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Included review availability: Your plan provides up to 1 included review per hour; 0 remain after this review. 📝 WalkthroughWalkthroughThe runner records diagnostics for failed and ungraded runs. Agentic evaluators propagate these records through outcomes and assertion errors. JSON reports expose them beside the winning run detail and identifiers. ChangesFailed Run Reporting
Priority: ⬇️ Low Estimated code review effort: 3 (Moderate) | ~25 minutes Change: Feature Sequence Diagram(s)sequenceDiagram
participant AgenticEvaluator
participant AgenticEvalOutcome
participant _process_item
participant ItemReport
participant _build_run_dict
AgenticEvaluator->>AgenticEvalOutcome: return winning detail and failed_runs
AgenticEvalOutcome->>_process_item: provide evaluation outcome
_process_item->>ItemReport: copy failed_runs
ItemReport->_build_run_dict: provide report data
_build_run_dict->>ItemReport: emit winning detail and failed_runs
Merge Risk: 🔵 Low · up to Diagnostics currently propagate, but a small test gap could allow future failed-run reporting regressions to pass unnoticed. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
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…single-shot PR #1816 added `failed_runs` to `ItemReport` and to the JSON report, but only `core/runner.py` ever filled it. The agentic kinds do not go through that runner: `cli/main.py` splits items on `AGENTIC_TEST_KINDS` and sends those to `cli/agentic_runner.run_agentic_items`, which calls each evaluator once with K and receives a single aggregate back. So every agentic result shipped the field empty. Measured on a real run: 135 `agentic_guardrail` results, every one with `failed_runs: []`, including 16 items that passed 1 of 3 runs and 29 that passed 2 of 3 -- precisely the items the field exists to explain. The runs were never actually lost. Each evaluator keeps its own `run_results` list; it simply never left the evaluator, because only `best` was carried out. So each K-running evaluator now builds the records from that list and attaches them to both its `AgenticEvalOutcome` and its `*AssertionError`, exactly as it already does for `reasoning_steps` and `detail`, and the runner reads them off either. - `core/agentic/_failed_runs.py`: `build_failed_runs`, shared by all seven kinds. Keys mirror `core.runner._failed_run_record` so a consumer can read `failed_runs` from either path without branching on test kind. `passed`/`detail` are supplied per kind because neither is uniform (`run.passed` vs `run.eval_result.strict_pass`). - Each evaluator grows a `_run_detail(run)` extracted from what it already built for the winning run, so a failing run is described by the same keys as the winner -- otherwise the two are not comparable, which is the whole point of keeping them. - `tool_call_count`/`tool_names` are the one addition over the single-shot record: the agentic kinds capture tool calls per run, and a final answer produced with no tool call at all is an agent answering from the model rather than the workspace. No other recorded field exposes that. - Ungraded runs are recorded with their `judge_error` rather than dropped; the verdict-level accounting stays in `unscored_runs`. - `agentic_conversation` is excluded: it drives its fixture exactly once whatever --runs says, so it has no K to have failing runs within. Tests: per-run detail/conversation ids/tool calls/ungraded runs on guardrail; the records reaching the report from both the outcome and the exception; and a structural per-kind guard, because a canned-outcome test cannot see whether the evaluator filled the field -- which is how this gap survived a release. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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Inline comments:
In `@packages/gooddata-eval/src/gooddata_eval/core/agentic/general_question.py`:
- Around line 352-356: Update run_agentic_items to build failed_runs before the
all-ungraded JudgeResponseError branch, attach those records to the raised
error, and ensure the runner’s generic error path copies them through
_apply_failed_runs so ItemReport preserves each run’s conversation ID, response
ID, and judge error.
In `@packages/gooddata-eval/src/gooddata_eval/core/agentic/guardrail.py`:
- Around line 318-322: Update the guardrail evaluation flow around
build_failed_runs and run_agentic_items so failed-run records, pass/effective
counts, and detail are computed before raising JudgeResponseError when all runs
have judge_error; attach these diagnostics to the exception. Add a dedicated
JudgeResponseError handler in run_agentic_items that propagates the exception’s
runs and detail into ItemReport using the same behavior as the assertion-failure
path, while preserving the existing generic error handling for other
RuntimeError cases.
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In `@packages/gooddata-eval/tests/test_agentic_runner.py`:
- Around line 860-861: Add a behavioral assertion to
test_an_item_with_no_gradeable_run_raises_instead_of_reporting_failures that the
multi-run JudgeResponseError includes both failed-run records in its failed_runs
data, rather than relying on the source-based attaches check.
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…ning one An item that passed 1 of 3 runs reported only the run that won, so the two that failed -- and the reason they did -- were discarded. Each failing run now keeps its own detail, conversation id and exit reason, and they reach the JSON report, which is where a failure is actually read. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
…single-shot The capture landed in core/runner.py, which the agentic kinds never reach: cli/main routes them to cli/agentic_runner instead, so every agentic result shipped the field present and empty. Measured on 2026-09-18: 135 agentic_guardrail results, all with `failed_runs: []`, including 45 items that passed some but not all of their runs. All twelve K-running kinds now build the records through one shared helper, so a failing run is described with the same keys as the winning one. Records are kept when every run went ungraded -- the judge breaking is exactly when the per-run conversation ids matter most, and that path previously threw them away. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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The gap
best_detaildescribes whichever run ranked highest. On a partial pass that means every visible verdict belongs to the attempt that worked, and the runs that failed leave no trace — theirdetailis computed inside the run loop and then dropped.So a 1-of-3 item is undiagnosable after the fact. The only recourse is re-running the question and hoping it fails the same way, which for a nondeterministic agent is not a given.
This isn't an edge case. On one evaluation day in our corpus, half of all lost runs sat on items whose recorded detail was entirely green — every criterion passing, the item still failing 2 of 3 times, and nothing anywhere explaining why.
The change
One new field on
ItemReport, emitted besidedetailin the JSON report:Kind-agnostic.
detailis opaque to the runner — it never inspects its shape — so this covers all test kinds and any added later, with no per-evaluator work.Failing runs only. A fully-passing item records nothing, so the cost tracks how broken the corpus is rather than how large it is, and shrinks as quality improves.
Nothing existing changes.
detailand the top-level ids keep their exact current meaning, so consumers of this report are unaffected.It also fixes a latent mismatch
The top-level
conversation_id/response_idare overwritten on every iteration and end up describing the last run, whilebest_detailandreasoning_stepsdescribe the best one. When those differ, the ids point at a different conversation than the detail beside them.best_chat_resultalready exists precisely to keepreasoning_stepsaligned withbest_detail(see the comment at its declaration) — the ids were never given the same treatment. Per-run ids make the pairing correct by construction rather than adding a fourth field to keep in sync.Why
stream_endedis in thereA stalled turn leaves the evaluator's gated checks
Falseeven though none of them ran, which reads as a content failure in every downstream rate. Recording it at the source removes the need for consumers to infer stalls from the shape of the detail block.Tests
Eight new tests.
uv run pytest— 976 passed, 0 failed.best_detailstays the winnerstream_endedandreasoning_step_countare recordedpass_power_k: falseon an item whose graded runs all passedfailed_runsbesidedetail, and[]for a clean item🤖 Generated with Claude Code
Summary by CodeRabbit
New Features
Tests