feat(gooddata-eval): add the agentic forecasting evaluator - #1798
feat(gooddata-eval): add the agentic forecasting evaluator#1798Tomkess wants to merge 7 commits into
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The forecasting skill is enabled on the eval org and reachable today (confirmed
live: set_skills activates "forecasting"), but nothing evaluates it.
Unlike kda_skill this is not limited to "the process ran". The skill refuses to
execute unless the visualization carries an AAC forecast config, and that config
is exactly where the user's request lands:
config.forecast_enabled must be true or execute_forecast errors
config.forecast_period "next 3 months" is 3
config.forecast_confidence confidence level
config.forecast_seasonal whether seasonality is modelled
So a fixture states what it asked for and it is checked exactly, off the numbers
in the tool call the agent made -- no judge and no paraphrase tolerance. The
measure forecast is checked the same way, from the visualization's own fields.
expected_output pins whatever it wants:
{"metric": "metric/spend", "forecast_period": 3}
An unstated expectation passes rather than fails, and detail["asserted"] records
which checks the fixture actually pinned -- otherwise a run that verified nothing
reads identically to one where everything matched.
The loop follows kda_skill: the agent routinely asks which measure to forecast
before building anything (observed live: "your data has two different Spend
metrics"), so a simulated user answers from the fixture's own hints, and only
hints the fixture supplies reach the prompt.
LoopExit is deliberately not used -- it lands with #1789, which is still open.
This should gain exit_reason once that merges.
21 tests. 801 passed, lint and format clean.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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📝 WalkthroughWalkthroughAdds the ChangesAgentic forecasting
Estimated code review effort: 4 (Complex) | ~45 minutes Sequence Diagram(s)sequenceDiagram
participant CLI
participant evaluate_agentic_forecasting
participant ChatClient
participant OpenAI gpt-4o-mini
participant Langfuse
CLI->>evaluate_agentic_forecasting: evaluate forecasting request
evaluate_agentic_forecasting->>ChatClient: run forecast conversation
ChatClient-->>evaluate_agentic_forecasting: tool calls and run result
evaluate_agentic_forecasting->>OpenAI gpt-4o-mini: generate simulated reply when needed
OpenAI gpt-4o-mini-->>evaluate_agentic_forecasting: simulated response
evaluate_agentic_forecasting->>Langfuse: submit per-run scores
evaluate_agentic_forecasting-->>CLI: AgenticEvalOutcome or ForecastingAssertionError
Merge Risk: 🟡 Moderate · up to The new forecasting evaluator can incorrectly pass mismatched configurations, reject valid multi-turn forecasts, and report checks that fixtures did not request. These issues should be resolved before enabling the evaluator. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
✨ Finishing Touches 💡 1📝 Generate docstrings 💡
A rabbit checks the forecast chart, Comment |
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Actionable comments posted: 3
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Inline comments:
In `@packages/gooddata-eval/src/gooddata_eval/core/agentic/forecasting.py`:
- Around line 233-247: Extend _evaluate_run and the ForecastEvaluation result
handling to validate forecast_confidence and forecast_seasonal with exact
expected-versus-actual comparisons, including strict_pass, asserted fields,
_detail output, and trace scoring. Ensure fixtures specifying either value fail
when the received configuration differs, and update regression tests to cover
matching and mismatching confidence and seasonality expectations.
- Line 324: Update the forecast-call extraction assignments near _accumulate()
to pass all_tool_call_events instead of partial.tool_call_events, preserving
visualization and execute_forecast calls across conversation turns.
- Around line 474-475: Update the score payload around the forecast evaluation
fields so forecast_period_correct and forecast_metric_correct are included only
when their respective names are present in ev.asserted; do not submit unasserted
checks even when their internal values are True, while preserving the existing
values for asserted checks.
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Three findings, all real. Tool calls were extracted from the current turn only. The agent may build the chart on one turn and call execute_forecast on the next -- the common path, since it routinely asks which measure to forecast first -- and reading a single turn dropped the visualization the forecast actually ran on, failing a correct run for an empty config. Extraction now reads every turn accumulated so far. kda_skill does not have this bug only because its create and execute always land in the same turn. forecast_confidence and forecast_seasonal were described as checkable and never checked, so a fixture could pin either, receive something else, and pass. Both are now scored, with an absent forecast_seasonal counting as the tool's own default of false rather than as a mismatch. 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. 5 tests added; the cross-turn one verified to fail against the previous version. 808 passed, lint and format clean. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Also corrects the previous commit message, which said 808 tests; the suite is at 806. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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>
…detection 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 chart on one turn and detect on the next, and reading a single turn dropped the series the detection actually ran on, failing a correct run for having no metric or granularity. 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. 804 passed, lint and format clean. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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All three findings fixed in Tool calls extracted from the current turn only — the worst of the three, and it would have bitten the common path. The agent routinely asks which measure to forecast before building anything, so the chart lands on turn 2 and Worth noting why
Unasserted checks scored as BOOLEAN 1 — right, and the reasoning is exactly as you put it. The internal 5 tests added here (2 for confidence/seasonality, 1 for the tool default, 1 for the unasserted case, 1 for cross-turn extraction), 1 each on the siblings. The cross-turn test was verified to fail against the previous version on all three. 806 passed, lint and format clean. |
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>
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. 79% -> 93% on the module. What remains is the clarification prompt builder and the OpenAI call, both exercised through the loop tests via their patch points. 809 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. 946 passed, lint and format clean. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
1 participant
First of three evaluators for skills that ship in the product but have no eval coverage: forecasting, what-if analysis, anomaly detection. Each is a separate PR; this one is forecasting.
Why now
Probed live against
micdiagnose-dev: the skill is enabled and reachable — the agent answers a forecast question by activatingset_skills(["search", "forecasting", "visualization"]). Nothing evaluates it.This one can check correctness, not just completion
kda_skillis deliberately scoped to "the process ran to completion". Forecasting does not have to be, because the skill refuses to execute unless the visualization carries an AAC forecast config — and that config is exactly where the user's request lands:forecast_enabledtrue, orexecute_forecastreturns an errorforecast_period3forecast_confidenceforecast_seasonalSo "did it forecast the right horizon" is a number in the tool call the agent made. No judge, no paraphrase tolerance. The measure forecast is checked the same way, off the visualization's own fields.
A fixture pins whatever it cares about:
{"metric": "metric/spend", "forecast_period": 3}Two details worth review
An unstated expectation passes rather than fails — but
detail["asserted"]records which checks the fixture actually pinned. Without that, a run that verified nothing is indistinguishable in the report from one where everything matched.forecast_enabledmust be explicitlytrue. The tool treats unset andfalsethe same way, so the check does too — an agent that builds the right chart but never enables forecasting has not done the job.The loop
Follows
kda_skill. The agent routinely asks which measure to forecast before building anything — observed live: "your data has two different Spend metrics that could mean different things." A simulated user answers from the fixture's own hints, and only hints the fixture supplies reach the prompt, so an absent one is dropped rather than asserted as a literalNone.Not included, on purpose
LoopExit/exit_reason— it lands with #1789, still open. This should gain it once that merges, rather than duplicating the enum here.Tests
21, including: extraction pairing an execute with the visualization it followed (not the last of each independently), a bare-URI field in raw tool-call arguments, the wrong horizon failing on period alone, an unstated expectation neither failing nor silently passing, a chat error keeping what its
partial_resultcarried, and a chat error on a later run not discarding the earlier one.801 passed, lint and format clean.
Merge note
The two other PRs in this set touch the same three files —
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