent See? A Chance-Corrected Answer](https://arxiv.org/html/2605.24660v1) ·
+[DS-STAR](https://arxiv.org/pdf/2509.21825) ·
+[DABstep](https://arxiv.org/html/2506.23719v1) ·
+[Data Interpreter](https://arxiv.org/html/2402.18679v3) ·
+[BLADE](https://arxiv.org/html/2408.09667) ·
+[LLM-Based Data Science Agents: A Survey](https://arxiv.org/abs/2510.04023v1) ·
+[Ambig-DS](https://arxiv.org/pdf/2605.09698) ·
+[Knowing but Not Showing](https://arxiv.org/html/2605.25284v1) ·
+[Structured Uncertainty guided Clarification (SAGE-Agent)](https://arxiv.org/html/2511.08798v2) ·
+[Visual Belief Elicitation Reduces the Incidence of False Discovery](https://arxiv.org/pdf/2301.12512) ·
+[Spider 2.0](https://spider2-sql.github.io/)
+
+Engineering / vendor (weighted accordingly):
+[Writing effective tools for AI agents — Anthropic](https://www.anthropic.com/engineering/writing-tools-for-agents) ·
+[Effective context engineering for AI agents — Anthropic](https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents) ·
+[Context Rot — Chroma](https://www.trychroma.com/research/context-rot) ·
+[Semantic Layer vs. Text-to-SQL: 2026 Benchmark Update — dbt](https://docs.getdbt.com/blog/semantic-layer-vs-text-to-sql-2026) ·
+[InteractComp (ICML 2026)](https://atoms.dev/blog/icml-2026-interactcomp-search-agents-ambiguous-queries) ·
+[The Over-Tooled Agent Problem](https://tianpan.co/blog/2026-04-19-over-tooled-agent-problem)
+
+---
+
+## Appendix A — reproducing the measurements
+
+```bash
+# Per-scope planner prompt budget and tool counts (§8.1)
+PYTHONPATH=. ./.venv/Scripts/python.exe -c "
+from src.agents.planner.registry import default_registry
+from src.agents.planner.groups import scope, HINTABLE_GROUPS, group_of
+from src.agents.planner.prompt import render_registry
+from src.agents.planner.examples import render_examples
+reg = default_registry()
+for g in list(HINTABLE_GROUPS) + [None]:
+    s = scope(reg, [g] if g else None)
+    r, e = render_registry(s), render_examples(s.names())
+    print(f'{str(g):14s} tools={len(s.tools):2d} reg~tok={len(r)//4:5d} ex~tok={len(e)//4:5d}')
+"
+
+# Eval tool coverage (§8.2)
+PYTHONPATH=. ./.venv/Scripts/python.exe -c "
+import json
+from src.tools.registry import ACTIVE_ANALYTICS_TOOLS
+d = json.load(open('eval/planner/planner_dataset.json'))
+covered = {e['has_tool'] for c in d['cases'] if 'has_tool' in (e := c['expect'])}
+covered |= {t for c in d['cases'] for t in (c['expect'].get('any_tool') or [])}
+print('no eval case:', sorted({t.name for t in ACTIVE_ANALYTICS_TOOLS} - covered))
+"
+
+# group_hint is unscored
+grep -rn "group_hint" eval/
+```

docs/testing/agentic-flow.excalidraw

@@ -0,0 +1,2980 @@
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+ "version": 2,
+ "source": "https://excalidraw.com",
+ "appState": {
+  "viewBackgroundColor": "#ffffff",
+  "gridSize": null
+ },
+ "files": {},
+ "elements": [
+  {
+   "type": "text",
+   "id": "title",
+   "x": 60,
+   "y": 40,
+   "width": 820,
+   "height": 45,
+   "text": "How a question reaches a tool",
+   "originalText": "How a question reaches a tool",
+   "fontSize": 36,
+   "fontFamily": 5,
+   "textAlign": "left",
+   "verticalAlign": "top",
+   "strokeColor": "#1e40af",
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+   "strokeStyle": "solid",
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+   "angle": 0,
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+   "id": "subtitle",
+   "x": 60,
+   "y": 96,
+   "width": 900,
+   "height": 30,
+   "text": "the router hints a group — the planner only ever sees that slice",
+   "originalText": "the router hints a group — the planner only ever sees that slice",
+   "fontSize": 20,
+   "fontFamily": 5,
+   "textAlign": "left",
+   "verticalAlign": "top",
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+   "type": "ellipse",
+   "id": "input_box",
+   "x": 60,
+   "y": 300,
+   "width": 210,
+   "height": 110,
+   "strokeColor": "#c2410c",
+   "backgroundColor": "#fed7aa",
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+  {
+   "type": "text",
+   "id": "input_txt",
+   "x": 75,
+   "y": 340,
+   "width": 180,
+   "height": 30,
+   "text": "User message",
+   "originalText": "User message",
+   "fontSize": 20,
+   "fontFamily": 5,
+   "textAlign": "center",
+   "verticalAlign": "middle",
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+   "lineHeight": 1.25
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+  {
+   "type": "text",
+   "id": "input_eg",
+   "x": 30,
+   "y": 428,
+   "width": 270,
+   "height": 60,
+   "text": "\"Tren PA harian bulan\nApril gimana?\"",
+   "originalText": "\"Tren PA harian bulan\nApril gimana?\"",
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+   "fontFamily": 5,
+   "textAlign": "center",
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+   },
+   "boundElements": [
+    {
+     "type": "text",
+     "id": "router_t"
+    },
+    {
+     "type": "arrow",
+     "id": "a2"
+    },
+    {
+     "type": "arrow",
+     "id": "a3"
+    },
+    {
+     "type": "arrow",
+     "id": "a_hint"
+    }
+   ],
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+   "locked": false
+  },
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+   "id": "router_t",
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+   "y": 332,
+   "width": 190,
+   "height": 45,
+   "text": "Intent router\nLLM",
+   "originalText": "Intent router\nLLM",
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+   "fontFamily": 5,
+   "textAlign": "center",
+   "verticalAlign": "middle",
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+   "isDeleted": false,
+   "groupIds": [],
+   "frameId": null,
+   "boundElements": null,
+   "updated": 1,
+   "link": null,
+   "locked": false,
+   "containerId": null,
+   "lineHeight": 1.3
+  },
+  {
+   "type": "text",
+   "id": "count_note",
+   "x": 2560,
+   "y": 830,
+   "width": 480,
+   "height": 100,
+   "text": "18 visible, 8 this turn.\nAll tools carry the analyze_ prefix\nexcept the four data-access ones.",
+   "originalText": "18 visible, 8 this turn.\nAll tools carry the analyze_ prefix\nexcept the four data-access ones.",
+   "fontSize": 16,
+   "fontFamily": 5,
+   "textAlign": "left",
+   "verticalAlign": "top",
+   "strokeColor": "#1e40af",
+   "backgroundColor": "transparent",
+   "fillStyle": "solid",
+   "strokeWidth": 1,
+   "strokeStyle": "solid",
+   "roughness": 1,
+   "opacity": 100,
+   "angle": 0,
+   "seed": 400070,
+   "version": 1,
+   "versionNonce": 400071,
+   "isDeleted": false,
+   "groupIds": [],
+   "frameId": null,
+   "boundElements": null,
+   "updated": 1,
+   "link": null,
+   "locked": false,
+   "containerId": null,
+   "lineHeight": 1.3
+  },
+  {
+   "type": "text",
+   "id": "why",
+   "x": 60,
+   "y": 790,
+   "width": 700,
+   "height": 130,
+   "text": "Why filter at all?\n\n18 tools that resemble one another — trend vs forecast,\ncorrelation vs importance, segmentation vs cluster — sit inside\nthe band where selection accuracy starts to slip. Narrowing the\nmenu is cheaper and safer than turning tools off.",
+   "originalText": "Why filter at all?\n\n18 tools that resemble one another — trend vs forecast,\ncorrelation vs importance, segmentation vs cluster — sit inside\nthe band where selection accuracy starts to slip. Narrowing the\nmenu is cheaper and safer than turning tools off.",
+   "fontSize": 16,
+   "fontFamily": 5,
+   "textAlign": "left",
+   "verticalAlign": "top",
+   "strokeColor": "#64748b",
+   "backgroundColor": "transparent",
+   "fillStyle": "solid",
+   "strokeWidth": 1,
+   "strokeStyle": "solid",
+   "roughness": 1,
+   "opacity": 100,
+   "angle": 0,
+   "seed": 400080,
+   "version": 1,
+   "versionNonce": 400081,
+   "isDeleted": false,
+   "groupIds": [],
+   "frameId": null,
+   "boundElements": null,
+   "updated": 1,
+   "link": null,
+   "locked": false,
+   "containerId": null,
+   "lineHeight": 1.4
+  }
+ ]
+}

docs/testing/agentic-flow.preview.html

@@ -0,0 +1 @@
+<!doctype html><body style='margin:0'><svg xmlns="http://www.w3.org/2000/svg" width="3130" height="1010" viewBox="0 0 3130 1010"><defs><marker id="ah" viewBox="0 0 10 10" refX="9" refY="5" markerWidth="6" markerHeight="6" orient="auto-start-reverse"><path d="M 0 0 L 10 5 L 0 10 z" fill="context-stroke"/></marker></defs><rect width="3130" height="1010" fill="#ffffff"/><text x="90" y="96.9" font-size="36" font-family="Consolas, Menlo, monospace" fill="#1e40af" text-anchor="start">How a question reaches a tool</text><text x="90" y="136.5" font-size="20" font-family="Consolas, Menlo, monospace" fill="#64748b" text-anchor="start">the router hints a group — the planner only ever sees that slice</text><ellipse cx="195.0" cy="375.0" rx="105.0" ry="55.0" fill="#fed7aa" stroke="#c2410c" stroke-width="2"/><text x="195.0" y="380.5" font-size="20" font-family="Consolas, Menlo, monospace" fill="#374151" text-anchor="middle">User message</text><text x="195.0" y="466.45" font-size="18" font-family="Consolas, Menlo, monospace" fill="#3b82f6" text-anchor="middle">&quot;Tren PA harian bulan</text><text x="195.0" y="488.95" font-size="18" font-family="Consolas, Menlo, monospace" fill="#3b82f6" text-anchor="middle">April gimana?&quot;</text><rect x="360" y="340" width="160" height="70" rx="8" fill="#fee2e2" stroke="#dc2626" stroke-width="2"/><text x="440.0" y="380.425" font-size="17" font-family="Consolas, Menlo, monospace" fill="#374151" text-anchor="middle">Input guard</text><rect x="580" y="320" width="220" height="110" rx="8" fill="#ddd6fe" stroke="#6d28d9" stroke-width="2"/><text x="690.0" y="371.475" font-size="19" font-family="Consolas, Menlo, monospace" fill="#374151" text-anchor="middle">Intent router</text><text x="690.0" y="395.225" font-size="19" font-family="Consolas, Menlo, monospace" fill="#374151" text-anchor="middle">LLM</text><rect x="485" y="525" width="410" height="125" rx="8" fill="#1e293b" stroke="#6d28d9" stroke-width="2"/><text x="505" y="561.4" font-size="16" font-family="Consolas, Menlo, monospace" fill="#22c55e" text-anchor="start">intent: &quot;structured_flow&quot;</text><text x="505" y="581.4" font-size="16" font-family="Consolas, Menlo, monospace" fill="#22c55e" text-anchor="start">confidence: 0.96</text><text x="505" y="601.4" font-size="16" font-family="Consolas, Menlo, monospace" fill="#22c55e" text-anchor="start">group_hint: [&quot;timeseries&quot;]</text><text x="695.0" y="683.375" font-size="15" font-family="Consolas, Menlo, monospace" fill="#64748b" text-anchor="middle">the router is CATALOG-BLIND — it judges what the</text><text x="695.0" y="702.125" font-size="15" font-family="Consolas, Menlo, monospace" fill="#64748b" text-anchor="middle">question is shaped like, never what the data can support</text><rect x="930" y="325" width="250" height="100" rx="8" fill="#3b82f6" stroke="#1e3a5f" stroke-width="3"/><text x="1055.0" y="368.45" font-size="18" font-family="Consolas, Menlo, monospace" fill="#ffffff" text-anchor="middle">structured_flow</text><text x="1055.0" y="390.95" font-size="18" font-family="Consolas, Menlo, monospace" fill="#ffffff" text-anchor="middle">the slow path</text><text x="1060.0" y="210.375" font-size="15" font-family="Consolas, Menlo, monospace" fill="#64748b" text-anchor="middle">1 of 6 intents. The other five</text><text x="1060.0" y="229.125" font-size="15" font-family="Consolas, Menlo, monospace" fill="#64748b" text-anchor="middle">(chat · help · check ·</text><text x="1060.0" y="247.875" font-size="15" font-family="Consolas, Menlo, monospace" fill="#64748b" text-anchor="middle">unstructured_flow · out_of_scope)</text><text x="1060.0" y="266.625" font-size="15" font-family="Consolas, Menlo, monospace" fill="#64748b" text-anchor="middle">answer without a plan.</text><polyline points="308,375 352,375" fill="none" stroke="#c2410c" stroke-width="2" stroke-dasharray="8 6" marker-end="url(#ah)"/><polyline points="528,375 572,375" fill="none" stroke="#dc2626" stroke-width="2" stroke-dasharray="8 6" marker-end="url(#ah)"/><polyline points="808,375 922,375" fill="none" stroke="#6d28d9" stroke-width="3" marker-end="url(#ah)"/><polyline points="685,438 690,517" fill="none" stroke="#6d28d9" stroke-width="2" stroke-dasharray="8 6" marker-end="url(#ah)"/><rect x="1260" y="325" width="210" height="100" rx="8" fill="#fef3c7" stroke="#b45309" stroke-width="2"/><text x="1365.0" y="368.45" font-size="18" font-family="Consolas, Menlo, monospace" fill="#374151" text-anchor="middle">scope filter</text><text x="1365.0" y="390.95" font-size="18" font-family="Consolas, Menlo, monospace" fill="#374151" text-anchor="middle">0 LLM</text><text x="1365.0" y="215.375" font-size="15" font-family="Consolas, Menlo, monospace" fill="#b45309" text-anchor="middle">pure code, no model call.</text><text x="1365.0" y="234.125" font-size="15" font-family="Consolas, Menlo, monospace" fill="#b45309" text-anchor="middle">Unknown or empty hint →</text><text x="1365.0" y="252.875" font-size="15" font-family="Consolas, Menlo, monospace" fill="#b45309" text-anchor="middle">fails OPEN to all 18, because</text><text x="1365.0" y="271.625" font-size="15" font-family="Consolas, Menlo, monospace" fill="#b45309" text-anchor="middle">a hidden tool can&#x27;t be asked for.</text><rect x="1550" 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stroke="#6d28d9" stroke-width="2" stroke-dasharray="8 6" marker-end="url(#ah)"/><text x="1060.0" y="605.375" font-size="15" font-family="Consolas, Menlo, monospace" fill="#6d28d9" text-anchor="middle">the hint drives the filter</text><polyline points="1430,433 1490,463 1580,493" fill="none" stroke="#b45309" stroke-width="3" marker-end="url(#ah)"/><text x="1880" y="152.55" font-size="22" font-family="Consolas, Menlo, monospace" fill="#1e40af" text-anchor="start">the 18 tools, by group</text><text x="2070" y="209.425" font-size="17" font-family="Consolas, Menlo, monospace" fill="#1e3a5f" text-anchor="end">data_access</text><text x="2070" y="230.675" font-size="17" font-family="Consolas, Menlo, monospace" fill="#1e3a5f" text-anchor="end">ALWAYS ON</text><rect x="2140" y="190" width="195" height="48" rx="8" fill="#60a5fa" stroke="#1e3a5f" stroke-width="2"/><text x="2237.5" y="220.375" font-size="15" font-family="Consolas, Menlo, monospace" fill="#374151" text-anchor="middle">retrieve_data</text><rect x="2355" y="190" width="195" height="48" rx="8" fill="#60a5fa" stroke="#1e3a5f" stroke-width="2"/><text x="2452.5" y="220.375" font-size="15" font-family="Consolas, Menlo, monospace" fill="#374151" text-anchor="middle">check_data</text><rect x="2570" y="190" width="205" height="48" rx="8" fill="#60a5fa" stroke="#1e3a5f" stroke-width="2"/><text x="2672.5" y="220.375" font-size="15" font-family="Consolas, Menlo, monospace" fill="#374151" text-anchor="middle">check_knowledge</text><rect x="2795" y="190" width="215" height="48" rx="8" fill="#60a5fa" stroke="#1e3a5f" stroke-width="2"/><text x="2902.5" y="220.375" font-size="15" font-family="Consolas, Menlo, monospace" fill="#374151" text-anchor="middle">retrieve_knowledge</text><text x="2070" y="329.425" font-size="17" font-family="Consolas, Menlo, monospace" fill="#93c5fd" text-anchor="end">explore</text><rect x="2140" y="300" width="195" height="48" rx="8" fill="#ffffff" stroke="#93c5fd" 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y="440.375" font-size="15" font-family="Consolas, Menlo, monospace" fill="#93c5fd" text-anchor="middle">correlation</text><rect x="2355" y="410" width="195" height="48" rx="8" fill="#ffffff" stroke="#93c5fd" stroke-width="1" stroke-dasharray="8 6"/><text x="2452.5" y="440.375" font-size="15" font-family="Consolas, Menlo, monospace" fill="#93c5fd" text-anchor="middle">importance</text><rect x="2570" y="410" width="205" height="48" rx="8" fill="#ffffff" stroke="#93c5fd" stroke-width="1" stroke-dasharray="8 6"/><text x="2672.5" y="440.375" font-size="15" font-family="Consolas, Menlo, monospace" fill="#93c5fd" text-anchor="middle">pareto</text><text x="2070" y="539.475" font-size="19" font-family="Consolas, Menlo, monospace" fill="#1e40af" text-anchor="end">timeseries</text><text x="2070" y="563.225" font-size="19" font-family="Consolas, Menlo, monospace" fill="#1e40af" text-anchor="end">HINTED</text><rect x="2140" y="518" width="195" height="52" rx="8" fill="#3b82f6" stroke="#1e3a5f" stroke-width="3"/><text x="2237.5" y="551.425" font-size="17" font-family="Consolas, Menlo, monospace" fill="#ffffff" text-anchor="middle">trend</text><rect x="2355" y="518" width="195" height="52" rx="8" fill="#3b82f6" stroke="#1e3a5f" stroke-width="3"/><text x="2452.5" y="551.425" font-size="17" font-family="Consolas, Menlo, monospace" fill="#ffffff" text-anchor="middle">forecast</text><text x="2580" y="540.375" font-size="15" font-family="Consolas, Menlo, monospace" fill="#1e40af" text-anchor="start">← the question was about movement over time,</text><text x="2580" y="559.125" font-size="15" font-family="Consolas, Menlo, monospace" fill="#1e40af" text-anchor="start">so only these two were offered</text><text x="2070" y="659.425" font-size="17" font-family="Consolas, Menlo, monospace" fill="#93c5fd" text-anchor="end">grouping</text><rect x="2140" y="630" width="195" height="48" rx="8" fill="#ffffff" stroke="#93c5fd" stroke-width="1" stroke-dasharray="8 6"/><text x="2237.5" y="660.375" font-size="15" font-family="Consolas, Menlo, monospace" fill="#93c5fd" text-anchor="middle">segmentation</text><rect x="2355" y="630" width="195" height="48" rx="8" fill="#ffffff" stroke="#93c5fd" stroke-width="1" stroke-dasharray="8 6"/><text x="2452.5" y="660.375" font-size="15" font-family="Consolas, Menlo, monospace" fill="#93c5fd" text-anchor="middle">cluster</text><text x="2070" y="769.425" font-size="17" font-family="Consolas, Menlo, monospace" fill="#93c5fd" text-anchor="end">evaluate</text><rect x="2140" y="740" width="195" height="48" rx="8" fill="#ffffff" stroke="#93c5fd" stroke-width="1" stroke-dasharray="8 6"/><text x="2237.5" y="770.375" font-size="15" font-family="Consolas, Menlo, monospace" fill="#93c5fd" text-anchor="middle">ab_test</text><rect x="2355" y="740" width="195" height="48" rx="8" fill="#ffffff" stroke="#93c5fd" stroke-width="1" stroke-dasharray="8 6"/><text x="2452.5" y="770.375" font-size="15" font-family="Consolas, Menlo, monospace" fill="#93c5fd" text-anchor="middle">anomaly</text><text x="2070" y="869.425" font-size="17" font-family="Consolas, Menlo, monospace" fill="#1e3a5f" text-anchor="end">utility</text><text x="2070" y="890.675" font-size="17" font-family="Consolas, Menlo, monospace" fill="#1e3a5f" text-anchor="end">ALWAYS ON</text><rect x="2140" y="850" width="195" height="48" rx="8" fill="#60a5fa" stroke="#1e3a5f" stroke-width="2"/><text x="2237.5" y="880.375" font-size="15" font-family="Consolas, Menlo, monospace" fill="#374151" text-anchor="middle">merge</text><rect x="2355" y="850" width="195" height="48" rx="8" fill="#60a5fa" stroke="#1e3a5f" stroke-width="2"/><text x="2452.5" y="880.375" font-size="15" font-family="Consolas, Menlo, monospace" fill="#374151" text-anchor="middle">render_chart</text><polyline points="1798,550 1968,430 2128,214" fill="none" stroke="#1e3a5f" stroke-width="2" marker-end="url(#ah)"/><polyline points="1798,560 2132,544" fill="none" stroke="#1e3a5f" stroke-width="3" marker-end="url(#ah)"/><polyline points="1798,570 1968,680 2128,874" fill="none" stroke="#1e3a5f" stroke-width="2" marker-end="url(#ah)"/><text x="1655.0" y="736.4" font-size="16" font-family="Consolas, Menlo, monospace" fill="#93c5fd" text-anchor="middle">no arrow, on purpose:</text><text x="1655.0" y="756.4" font-size="16" font-family="Consolas, Menlo, monospace" fill="#93c5fd" text-anchor="middle">these four groups are not in</text><text x="1655.0" y="776.4" font-size="16" font-family="Consolas, Menlo, monospace" fill="#93c5fd" text-anchor="middle">the planner&#x27;s prompt this turn.</text><text x="1655.0" y="796.4" font-size="16" font-family="Consolas, Menlo, monospace" fill="#93c5fd" text-anchor="middle">It cannot pick what it</text><text x="1655.0" y="816.4" font-size="16" font-family="Consolas, Menlo, monospace" fill="#93c5fd" text-anchor="middle">cannot see.</text><text x="2590" y="866.4" font-size="16" font-family="Consolas, Menlo, monospace" fill="#1e40af" text-anchor="start">18 visible, 8 this turn.</text><text x="2590" y="886.4" font-size="16" font-family="Consolas, Menlo, monospace" fill="#1e40af" text-anchor="start">All tools carry the analyze_ prefix</text><text x="2590" y="906.4" font-size="16" font-family="Consolas, Menlo, monospace" fill="#1e40af" text-anchor="start">except the four data-access ones.</text><text x="90" y="826.4" font-size="16" font-family="Consolas, Menlo, monospace" fill="#64748b" text-anchor="start">Why filter at all?</text><text x="90" y="846.4" font-size="16" font-family="Consolas, Menlo, monospace" fill="#64748b" text-anchor="start"></text><text x="90" y="866.4" font-size="16" font-family="Consolas, Menlo, monospace" fill="#64748b" text-anchor="start">18 tools that resemble one another — trend vs forecast,</text><text x="90" y="886.4" font-size="16" font-family="Consolas, Menlo, monospace" fill="#64748b" text-anchor="start">correlation vs importance, segmentation vs cluster — sit inside</text><text x="90" y="906.4" font-size="16" font-family="Consolas, Menlo, monospace" fill="#64748b" text-anchor="start">the band where selection accuracy starts to slip. Narrowing the</text><text x="90" y="926.4" font-size="16" font-family="Consolas, Menlo, monospace" fill="#64748b" text-anchor="start">menu is cheaper and safer than turning tools off.</text></svg></body>

src/agents/slow_path/prompt.py

@@ -157,10 +157,22 @@ def build_assembler_prompt(
 # full result, which reaches the user through the chart and the record.
 _MAX_LIST_ITEMS = 20
 _MAX_STATS_CHARS = 12_000
+# Below this, a value is left ALONE (A30 follow-up, 2026-09-18).
+#
+# This bound exists to stop floods, not to second-guess a tool that already bounded
+# itself. `analyze_anomaly` now caps its own sample at source and reports
+# `anomalies_total` / `anomalies_shown` alongside it; trimming that already-small
+# payload again saved ~490 tokens and left `anomalies_shown: 36` sitting next to a
+# 20-row list — recoverable from the "+16 more" marker, but the kind of mismatch a
+# narrative reads straight past. A tool that keeps its own payload sane should not
+# have its self-description invalidated by this function.
+_MIN_TRIM_CHARS = 8_000
 
 
 def _bound(value: object, _depth: int = 0) -> object:
     """Recursively trim long lists inside a tool value, then cap the whole string."""
+    if len(str(value)) <= _MIN_TRIM_CHARS:
+        return value
     trimmed = _trim_lists(value, _depth)
     text = str(trimmed)
     if len(text) > _MAX_STATS_CHARS: