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QualityEngine - centralized scoring, domain-term whitelist, and budget enforcement.
Usage:
engine = QualityEngine()
report = engine.score_output("Some agent output text", "product_owner")
# report is a QualityReport with ARI scores, budget check, dimensions
"""
import re
import textstat
from .schemas import ARIResult, QualityDimension, QualityReport
class DomainTermWhitelist:
"""Known technical terms that ARI should not flag as verbosity.
When ARI (Automated Readability Index) scores technical text, domain-specific
multi-syllable terms like "microservices" and "containerization" inflate the
score. This class replaces those terms with short placeholders so the ARI
measures structural complexity rather than vocabulary density.
"""
def __init__(self):
self._terms: set[str] = set()
self._load_defaults()
def _load_defaults(self):
"""Seed from 100+ software engineering terms."""
self._terms = {
# Architecture patterns
"microservices",
"service-oriented",
"event-driven",
"domain-driven",
"clean architecture",
"hexagonal",
"layered architecture",
"command query responsibility segregation",
"cqrs",
"publish-subscribe",
"request-response",
"point-to-point",
# DDD terms
"bounded context",
"ubiquitous language",
"aggregate root",
"domain event",
"value object",
"domain service",
"anti-corruption layer",
"repository pattern",
"factory method",
# Database/data terms
"eventual consistency",
"strong consistency",
"relational database",
"non-relational",
"denormalization",
"materialized view",
"connection pooling",
"sharding",
"replication",
"acid",
"base",
"cap theorem",
"crud",
# Infrastructure
"continuous integration",
"continuous deployment",
"infrastructure as code",
"blue-green deployment",
"canary deployment",
"circuit breaker",
"bulkhead",
# Security
"cross-site scripting",
"cross-site request forgery",
"sql injection",
"denial of service",
"man-in-the-middle",
"role-based access control",
"attribute-based access control",
"authentication",
"authorization",
"single sign-on",
# Testing
"acceptance criteria",
"unit test",
"integration test",
"end-to-end test",
"regression test",
"performance test",
# General SE
"dependency injection",
"inversion of control",
"separation of concerns",
"single responsibility",
"open-closed principle",
"liskov substitution",
"interface segregation",
"dependency inversion",
# Additional technical terms
"asynchronous",
"containerization",
"orchestrator",
"idempotency",
"idempotent",
"serialization",
"deserialization",
"denormalized",
"polyglot",
"middleware",
"websocket",
"webhook",
# More architecture
"event sourcing",
"saga pattern",
"strangler fig",
"sidecar",
"ambassador",
"adapter",
"proxy",
# More data
"data warehouse",
"data lake",
"oltp",
"olap",
"etl",
# More infrastructure
"horizontal scaling",
"vertical scaling",
"auto-scaling",
"load balancing",
"service mesh",
"api gateway",
# More security
"oauth",
"jwt",
"saml",
"openid connect",
"zero trust",
"defense in depth",
"principle of least privilege",
}
def strip_known_terms(self, text: str) -> str:
"""Replace whitelisted terms with short tokens for ARI scoring.
Multi-word terms are matched longest-first to avoid partial replacement
(e.g., "blue-green deployment" replaces before "deployment" alone).
"""
result = text
for term in sorted(self._terms, key=len, reverse=True):
result = re.sub(
r"\b" + re.escape(term) + r"\b",
" word ",
result,
flags=re.IGNORECASE,
)
# Clean up extra spaces from placeholder insertion
result = re.sub(r" +", " ", result)
return result.strip()
def get_matched_terms(self, text: str) -> list[str]:
"""Return whitelisted terms found in text (for logging/metrics)."""
text_lower = text.lower()
matched: list[str] = []
for term in sorted(self._terms, key=len, reverse=True):
if term.lower() in text_lower:
matched.append(term)
text_lower = text_lower.replace(term.lower(), "")
return matched
class QualityEngine:
"""Centralized ARI scoring, whitelist filtering, and budget enforcement.
Each agent role has a calibrated ARI budget derived from research data.
The engine computes ARI (with and without domain-term whitelist) plus
complementary readability metrics (Flesch-Kincaid, Gunning Fog, Coleman-Liau).
Thread-safe: all computation is CPU-bound in textstat, runs synchronously.
Use ``score_output_async()`` for async callers.
"""
CALIBRATED_ARI_BUDGETS: dict[str, float] = {
"project_refiner": 12,
"product_owner": 12,
"business_analyst": 14,
"solution_architect": 20,
"data_architect": 18,
"security_analyst": 16,
"ux_designer": 14,
"api_designer": 14,
"qa_strategist": 16,
"devops_architect": 14,
"spec_coordinator": 14,
"technical_writer": 14,
"judge": 18,
}
def __init__(self, budgets: dict[str, float] | None = None):
self._whitelist: DomainTermWhitelist = DomainTermWhitelist()
self.budgets = budgets or dict(self.CALIBRATED_ARI_BUDGETS)
def score_output(self, text: str, role: str) -> QualityReport:
"""Score a single agent output. Synchronous - call via ``asyncio.to_thread``.
Steps:
1. Compute raw ARI on original text
2. Apply domain-term whitelist (strip known terms)
3. Compute whitelist-adjusted ARI + complementary metrics
4. Check role-specific budget
5. Build and return a complete QualityReport
"""
if not text.strip():
budget = self.budgets.get(role, 14)
return QualityReport(
role=role,
agent_output="",
ari_before_any=0,
final_ari=0,
ari_result=ARIResult(
raw_score=0, whitelist_score=0, budget=budget, passed=True
),
word_count=0,
sentence_count=0,
readability_label="Empty",
final_disposition="passed",
)
# Compute ARI on raw text (before whitelist)
raw_ari = textstat.automated_readability_index(text)
# Apply whitelist
cleaned = self._whitelist.strip_known_terms(text)
matched_terms = self._whitelist.get_matched_terms(text)
# Compute whitelist-adjusted ARI
adjusted_ari = textstat.automated_readability_index(cleaned)
# Compute complementary metrics (on cleaned text)
fk_grade = textstat.flesch_kincaid_grade(cleaned)
gf_index = textstat.gunning_fog(cleaned)
cl_index = textstat.coleman_liau_index(cleaned)
syl_per_word = textstat.avg_syllables_per_word(cleaned)
word_count = textstat.lexicon_count(cleaned)
sent_count = textstat.sentence_count(cleaned)
# Budget check
budget = self.budgets.get(role, 14)
ari_passed = adjusted_ari <= budget
# Dimensions
dimensions = [
QualityDimension(
name="ari_budget",
score=round(adjusted_ari, 2),
threshold=budget,
passed=ari_passed,
details=f"ARI {adjusted_ari:.1f} vs budget {budget}",
),
QualityDimension(
name="readability_fk",
score=round(fk_grade, 2),
threshold=budget + 2,
passed=fk_grade <= budget + 2,
details=f"Flesch-Kincaid {fk_grade:.1f}",
),
]
ari_result = ARIResult(
raw_score=round(raw_ari, 2),
whitelist_score=round(adjusted_ari, 2),
budget=budget,
passed=ari_passed,
terms_whitelisted=matched_terms,
)
report = QualityReport(
role=role,
agent_output=text,
ari_before_any=round(raw_ari, 2),
final_ari=round(adjusted_ari, 2),
flesch_kincaid_grade=round(fk_grade, 2),
gunning_fog_index=round(gf_index, 2),
coleman_liau_index=round(cl_index, 2),
avg_syllables_per_word=round(syl_per_word, 2),
word_count=word_count,
sentence_count=sent_count,
readability_label=self._readability_label(adjusted_ari),
ari_result=ari_result,
dimensions=dimensions,
final_disposition="passed" if ari_passed else "budget_exceeded",
)
return report
async def score_output_async(self, text: str, role: str) -> QualityReport:
"""Async wrapper - runs ``score_output`` in thread pool.
Usage in async orchestrators:
report = await self.quality_engine.score_output_async(text, role)
"""
import asyncio
return await asyncio.to_thread(self.score_output, text, role)
def detect_stagnation(self, scores: list[float]) -> bool:
"""Detect stagnation when ARI delta < 0.5 between last two attempts.
Returns True if 2+ scores exist and the absolute difference between
the last two scores is less than 0.5.
"""
if len(scores) < 2:
return False
return abs(scores[-1] - scores[-2]) < 0.5
@staticmethod
def _readability_label(ari: float) -> str:
"""Map ARI score to a readability level label."""
if ari <= 6:
return "Elementary"
elif ari <= 9:
return "Middle School"
elif ari <= 12:
return "High School"
elif ari <= 14:
return "College"
else:
return "Professional"
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