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Add OpenQuestion to research contract

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New field on ResearchResult: open_questions — follow-up questions that
emerged from the research itself. Distinct from gaps (backward: what
failed) and discovery_events (sideways: what's lateral). Open questions
look forward: 'based on what I found, this needs deeper investigation.'

- OpenQuestion model: question, context, priority (high/medium/low),
  source_locator
- Updated agent synthesis prompt to produce open_questions
- Updated agent result builder to parse open_questions from JSON
- 3 new tests for OpenQuestion model
- Updated existing tests for new field

77 tests passing.

Refs: archeious/marchwarden#1

Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
This commit is contained in:
Jeff Smith 2026-04-08 14:37:30 -06:00
parent ece2455415
commit ae9c11a79b
4 changed files with 111 additions and 1 deletions
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20
researchers/web/agent.py
View file

@ -19,6 +19,7 @@ from researchers.web.models import (
DiscoveryEvent,
Gap,
GapCategory,
OpenQuestion,
ResearchConstraints,
ResearchResult,
)
@ -93,6 +94,14 @@ Produce a JSON object with these exact fields:
"source_locator": "URL where you found this, or null"
}}
],
"open_questions": [
{{
"question": "A follow-up question that emerged from the research",
"context": "What evidence prompted this question",
"priority": "high|medium|low",
"source_locator": "URL where this question arose, or null"
}}
],
"confidence": 0.0-1.0,
"confidence_factors": {{
"num_corroborating_sources": 0,
@ -506,6 +515,16 @@ class WebResearcher:
for d in data.get("discovery_events", [])
]
open_questions = [
OpenQuestion(
question=q.get("question", ""),
context=q.get("context", ""),
priority=q.get("priority", "medium"),
source_locator=q.get("source_locator"),
)
for q in data.get("open_questions", [])
]
cf = data.get("confidence_factors", {})
confidence_factors = ConfidenceFactors(
num_corroborating_sources=cf.get("num_corroborating_sources", 0),
@ -521,6 +540,7 @@ class WebResearcher:
citations=citations,
gaps=gaps,
discovery_events=discovery_events,
open_questions=open_questions,
confidence=data.get("confidence", 0.5),
confidence_factors=confidence_factors,
cost_metadata=CostMetadata(

33
researchers/web/models.py
View file

@ -132,6 +132,35 @@ class DiscoveryEvent(BaseModel):
)
# ---------------------------------------------------------------------------
# Output types — OpenQuestion
# ---------------------------------------------------------------------------
class OpenQuestion(BaseModel):
"""A follow-up question that emerged from the research.
Distinct from gaps (what failed) and discovery events (what's lateral).
Open questions look forward: "based on what I found, this needs deeper
investigation." The PI uses these to decide whether to dispatch
additional research calls.
"""
question: str = Field(
description="The follow-up question that emerged from the research.",
)
context: str = Field(
description="What evidence or finding prompted this question.",
)
priority: str = Field(
description="'high' (critical to answer quality), 'medium' (would improve answer), 'low' (nice to know).",
)
source_locator: Optional[str] = Field(
default=None,
description="URL or source where this question arose from.",
)
# ---------------------------------------------------------------------------
# Output types — Confidence
# ---------------------------------------------------------------------------
@ -215,6 +244,10 @@ class ResearchResult(BaseModel):
default_factory=list,
description="Lateral findings for other researchers.",
)
open_questions: list[OpenQuestion] = Field(
default_factory=list,
description="Follow-up questions that emerged from the research.",
)
confidence: float = Field(
ge=0.0,
le=1.0,

10
tests/test_agent.py
View file

@ -70,6 +70,14 @@ VALID_SYNTHESIS_JSON = json.dumps(
"source_locator": "https://example.com/ref",
}
],
"open_questions": [
{
"question": "What is the optimal irrigation schedule for high-elevation potatoes?",
"context": "Multiple sources mention irrigation is critical but none specify schedules.",
"priority": "medium",
"source_locator": "https://example.com/utah-crops",
}
],
"confidence": 0.82,
"confidence_factors": {
"num_corroborating_sources": 3,
@ -197,6 +205,8 @@ class TestWebResearcher:
assert len(result.gaps) == 1
assert result.gaps[0].category == "source_not_found"
assert len(result.discovery_events) == 1
assert len(result.open_questions) == 1
assert "irrigation" in result.open_questions[0].question
assert result.confidence == 0.82
assert result.confidence_factors.num_corroborating_sources == 3
assert result.cost_metadata.model_id == "claude-test"

49
tests/test_models.py
View file

@ -13,6 +13,7 @@ from researchers.web.models import (
DiscoveryEvent,
Gap,
GapCategory,
OpenQuestion,
ResearchConstraints,
ResearchResult,
)
@ -58,6 +59,17 @@ def make_discovery_event(**overrides) -> DiscoveryEvent:
return DiscoveryEvent(**defaults)
def make_open_question(**overrides) -> OpenQuestion:
defaults = {
"question": "What is the optimal irrigation schedule for high-elevation potatoes?",
"context": "Multiple sources mention irrigation is critical but none specify schedules.",
"priority": "medium",
"source_locator": "https://example.com/utah-crops",
}
defaults.update(overrides)
return OpenQuestion(**defaults)
def make_confidence_factors(**overrides) -> ConfidenceFactors:
defaults = {
"num_corroborating_sources": 3,
@ -89,6 +101,7 @@ def make_research_result(**overrides) -> ResearchResult:
"citations": [make_citation()],
"gaps": [make_gap()],
"discovery_events": [make_discovery_event()],
"open_questions": [make_open_question()],
"confidence": 0.82,
"confidence_factors": make_confidence_factors(),
"cost_metadata": make_cost_metadata(),
@ -250,6 +263,33 @@ class TestDiscoveryEvent:
assert e == e2
# ---------------------------------------------------------------------------
# OpenQuestion
# ---------------------------------------------------------------------------
class TestOpenQuestion:
def test_full_question(self):
q = make_open_question()
assert "irrigation" in q.question
assert q.priority == "medium"
assert q.source_locator is not None
def test_minimal_question(self):
q = OpenQuestion(
question="Is this viable?",
context="Found conflicting data.",
priority="low",
)
assert q.source_locator is None
def test_serialization_roundtrip(self):
q = make_open_question()
data = q.model_dump()
q2 = OpenQuestion(**data)
assert q == q2
# ---------------------------------------------------------------------------
# ConfidenceFactors
# ---------------------------------------------------------------------------
@ -325,16 +365,18 @@ class TestResearchResult:
assert len(r.citations) == 1
assert len(r.gaps) == 1
assert len(r.discovery_events) == 1
assert len(r.open_questions) == 1
assert 0.0 <= r.confidence <= 1.0
assert r.cost_metadata.model_id == "claude-sonnet-4-6"
def test_empty_lists_allowed(self):
r = make_research_result(
citations=[], gaps=[], discovery_events=[]
citations=[], gaps=[], discovery_events=[], open_questions=[]
)
assert r.citations == []
assert r.gaps == []
assert r.discovery_events == []
assert r.open_questions == []
def test_confidence_bounds(self):
with pytest.raises(ValidationError):
@ -358,6 +400,7 @@ class TestResearchResult:
"citations",
"gaps",
"discovery_events",
"open_questions",
"confidence",
"confidence_factors",
"cost_metadata",
@ -365,6 +408,10 @@ class TestResearchResult:
}
assert set(data.keys()) == expected_keys
# OpenQuestion keys
oq_keys = {"question", "context", "priority", "source_locator"}
assert set(data["open_questions"][0].keys()) == oq_keys
# Citation keys
citation_keys = {
"source",