feat(gooddata-eval): add the agentic forecasting evaluator

[Tomkess]

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 activating set_skills(["search", "forecasting", "visualization"]). Nothing evaluates it.

This one can check correctness, not just completion

kda_skill is 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:

Config field	Carries
forecast_enabled	must be true, or execute_forecast returns an error
forecast_period	the horizon — "next 3 months" is 3
forecast_confidence	confidence level
forecast_seasonal	whether seasonality is modelled

So "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_enabled must be explicitly true. The tool treats unset and false the 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 literal None.

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_result carried, 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.py plus the _ALL_AGENTIC_KIND_CASES and _EVALUATE_FUNCS staleness guards. Whichever merges first, the others need a trivial rebase on those lists.

🤖 Generated with Claude Code

Summary by CodeRabbit

• New Features

  • Added support for agentic forecasting evaluations.
  • Forecasting runs can verify forecast activation, period selection, and metric accuracy across multiple attempts.
  • Added pass-rate summaries and detailed diagnostics for forecasting evaluation results.

• Tests

  • Added coverage for forecasting behavior, evaluation scoring, dispatching, and trace reporting.

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📥 Commits

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📒 Files selected for processing (3)

• packages/gooddata-eval/src/gooddata_eval/core/agentic/forecasting.py
• packages/gooddata-eval/tests/test_agentic_forecasting.py
• packages/gooddata-eval/tests/test_trace_linker.py

📝 Walkthrough

Walkthrough

Adds the agentic_forecasting evaluation kind. The evaluator runs forecasting conversations, scores forecast configuration and metrics, aggregates K runs, reports failures, submits trace scores, and exposes the evaluator through CLI dispatch.

Changes

Agentic forecasting

Layer / File(s)	Summary
Forecast contract and scoring packages/gooddata-eval/src/gooddata_eval/core/agentic/forecasting.py, packages/gooddata-eval/tests/test_agentic_forecasting.py	Defines forecast result data, extracts visualization and execution calls, resolves metric URIs, and validates forecast enablement, period, metrics, and execution status.
Forecast conversation execution packages/gooddata-eval/src/gooddata_eval/core/agentic/forecasting.py, packages/gooddata-eval/tests/test_agentic_forecasting.py	Runs conversations with iteration limits, simulated replies, partial-result handling, conversation cleanup, and K-run aggregation.
Forecast evaluator integration packages/gooddata-eval/src/gooddata_eval/core/agentic/forecasting.py, packages/gooddata-eval/src/gooddata_eval/cli/agentic_runner.py, packages/gooddata-eval/tests/test_agentic_forecasting.py, packages/gooddata-eval/tests/test_agentic_runner.py, packages/gooddata-eval/tests/test_trace_linker.py	Adds assertion details and Langfuse scoring, registers CLI dispatch, and extends dispatch and trace-linker regression coverage.

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

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Merge Risk: 🟡 Moderate · up to 1d9d7

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)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 35.71% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 42 functions across 5 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title clearly and concisely identifies the main change: adding an agentic forecasting evaluator to gooddata-eval.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

✨ Finishing Touches 💡 1

📝 Generate docstrings 💡

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---

_{A rabbit checks the forecast chart,
Counts each turn and plays its part.
It asks the model, “Try once more,”
Then scores the horizon it saw before.
Metrics hop neatly into line.}

_{Comment @coderabbitai help to get the list of available commands.}

[codecov]

Codecov Report

❌ Patch coverage is 92.94606% with 17 lines in your changes missing coverage. Please review.
✅ Project coverage is 82.39%. Comparing base (72858ca) to head (559c18a).

Files with missing lines	Patch %	Lines
...eval/src/gooddata_eval/core/agentic/forecasting.py	92.85%	17 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##           master    #1798      +/-   ##
==========================================
+ Coverage   82.27%   82.39%   +0.12%     
==========================================
  Files         282      283       +1     
  Lines       20326    20567     +241     
==========================================
+ Hits        16723    16947     +224     
- Misses       3603     3620      +17     

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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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Configuration used: Organization UI

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📥 Commits

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📒 Files selected for processing (5)

• packages/gooddata-eval/src/gooddata_eval/cli/agentic_runner.py
• packages/gooddata-eval/src/gooddata_eval/core/agentic/forecasting.py
• packages/gooddata-eval/tests/test_agentic_forecasting.py
• packages/gooddata-eval/tests/test_agentic_runner.py
• packages/gooddata-eval/tests/test_trace_linker.py

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[Tomkess]

All three findings fixed in 59958ce0 (+ 273b8428 for a PERF403 follow-up). All three were real, and two of them applied to the sibling PRs too — fixed there as well, #1799 in ab988606 and #1801 in bed8974d.

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 execute_forecast on turn 3; reading only the current turn dropped the visualization the forecast actually ran on, and the evaluator then failed a correct run for an empty config. Extraction now reads every turn accumulated so far.

Worth noting why kda_skill doesn't have this bug despite the identical structure: its create and execute always land in the same turn ("NO confirmation needed" in the skill's own prompt), so a single-turn read is sufficient there. I copied the shape without re-checking that assumption held for a skill that does ask questions first.

forecast_confidence and forecast_seasonal unchecked — correct, and it's the same failure as the unwired max_widgets you caught on #1797: I described them as checkable in the module docstring and the PR body, then never implemented them. Both are now scored. One detail: an absent forecast_seasonal counts as the tool's own default of false rather than a mismatch, so an agent that leaves the default alone isn't penalised.

Unasserted checks scored as BOOLEAN 1 — right, and the reasoning is exactly as you put it. The internal True exists so an unstated expectation can't fail a run; publishing it as a score claims the evaluator verified something it never looked at. Only checks named in ev.asserted are scored now, on all three PRs.

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.