feat(gooddata-eval): add the agentic what-if-analysis evaluator - #1799
feat(gooddata-eval): add the agentic what-if-analysis evaluator#1799Tomkess wants to merge 6 commits into
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The what-if skill is enabled on the eval org and reachable today (confirmed live:
set_skills activates "what_if_analysis"), but nothing evaluates it.
This is the most checkable of the three analysis skills. The scenario spec carries
adjustments of the form {metric_id, metric_type, scenario_maql}, where
scenario_maql is the adjusted expression -- a 10% uplift on a revenue metric
defined as SELECT SUM({fact/price} * {fact/quantity}) becomes
SELECT SUM({fact/price} * 1.10 * {fact/quantity}). That is MAQL, and MAQL already
has a comparator here (evaluators._maql.normalize_maql, used by metric_skill), so
"did it apply the right adjustment" is answerable without a judge.
Checked: the tool chain triggered and executed successfully, the right measure was
adjusted, the adjustment matches (normalized, against a candidate list since
* 1.1 and * 1.10 are the same uplift), the scenario count, and whether a baseline
was requested. What the adjustment produced is deliberately not checked -- that is
the platform's arithmetic, not the agent's.
expected_output pins whatever it wants:
{"metric_id": "revenue", "scenario_maql": "SELECT SUM({fact/price} * 1.10 * ...)"}
An unstated expectation passes, and detail["asserted"] records which checks the
fixture pinned, so a run that verified nothing does not read as a full pass.
include_baseline needs care: the tool defaults it to true, so an absent argument
means the agent did ask for a baseline. Treating absent as false would fail
correct behaviour.
The loop follows kda_skill. The agent asks which measure to adjust before building
anything (observed live: "I need to confirm which 'Spend' calculation you want to
adjust"), so a simulated user answers from the fixture's own hints.
LoopExit is deliberately not used -- it lands with #1789, which is still open.
22 tests. 802 passed, lint and format clean.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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📝 WalkthroughWalkthroughAdds ChangesWhat-if evaluation
Sequence Diagram(s)sequenceDiagram
participant CLI
participant evaluate_agentic_what_if
participant run_agentic_what_if
participant ChatClient
participant Langfuse
CLI->>evaluate_agentic_what_if: dispatch question and expected output
evaluate_agentic_what_if->>run_agentic_what_if: run k evaluations
run_agentic_what_if->>ChatClient: send question and clarification replies
ChatClient-->>run_agentic_what_if: tool calls and responses
run_agentic_what_if-->>evaluate_agentic_what_if: run summary and scores
evaluate_agentic_what_if->>Langfuse: submit asserted scores and metadata
evaluate_agentic_what_if-->>CLI: return outcome or raise assertion error
Merge Risk: 🟡 Moderate · up to The new what-if evaluator can incorrectly mark a scenario as correct when the required metric and MAQL appear in different adjustments, reducing confidence in evaluation results. Its run-quality telemetry can also underreport multi-turn latency and failed-run cost; the matching issue should be resolved before merge. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
✨ Finishing Touches 💡 1📝 Generate docstrings 💡
A rabbit checks each scenario bright Comment |
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Additional details and impacted files@@ Coverage Diff @@
## master #1799 +/- ##
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Two of the three findings on #1798 are structural and apply here unchanged. Tool calls were extracted from the current turn only. The agent may build the scenario spec on one turn and execute it on the next -- the common path, since it asks which measure to adjust first -- and reading a single turn dropped the scenario the execution actually ran, failing a correct run for having no adjustments. Extraction now reads every turn accumulated so far. Unasserted content checks were published to Langfuse as BOOLEAN 1. They are True internally so they cannot fail a run, but reporting that as a score claims the evaluator verified something it never looked at. Only checks named in ev.asserted are now scored. The third finding (unchecked confidence/seasonality) was forecasting-specific. 1 test added, verified to fail against the previous version. 803 passed, lint and format clean. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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Review on the sibling PR #1798 surfaced two findings that are structural and applied here unchanged. Fixed in ``, before this PR was reviewed. Tool calls were extracted from the current turn only. The agent asks a disambiguation question before building anything, so the create call and the execute call can land on different turns — reading a single turn dropped the object the execution actually ran on, and the evaluator then failed a correct run for having no content to check. Extraction now reads every turn accumulated so far. Unasserted content checks were published to Langfuse as BOOLEAN 1. They are One test added, verified to fail against the previous version. Lint and format clean. |
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Inline comments:
In `@packages/gooddata-eval/src/gooddata_eval/core/agentic/what_if.py`:
- Around line 521-522: Update the metadata construction around latency_sec and
cost_usd to use trace-wide latency and cost for the complete run, following the
existing agentic evaluator pattern. Replace run.turn_wall_clock_sec and the
ev.triggered-gated pt.total_cost expression with the established trace-level
metrics, while preserving the surrounding metadata behavior.
- Around line 242-245: Update the MAQL validation near metric_correct and
maql_correct so maql_correct only evaluates adjustments whose metric matches
expected_metric, then checks scenario_maql against expected_maql on that same
adjustment. Preserve the existing string validation and _maql_matches behavior.
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packages/gooddata-eval/src/gooddata_eval/cli/agentic_runner.pypackages/gooddata-eval/src/gooddata_eval/core/agentic/what_if.pypackages/gooddata-eval/tests/test_agentic_runner.pypackages/gooddata-eval/tests/test_agentic_what_if.pypackages/gooddata-eval/tests/test_trace_linker.py
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Third structural finding from #1799's review, applied here. latency_sec used run.turn_wall_clock_sec, which is the goal turn alone and excludes the clarification turns that got there -- understating the item's real elapsed cost on exactly the runs where it matters. It now prefers pt.latency and falls back to the goal turn, which is what 7 of the 8 existing kinds already do; kda_skill is the outlier and documents its own reason, and this copied it without re-checking. cost_usd was gated on ev.triggered, so a run that answered without ever reaching the tool reported no cost despite having spent tokens. The gate is gone. 806 passed, lint and format clean. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Two findings from review. metric_correct and maql_correct searched every adjustment independently, so a wrong adjustment on the right measure and a right adjustment on the wrong measure satisfied one check each and the run passed -- two failures scoring as a success. The MAQL check is now scoped to adjustments on the expected measure. This also corrects the semantics when the wrong measure is adjusted: maql_correct is now False there too, because the expected measure was not adjusted at all, correctly or otherwise. A test asserting the old behaviour was documenting the independence that was the bug, and is updated to say why. Separately, latency_sec used run.turn_wall_clock_sec, the goal turn alone, excluding the clarification turns that got there -- understating the item's elapsed cost on exactly the runs where it matters. It now prefers pt.latency, as 7 of the 8 existing kinds do; kda_skill is the outlier and documents its reason. cost_usd is no longer gated on ev.triggered: a run that answered without ever reaching the tool still spent tokens. 2 tests added, one verified to fail against the previous version. 805 passed, lint and format clean. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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Both fixed in MAQL and metric matched independently. Correct, and it was a false PASS, not a false FAIL: a wrong adjustment on the right measure plus a right adjustment on the wrong measure satisfied one check each, and the run passed with two actual failures. The MAQL check is now scoped to adjustments on the expected measure. That also corrected a semantic I had wrong. Latency and cost. Also right, and I checked the claim rather than taking it: 7 of the 8 existing kinds use The Both latency/cost fixes applied to the siblings too — #1798 in 2 tests added, the pairing one verified to fail against the previous version. 805 passed, lint and format clean. |
The ev.asserted gating and the latency/cost change made in response to review were behavioural fixes shipped with no test. Covered now by capturing the deferred callable and running it against a fake context: only pinned checks are scored, every pinned check is scored, and cost is reported even when the tool was never reached. 808 passed, lint and format clean. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Merges master, which added a guard requiring every kind to hand the scored item's question to the linker -- a score is otherwise readable only by resolving its conversation back to the item. This kind predates the guard and did not. 945 passed, lint and format clean. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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Second of three evaluators for skills that ship in the product but have no eval coverage. Sibling PRs: #1798 (forecasting), anomaly detection to follow.
Why now
Probed live against
micdiagnose-dev: the skill is enabled and reachable — a what-if question activatesset_skills(["what_if_analysis", "visualization"]). Nothing evaluates it.The most checkable of the three
The scenario spec carries adjustments shaped
{metric_id, metric_type, scenario_maql}, andscenario_maqlis the adjusted expression. From the tool's own description: a 10% uplift on a revenue metric defined asSELECT SUM({fact/price} * {fact/quantity})becomesSELECT SUM({fact/price} * 1.10 * {fact/quantity}).That is MAQL, and MAQL already has a comparator in this package —
evaluators._maql.normalize_maql, used bymetric_skill. So "did it apply the right adjustment" is answerable exactly, without a judge.triggered/executed/successmetric_correctadjustments[].metric_idmaql_correctadjustments[].scenario_maql, normalizedscenario_count_correctlen(scenarios)baseline_correctinclude_baselineWhat the adjustment produced is deliberately not checked — that is the platform's arithmetic, not the agent's. Only that the agent asked for the right thing and the execution succeeded.
Three details worth review
A candidate list for MAQL.
* 1.1and* 1.10are the same uplift, and a rewrite can reach the same value, soscenario_maqlaccepts a string or a list — the same shapemetric_skilluses for its accepted MAQLs.include_baselinedefaults to true in the tool, so an absent argument means the agent did ask for a baseline. Treating absent as false would fail correct behaviour; the check treatsNoneas the default.An unstated expectation passes, with
detail["asserted"]recording which checks the fixture pinned — otherwise a run that verified nothing is indistinguishable from one where everything matched.The loop
Follows
kda_skill. The agent asks which measure to adjust before building anything — observed live: "I need to confirm which 'Spend' calculation you want to adjust (there are multiple in your data)." A simulated user answers from the fixture's hints; absent hints are dropped rather than asserted asNone.Not included, on purpose
LoopExit/exit_reason— lands with #1789, still open.Tests
22, including: extraction pairing an execute with the spec it followed, adjustments flattening across scenarios, MAQL compared normalized rather than literally, a candidate list accepting either uplift spelling, an absent
include_baselinecounting as the tool's default, and a malformed scenario list not raising.802 passed, lint and format clean.
Merge note
Touches the same three files as the sibling PRs —
cli/agentic_runner.pyplus the_ALL_AGENTIC_KIND_CASESand_EVALUATE_FUNCSstaleness guards. Whichever merges first, the others need a trivial rebase on those lists.🤖 Generated with Claude Code
Summary by CodeRabbit
New Features
Tests