feat(gooddata-eval): record why an agentic simulated-user loop stopped - #1789
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📝 WalkthroughWalkthroughThe change adds shared loop-exit classifications and records exit reasons and turn counts for conversation, alert, KDA, metric, and visualization evaluations. Evaluation details and tests now expose success, silence, budget exhaustion, and harness failure states. ChangesAgentic loop observability
Priority: ⬇️ Low Estimated code review effort: 3 (Moderate) | ~25 minutes Merge Risk: 🟡 Moderate · up to Agentic evaluation results now expose loop termination diagnostics, but several error paths still abort without those diagnostics, and conversation and zero-iteration reporting remain inconsistent. This can leave evaluation consumers unable to distinguish harness failures from agent outcomes, so the change should be completed before merge. Sequence Diagram(s)sequenceDiagram
participant Agent
participant SimulatedUser
participant AgenticSkillRunner
participant EvaluationDetail
Agent->>AgenticSkillRunner: produce tool call or response
AgenticSkillRunner->>SimulatedUser: request next simulated reply
SimulatedUser-->>AgenticSkillRunner: return reply or failure
AgenticSkillRunner->>EvaluationDetail: store exit_reason and turns_used
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Inline comments:
In `@packages/gooddata-eval/src/gooddata_eval/core/agentic/conversation.py`:
- Around line 119-120: Update TurnResult and the conversation detail payload
around _DETAIL_FIELDS to expose turns_used and max_iterations for every reported
turn, deriving turns_used from the actual message-turn count and using the
configured iteration limit; ensure LoopExit.NOT_RUN reports turns_used as 0
while preserving the existing exit_reason detail.
In `@packages/gooddata-eval/src/gooddata_eval/core/agentic/visualization.py`:
- Around line 436-437: Update _execute_single_run and the turns_used payload
calculation so every send_message call, including the initial request when
max_iterations is zero, is counted. Validate max_iterations before sending the
initial request or increment total_turns for that request, ensuring turns_used
never reports zero after a request is sent.
In `@packages/gooddata-eval/tests/test_agentic_alert_skill.py`:
- Line 968: In the alert test at
packages/gooddata-eval/tests/test_agentic_alert_skill.py:968, add an assertion
that detail["max_iterations"] equals 6 after _run_alert(..., max_iterations=6).
Apply the same assertion in the metric test at
packages/gooddata-eval/tests/test_agentic_metric_skill.py:845 to validate both
early-termination result details.
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Codecov Report❌ Patch coverage is Additional details and impacted files@@ Coverage Diff @@
## master #1789 +/- ##
==========================================
+ Coverage 81.86% 81.91% +0.04%
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Files 277 277
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+ Hits 16387 16474 +87
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Every agentic evaluator drives a loop that can exit several ways, but the result
only ever recorded *whether* the agent produced its output. A run that ran out of
turns while doing the right thing was reported identically to one that refused,
and identically to one that answered wrongly.
It is worse than a missing field, because the downstream checks are all of the
form `produced_output and <check>`. An exhausted alert run reports
operator_correct, threshold_correct, metric_correct and recipients_correct as
False -- four specific-sounding content failures for work the agent was never
given the chance to attempt.
Adds `LoopExit` (core/models.py) and threads it through all five loops, plus
`turns_used`/`max_iterations` in `detail`:
success the agent produced its output
agent_silent neither text nor a tool call -- genuinely stuck
budget_exhausted hit max_iterations; says nothing about being on track
simulated_user_failed OUR simulated-user model failed, not the agent
chat_error the chat call raised mid-conversation (kda partial path)
not_run the loop never started (conversation $ref skip)
The default is BUDGET_EXHAUSTED and every other exit assigns explicitly, so a loop
that simply runs out of range() is labelled correctly with no trailing else.
Two exits were previously invisible and are the reason this is worth doing:
- metric_skill catches SimulatedResponseError and breaks. A harness-side outage
was scored against the product as metric_created=False/maql_correct=False.
- kda_skill breaks on a chat error with a partial result.
Deliberately NOT included:
- No verdict changes. An exhausted run still fails. The point is that the cases
become countable, not that any of them start passing.
- No change to any max_iterations default (4-7, already tuned per kind). Whether
a budget is too tight becomes answerable from data instead of argued -- which is
the actual ask behind GDAI-2200, where ~13% of alert runs are estimated to need
7 turns against a ceiling of 6. Raising the ceiling first would have hidden the
interaction with GDAI-2199's MANDATORY STOPs, which make prescribed
end-turn-without-a-tool-call behaviour consume budget.
- No try/except added to alert_skill's simulated-user call: there a failure
already propagates as a hard error rather than being swallowed, which is the
behaviour we want. Only metric_skill needed the label.
Tests: existing detail assertions extended across all five kinds, plus dedicated
coverage for budget_exhausted vs agent_silent vs success (including the turn the
tool landed on), simulated_user_failed, and a regression guard asserting that two
runs with identical scored booleans differ only in exit_reason -- the exact
ambiguity this removes.
739 passed; ruff check clean.
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Inline comments:
In `@packages/gooddata-eval/src/gooddata_eval/core/agentic/alert_skill.py`:
- Around line 713-717: Update alert_skill.py lines 713-717 in the alert run loop
to catch simulated-user failures, set LoopExit.SIMULATED_USER_FAILED, and return
an AlertRunResult with the available evaluation details. At alert_skill.py line
685, catch chat failures, set LoopExit.CHAT_ERROR, and return an AlertRunResult.
At metric_skill.py line 271, catch chat failures, set LoopExit.CHAT_ERROR, and
return a MetricRunResult; ensure all returned results include the established
exit_reason and turns_used fields.
In `@packages/gooddata-eval/src/gooddata_eval/core/agentic/conversation.py`:
- Line 519: In
packages/gooddata-eval/src/gooddata_eval/core/agentic/conversation.py lines
519-519, catch ChatError around ChatClient.send_message() and append a failed
turn with exit_reason=LoopExit.CHAT_ERROR. In
packages/gooddata-eval/src/gooddata_eval/core/agentic/visualization.py lines
250-250, catch ChatError around both message sends and return a RunResult with
exit_reason=LoopExit.CHAT_ERROR, preserving normal behavior for successful
sends.
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packages/gooddata-eval/src/gooddata_eval/core/agentic/alert_skill.pypackages/gooddata-eval/src/gooddata_eval/core/agentic/conversation.pypackages/gooddata-eval/src/gooddata_eval/core/agentic/kda_skill.pypackages/gooddata-eval/src/gooddata_eval/core/agentic/metric_skill.pypackages/gooddata-eval/src/gooddata_eval/core/agentic/visualization.pypackages/gooddata-eval/src/gooddata_eval/core/models.pypackages/gooddata-eval/tests/test_agentic_alert_skill.pypackages/gooddata-eval/tests/test_agentic_metric_skill.py
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… work All three findings were valid. 1. visualization reported turns_used=0 after sending a request. The initial send_message happens before the loop, but total_turns only incremented inside it -- so max_iterations=0 sent one message and claimed zero turns. Counted at the send instead, with the loop's first pass skipping its own increment to compensate; verified turns_used == send_message call count for every (max_iterations, break point) combination, not just the edge case. 2. agentic_conversation was the exception to the new detail contract: it had no turns_used equivalent and no budget. It already tracked clarification_turns_used per turn and simply never reported it -- now in _DETAIL_FIELDS -- and ConversationResult carries max_clarification_turns so detail can state the limit the way every other kind does. LoopExit.NOT_RUN already reports 0, since that path never touches the counter. 3. The two early-termination tests asserted exit_reason and turns_used but not max_iterations, so a wrong or missing limit would have passed unnoticed. Adds a parametrized regression test over max_iterations 0..3 asserting total_turns equals the number of requests actually sent, which is the invariant finding 1 broke. 785 passed; ruff check clean; ruff format delta unchanged from master's baseline.
…aborting the item LoopExit declared CHAT_ERROR and SIMULATED_USER_FAILED, but only kda_skill could produce either: every other kind let the exception escape its runner. Since the K-run loop appends results as it goes, an exception on run 2 discarded run 1 along with any exit_reason -- an infrastructure blip erased the results of the runs that had worked, and the item reported no loop-exit at all. The contract this PR introduces was therefore unreachable in four of the five kinds. Both faults are now caught where kda_skill already catches them: - alert_skill, metric_skill, conversation, visualization record CHAT_ERROR and end the run, harvesting exc.partial_result where the surrounding code already knows how to consume one. - alert_skill and visualization record SIMULATED_USER_FAILED. alert_skill deliberately let this propagate, to keep a harness fault from being scored as a content failure; the exit reason achieves that same separation -- reporting reads it to classify the run as an error -- while keeping the completed runs. - visualization guards its opening request too, where there is no partial conversation to evaluate, and skips the loop rather than scoring a stale result. - conversation's no_error was hardcoded True on the reasoning that a chat fault would have escaped before reaching it. It now reads the exit reason back. The catch is ChatError, not Exception: a bug in our own code must still surface as a crash rather than be relabelled as a GoodData-side fault. kda_skill catches Exception broadly and is left alone here, but the two should agree. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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Follow-up to GDAI-2200, which closed with "no gen-ai change, fix in the eval". This is the harness-side half — minus the budget raise, for reasons below.
The problem
Every agentic evaluator drives the agent through a simulated-user loop that can exit several ways. Only "the agent produced its output" was ever recorded. A run that ran out of turns while doing the right thing is reported identically to one that refused, and identically to one that answered wrongly.
It's worse than a missing field, because every downstream check has the form
produced_output and <check>. An exhausted alert run reports:{"alert_created": false, "operator_correct": false, "threshold_correct": false, "metric_correct": false, "recipients_correct": false}Four specific-sounding content failures for work the agent was never given the chance to attempt. That false precision is the same objection raised internally about stalled visualization runs.
What this adds
LoopExitincore/models.py, threaded through all five loops, plusturns_usedandmax_iterationsindetail:successagent_silentbudget_exhaustedmax_iterations; says nothing about being on tracksimulated_user_failedchat_errornot_run$refskip)The field defaults to
BUDGET_EXHAUSTEDand every other exit assigns explicitly, so a loop that simply runs out ofrange()is labelled correctly without a trailingelse.Two exits were previously invisible, and they're the reason this is worth doing:
metric_skillcatchesSimulatedResponseErrorandbreaks. A failure of our own gpt-4o-mini was scored against the product asmetric_created=False, maql_correct=False.kda_skillbreaks on a chat error with a partial result.Deliberately not included
max_iterationsdefault (4–7, already tuned per kind). GDAI-2200 estimates ~13% of alert runs need 7 turns against a ceiling of 6 — but raising the ceiling first would hide its interaction with GDAI-2199's MANDATORY STOPs, which make prescribed end-turn-without-a-tool-call behaviour consume budget. Withexit_reasonin place, "is this budget too tight" becomes answerable from data instead of argued.try/exceptaroundalert_skill's simulated-user call. There a failure already propagates as a hard error rather than being swallowed into a content failure, which is the behaviour we want. Onlymetric_skillneeded the label.Tests
Existing
detailassertions extended across all five kinds, plus dedicated coverage forbudget_exhaustedvsagent_silentvssuccess(including which turn the tool landed on),simulated_user_failed, and a regression guard asserting two runs with identical scored booleans differ only inexit_reason— the exact ambiguity this removes.739 passed,ruff checkclean.ruff formatreports the same 8 pre-existing files as master — none added.Summary by CodeRabbit
New Features
Tests