feat(J): J-SIGNAL/J-PATTERN/J-PROP/J-DRAFT/J-EVAL/J-APPROVAL/J-ACTIVATE/J-MEASURE — controlled self-improvement backend (signals, patterns, proposals, drafts, evaluation, approval, activation, rollback, impact measurement), 26 tests passing
This commit is contained in:
Agent Zero
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"""Controlled self-improvement system (Phase J).
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Implements the improvement loop:
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Observe → Detect Patterns → Propose → Draft → Evaluate →
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Human Approval → Activate → Measure → Keep/Rollback
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No autonomous production code changes. All improvements go through
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versioned drafts, evaluation, and human approval.
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"""
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from __future__ import annotations
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import logging
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import uuid
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from dataclasses import dataclass, field
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from datetime import UTC, datetime, timedelta
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from enum import Enum
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from typing import Any, Literal
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from sqlalchemy import func, select
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from sqlalchemy.ext.asyncio import AsyncSession
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logger = logging.getLogger(__name__)
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# ─── Enums ───────────────────────────────────────────────────────────────────
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class ProposalType(str, Enum):
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AGENT = "agent"
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SKILL = "skill"
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TRIGGER = "trigger"
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WORKFLOW = "workflow"
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MINIAPP_TEMPLATE = "miniapp_template"
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PLUGIN_PATCH = "plugin_patch"
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class ProposalStatus(str, Enum):
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DRAFT = "draft"
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EVALUATING = "evaluating"
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PENDING_APPROVAL = "pending_approval"
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APPROVED = "approved"
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REJECTED = "rejected"
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ACTIVE = "active"
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ROLLED_BACK = "rolled_back"
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EXPIRED = "expired"
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class SignalType(str, Enum):
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AGENT_RUN = "agent_run"
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WORKFLOW_RUN = "workflow_run"
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PROACTIVE_SUGGESTION = "proactive_suggestion"
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AUDIT_LOG = "audit_log"
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ENTITY_HISTORY = "entity_history"
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USER_CORRECTION = "user_correction"
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HANDOFF = "handoff"
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ERROR_RETRY = "error_retry"
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# ─── J-SIGNAL: Improvement Signals ───────────────────────────────────────────
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@dataclass
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class ImprovementSignal:
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"""A referenced signal from platform usage data (J-SIGNAL).
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Uses references/aggregates instead of full PII copies.
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Data minimization/exposure-policy/retention apply.
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"""
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id: str = field(default_factory=lambda: str(uuid.uuid4()))
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signal_type: SignalType = SignalType.AGENT_RUN
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source_ref: str = "" # Reference to source (e.g. "agent_run:uuid")
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tenant_id: str = ""
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user_id: str | None = None
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timestamp: datetime = field(default_factory=lambda: datetime.now(UTC))
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outcome: str = "" # success, failure, corrected, dismissed, accepted
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metadata: dict[str, Any] = field(default_factory=dict)
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def to_dict(self) -> dict[str, Any]:
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return {
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"id": self.id,
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"signal_type": self.signal_type.value,
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"source_ref": self.source_ref,
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"tenant_id": self.tenant_id,
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"user_id": self.user_id,
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"timestamp": self.timestamp.isoformat(),
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"outcome": self.outcome,
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"metadata": self.metadata,
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}
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async def collect_signals(
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db: AsyncSession,
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tenant_id: uuid.UUID,
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days: int = 30,
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) -> list[ImprovementSignal]:
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"""Collect improvement signals from platform usage data (J-SIGNAL).
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Aggregates signals from AgentRuns, WorkflowRuns, AuditLog, and
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Proactive Suggestions. Uses references, not full PII copies.
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"""
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since = datetime.now(UTC) - timedelta(days=days)
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signals: list[ImprovementSignal] = []
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# Agent run signals
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try:
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from app.models.workflow import AgentRun
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result = await db.execute(
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select(AgentRun).where(
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AgentRun.tenant_id == tenant_id,
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AgentRun.created_at >= since,
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).limit(200)
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)
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for run in result.scalars().all():
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signals.append(ImprovementSignal(
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signal_type=SignalType.AGENT_RUN,
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source_ref=f"agent_run:{run.id}",
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tenant_id=str(tenant_id),
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user_id=str(run.user_id) if run.user_id else None,
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timestamp=run.created_at or datetime.now(UTC),
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outcome=run.status or "unknown",
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metadata={"agent_id": str(run.agent_id) if run.agent_id else None, "cost_usd": float(run.total_cost_usd or 0)},
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))
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except Exception as e:
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logger.warning("Signal collection (agent_runs) failed: %s", e)
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# Workflow instance signals
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try:
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from app.models.workflow import WorkflowInstance
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result = await db.execute(
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select(WorkflowInstance).where(
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WorkflowInstance.tenant_id == tenant_id,
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WorkflowInstance.created_at >= since,
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).limit(200)
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)
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for inst in result.scalars().all():
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signals.append(ImprovementSignal(
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signal_type=SignalType.WORKFLOW_RUN,
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source_ref=f"workflow_instance:{inst.id}",
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tenant_id=str(tenant_id),
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timestamp=inst.created_at or datetime.now(UTC),
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outcome=inst.status or "unknown",
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metadata={"workflow_id": str(inst.workflow_id) if inst.workflow_id else None},
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))
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except Exception as e:
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logger.warning("Signal collection (workflow_instances) failed: %s", e)
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# Audit log signals (user corrections)
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try:
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from app.models.audit import AuditLog
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result = await db.execute(
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select(AuditLog).where(
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AuditLog.tenant_id == tenant_id,
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AuditLog.created_at >= since,
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AuditLog.action.like("%.correct%"),
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).limit(100)
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)
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for entry in result.scalars().all():
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signals.append(ImprovementSignal(
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signal_type=SignalType.USER_CORRECTION,
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source_ref=f"audit:{entry.id}",
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tenant_id=str(tenant_id),
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user_id=str(entry.user_id) if entry.user_id else None,
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timestamp=entry.created_at or datetime.now(UTC),
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outcome="corrected",
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metadata={"action": entry.action},
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))
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except Exception as e:
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logger.warning("Signal collection (audit) failed: %s", e)
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return signals
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# ─── J-PATTERN: Pattern/Bottleneck Detection ────────────────────────────────
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@dataclass
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class DetectedPattern:
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"""A detected pattern or bottleneck from signals (J-PATTERN)."""
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id: str = field(default_factory=lambda: str(uuid.uuid4()))
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pattern_type: str = "" # repetitive_sequence, frequent_corrections, rejected_suggestions, error_retries, repetitive_handoffs
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description: str = ""
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confidence: float = 0.0
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occurrence_count: int = 0
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evidence_refs: list[str] = field(default_factory=list)
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metadata: dict[str, Any] = field(default_factory=dict)
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def to_dict(self) -> dict[str, Any]:
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return {
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"id": self.id,
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"pattern_type": self.pattern_type,
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"description": self.description,
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"confidence": self.confidence,
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"occurrence_count": self.occurrence_count,
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"evidence_refs": self.evidence_refs,
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"metadata": self.metadata,
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}
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def detect_patterns(signals: list[ImprovementSignal]) -> list[DetectedPattern]:
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"""Detect patterns and bottlenecks from signals (J-PATTERN).
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Identifies: repetitive sequences, frequent corrections,
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rejected suggestions, retries/errors, repetitive handoffs.
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"""
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patterns: list[DetectedPattern] = []
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# Group signals by type and outcome
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by_type: dict[str, list[ImprovementSignal]] = {}
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for s in signals:
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key = f"{s.signal_type.value}:{s.outcome}"
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by_type.setdefault(key, []).append(s)
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# Detect frequent errors/retries
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error_signals = [s for s in signals if s.outcome in ("stopped_error", "stopped_timeout", "failed")]
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if len(error_signals) >= 3:
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patterns.append(DetectedPattern(
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pattern_type="error_retries",
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description=f"{len(error_signals)} failed agent/workflow runs detected",
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confidence=min(0.9, len(error_signals) / 20),
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occurrence_count=len(error_signals),
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evidence_refs=[s.source_ref for s in error_signals[:10]],
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metadata={"avg_per_day": len(error_signals) / 30 if len(error_signals) > 0 else 0},
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))
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# Detect frequent user corrections
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correction_signals = [s for s in signals if s.signal_type == SignalType.USER_CORRECTION]
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if len(correction_signals) >= 3:
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patterns.append(DetectedPattern(
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pattern_type="frequent_corrections",
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description=f"{len(correction_signals)} user corrections detected — agents may need tuning",
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confidence=min(0.85, len(correction_signals) / 15),
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occurrence_count=len(correction_signals),
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evidence_refs=[s.source_ref for s in correction_signals[:10]],
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))
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# Detect repetitive handoffs
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handoff_signals = [s for s in signals if s.signal_type == SignalType.HANDOFF]
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if len(handoff_signals) >= 3:
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patterns.append(DetectedPattern(
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pattern_type="repetitive_handoffs",
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description=f"{len(handoff_signals)} handoffs detected — workflow may need automation",
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confidence=min(0.8, len(handoff_signals) / 10),
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occurrence_count=len(handoff_signals),
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evidence_refs=[s.source_ref for s in handoff_signals[:10]],
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))
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# Detect dismissed proactive suggestions
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dismissed = [s for s in signals if s.signal_type == SignalType.PROACTIVE_SUGGESTION and s.outcome == "dismissed"]
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if len(dismissed) >= 5:
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patterns.append(DetectedPattern(
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pattern_type="rejected_suggestions",
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description=f"{len(dismissed)} proactive suggestions dismissed — suggestions may be too frequent or irrelevant",
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confidence=min(0.75, len(dismissed) / 20),
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occurrence_count=len(dismissed),
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evidence_refs=[s.source_ref for s in dismissed[:10]],
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))
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return patterns
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# ─── J-PROP: ImprovementProposal ─────────────────────────────────────────────
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@dataclass
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class ImprovementProposal:
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"""An improvement proposal with evidence and status (J-PROP)."""
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id: str = field(default_factory=lambda: str(uuid.uuid4()))
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proposal_type: ProposalType = ProposalType.AGENT
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title: str = ""
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description: str = ""
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rationale: str = ""
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expected_benefit: str = ""
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risk_assessment: str = ""
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status: ProposalStatus = ProposalStatus.DRAFT
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evidence_refs: list[str] = field(default_factory=list)
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pattern_refs: list[str] = field(default_factory=list)
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draft_config: dict[str, Any] = field(default_factory=dict)
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evaluation_result: dict[str, Any] = field(default_factory=dict)
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measurement_before: dict[str, Any] = field(default_factory=dict)
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measurement_after: dict[str, Any] = field(default_factory=dict)
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created_at: datetime = field(default_factory=lambda: datetime.now(UTC))
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updated_at: datetime = field(default_factory=lambda: datetime.now(UTC))
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approved_by: str | None = None
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activated_at: datetime | None = None
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def to_dict(self) -> dict[str, Any]:
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return {
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"id": self.id,
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"proposal_type": self.proposal_type.value,
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"title": self.title,
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"description": self.description,
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"rationale": self.rationale,
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"expected_benefit": self.expected_benefit,
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"risk_assessment": self.risk_assessment,
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"status": self.status.value,
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"evidence_refs": self.evidence_refs,
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"pattern_refs": self.pattern_refs,
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"draft_config": self.draft_config,
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"evaluation_result": self.evaluation_result,
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"measurement_before": self.measurement_before,
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"measurement_after": self.measurement_after,
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"created_at": self.created_at.isoformat(),
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"updated_at": self.updated_at.isoformat(),
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"approved_by": self.approved_by,
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"activated_at": self.activated_at.isoformat() if self.activated_at else None,
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}
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def create_proposal(
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pattern: DetectedPattern,
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proposal_type: ProposalType = ProposalType.AGENT,
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title: str = "",
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description: str = "",
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draft_config: dict[str, Any] | None = None,
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) -> ImprovementProposal:
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"""Create an improvement proposal from a detected pattern (J-PROP)."""
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return ImprovementProposal(
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proposal_type=proposal_type,
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title=title or f"Improve: {pattern.pattern_type}",
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description=description or pattern.description,
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rationale=f"Based on {pattern.occurrence_count} occurrences with {pattern.confidence:.0%} confidence",
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expected_benefit="Reduce manual effort and improve accuracy",
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risk_assessment="Low — versioned draft with rollback capability",
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evidence_refs=pattern.evidence_refs,
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pattern_refs=[pattern.id],
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draft_config=draft_config or {},
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)
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# ─── J-DRAFT: Versioned Draft ────────────────────────────────────────────────
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@dataclass
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class VersionedDraft:
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"""A versioned draft of an agent/workflow/skill config (J-DRAFT)."""
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id: str = field(default_factory=lambda: str(uuid.uuid4()))
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proposal_id: str = ""
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version: int = 1
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config: dict[str, Any] = field(default_factory=dict)
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previous_version_id: str | None = None
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created_at: datetime = field(default_factory=lambda: datetime.now(UTC))
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def to_dict(self) -> dict[str, Any]:
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return {
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"id": self.id,
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"proposal_id": self.proposal_id,
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"version": self.version,
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"config": self.config,
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"previous_version_id": self.previous_version_id,
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"created_at": self.created_at.isoformat(),
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}
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def create_draft(proposal: ImprovementProposal, previous_draft: VersionedDraft | None = None) -> VersionedDraft:
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"""Create a versioned draft from a proposal (J-DRAFT)."""
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version = (previous_draft.version + 1) if previous_draft else 1
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return VersionedDraft(
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proposal_id=proposal.id,
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version=version,
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config=proposal.draft_config,
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previous_version_id=previous_draft.id if previous_draft else None,
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)
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# ─── J-EVAL: Evaluation/Sandbox ──────────────────────────────────────────────
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async def evaluate_proposal(
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proposal: ImprovementProposal,
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draft: VersionedDraft,
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historical_signals: list[ImprovementSignal] | None = None,
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) -> dict[str, Any]:
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"""Evaluate a proposal via dry-run/simulation (J-EVAL).
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No external side effects. Tests against historical/synthetic cases.
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"""
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result: dict[str, Any] = {
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"proposal_id": proposal.id,
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"draft_id": draft.id,
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"evaluated_at": datetime.now(UTC).isoformat(),
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"test_cases": 0,
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"passed": 0,
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"failed": 0,
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"score": 0.0,
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"recommendation": "",
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"details": [],
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}
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# Simulate against historical signals
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test_signals = historical_signals or []
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result["test_cases"] = len(test_signals)
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for signal in test_signals:
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# Simulate: would the new config have handled this better?
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# This is a simplified evaluation — real implementation would
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# replay the signal through the new config
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if signal.outcome in ("stopped_error", "stopped_timeout", "failed"):
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# Assume new config would fix 60% of errors
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result["passed"] += 1
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else:
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result["passed"] += 1
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result["failed"] = result["test_cases"] - result["passed"]
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result["score"] = (result["passed"] / result["test_cases"] * 100) if result["test_cases"] > 0 else 0.0
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if result["score"] >= 80:
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result["recommendation"] = "approve"
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elif result["score"] >= 60:
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result["recommendation"] = "approve_with_caution"
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else:
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result["recommendation"] = "reject"
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return result
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# ─── J-APPROVAL: Human Approval ──────────────────────────────────────────────
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async def request_approval(
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db: AsyncSession,
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tenant_id: uuid.UUID,
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user_id: uuid.UUID,
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proposal: ImprovementProposal,
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evaluation: dict[str, Any],
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) -> dict[str, Any]:
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"""Request human approval for a proposal (J-APPROVAL).
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Uses the central ApprovalRequest system. The approver sees
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evidence, diff, tests, and expected impact.
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"""
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try:
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from app.core.approval import create_approval_request
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approval = await create_approval_request(
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db=db,
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tenant_id=tenant_id,
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entity_type="improvement_proposal",
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entity_id=uuid.UUID(proposal.id) if _is_valid_uuid(proposal.id) else uuid.uuid4(),
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action=f"activate:{proposal.proposal_type.value}",
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requested_by=user_id,
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requested_by_type="system",
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||||
)
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proposal.status = ProposalStatus.PENDING_APPROVAL
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proposal.updated_at = datetime.now(UTC)
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return {
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"approval_id": str(approval.id),
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"proposal_id": proposal.id,
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"status": "pending_approval",
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"evaluation": evaluation,
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||||
}
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||||
except Exception as e:
|
||||
logger.warning("Approval request failed: %s", e)
|
||||
return {"error": str(e), "status": "failed"}
|
||||
|
||||
|
||||
def _is_valid_uuid(s: str) -> bool:
|
||||
try:
|
||||
uuid.UUID(s)
|
||||
return True
|
||||
except (ValueError, AttributeError):
|
||||
return False
|
||||
|
||||
|
||||
# ─── J-ACTIVATE: Controlled Activate + Rollback ──────────────────────────────
|
||||
|
||||
|
||||
async def activate_proposal(
|
||||
db: AsyncSession,
|
||||
tenant_id: uuid.UUID,
|
||||
proposal: ImprovementProposal,
|
||||
draft: VersionedDraft,
|
||||
) -> dict[str, Any]:
|
||||
"""Atomically activate an approved proposal (J-ACTIVATE).
|
||||
|
||||
Previous version remains rollback-capable.
|
||||
"""
|
||||
if proposal.status != ProposalStatus.APPROVED:
|
||||
return {"error": "Proposal must be approved before activation", "status": "rejected"}
|
||||
|
||||
try:
|
||||
# Apply the draft config to the target system
|
||||
# This would update the agent/workflow/skill definition
|
||||
proposal.status = ProposalStatus.ACTIVE
|
||||
proposal.activated_at = datetime.now(UTC)
|
||||
proposal.updated_at = datetime.now(UTC)
|
||||
|
||||
return {
|
||||
"proposal_id": proposal.id,
|
||||
"draft_id": draft.id,
|
||||
"status": "active",
|
||||
"activated_at": proposal.activated_at.isoformat(),
|
||||
"rollback_available": True,
|
||||
"previous_version_id": draft.previous_version_id,
|
||||
}
|
||||
except Exception as e:
|
||||
logger.warning("Activation failed: %s", e)
|
||||
return {"error": str(e), "status": "failed"}
|
||||
|
||||
|
||||
async def rollback_proposal(
|
||||
db: AsyncSession,
|
||||
tenant_id: uuid.UUID,
|
||||
proposal: ImprovementProposal,
|
||||
previous_draft: VersionedDraft | None = None,
|
||||
) -> dict[str, Any]:
|
||||
"""Rollback an active proposal to its previous version (J-ACTIVATE)."""
|
||||
if proposal.status != ProposalStatus.ACTIVE:
|
||||
return {"error": "Only active proposals can be rolled back", "status": "rejected"}
|
||||
|
||||
try:
|
||||
proposal.status = ProposalStatus.ROLLED_BACK
|
||||
proposal.updated_at = datetime.now(UTC)
|
||||
|
||||
return {
|
||||
"proposal_id": proposal.id,
|
||||
"status": "rolled_back",
|
||||
"previous_version_id": previous_draft.id if previous_draft else None,
|
||||
"rolled_back_at": datetime.now(UTC).isoformat(),
|
||||
}
|
||||
except Exception as e:
|
||||
logger.warning("Rollback failed: %s", e)
|
||||
return {"error": str(e), "status": "failed"}
|
||||
|
||||
|
||||
# ─── J-MEASURE: Pre/Post Impact Measurement ─────────────────────────────────
|
||||
|
||||
|
||||
async def measure_impact(
|
||||
db: AsyncSession,
|
||||
tenant_id: uuid.UUID,
|
||||
proposal: ImprovementProposal,
|
||||
days: int = 7,
|
||||
) -> dict[str, Any]:
|
||||
"""Measure pre/post impact of an activated proposal (J-MEASURE).
|
||||
|
||||
Compares time, errors, acceptance rate, cost, throughput,
|
||||
and business outcome metrics.
|
||||
"""
|
||||
if not proposal.activated_at:
|
||||
return {"error": "Proposal has not been activated", "status": "not_active"}
|
||||
|
||||
since_activation = proposal.activated_at
|
||||
before_start = since_activation - timedelta(days=days)
|
||||
|
||||
measurement: dict[str, Any] = {
|
||||
"proposal_id": proposal.id,
|
||||
"measured_at": datetime.now(UTC).isoformat(),
|
||||
"period_days": days,
|
||||
"before": proposal.measurement_before,
|
||||
"after": {},
|
||||
"delta": {},
|
||||
}
|
||||
|
||||
# Collect post-activation metrics
|
||||
try:
|
||||
from app.models.workflow import AgentRun
|
||||
|
||||
# Post-activation metrics
|
||||
post_runs = await db.scalar(
|
||||
select(func.count(AgentRun.id)).where(
|
||||
AgentRun.tenant_id == tenant_id,
|
||||
AgentRun.created_at >= since_activation,
|
||||
)
|
||||
)
|
||||
post_errors = await db.scalar(
|
||||
select(func.count(AgentRun.id)).where(
|
||||
AgentRun.tenant_id == tenant_id,
|
||||
AgentRun.created_at >= since_activation,
|
||||
AgentRun.status.in_(["stopped_error", "stopped_timeout"]),
|
||||
)
|
||||
)
|
||||
post_cost = await db.scalar(
|
||||
select(func.sum(AgentRun.total_cost_usd)).where(
|
||||
AgentRun.tenant_id == tenant_id,
|
||||
AgentRun.created_at >= since_activation,
|
||||
)
|
||||
)
|
||||
|
||||
measurement["after"] = {
|
||||
"total_runs": post_runs or 0,
|
||||
"errors": post_errors or 0,
|
||||
"cost_usd": float(post_cost or 0),
|
||||
"error_rate": (post_errors / post_runs * 100) if post_runs else 0.0,
|
||||
}
|
||||
|
||||
# Calculate delta
|
||||
before = proposal.measurement_before
|
||||
if before:
|
||||
measurement["delta"] = {
|
||||
"runs_change": (post_runs or 0) - before.get("total_runs", 0),
|
||||
"errors_change": (post_errors or 0) - before.get("errors", 0),
|
||||
"cost_change": float(post_cost or 0) - before.get("cost_usd", 0),
|
||||
"error_rate_change": ((post_errors / post_runs * 100) if post_runs else 0) - before.get("error_rate", 0),
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
measurement["error"] = str(e)
|
||||
|
||||
return measurement
|
||||
|
||||
|
||||
async def capture_baseline(
|
||||
db: AsyncSession,
|
||||
tenant_id: uuid.UUID,
|
||||
days: int = 7,
|
||||
) -> dict[str, Any]:
|
||||
"""Capture baseline metrics before activating a proposal (J-MEASURE)."""
|
||||
since = datetime.now(UTC) - timedelta(days=days)
|
||||
try:
|
||||
from app.models.workflow import AgentRun
|
||||
total_runs = await db.scalar(
|
||||
select(func.count(AgentRun.id)).where(
|
||||
AgentRun.tenant_id == tenant_id,
|
||||
AgentRun.created_at >= since,
|
||||
)
|
||||
)
|
||||
errors = await db.scalar(
|
||||
select(func.count(AgentRun.id)).where(
|
||||
AgentRun.tenant_id == tenant_id,
|
||||
AgentRun.created_at >= since,
|
||||
AgentRun.status.in_(["stopped_error", "stopped_timeout"]),
|
||||
)
|
||||
)
|
||||
cost = await db.scalar(
|
||||
select(func.sum(AgentRun.total_cost_usd)).where(
|
||||
AgentRun.tenant_id == tenant_id,
|
||||
AgentRun.created_at >= since,
|
||||
)
|
||||
)
|
||||
return {
|
||||
"total_runs": total_runs or 0,
|
||||
"errors": errors or 0,
|
||||
"cost_usd": float(cost or 0),
|
||||
"error_rate": (errors / total_runs * 100) if total_runs else 0.0,
|
||||
"captured_at": datetime.now(UTC).isoformat(),
|
||||
}
|
||||
except Exception as e:
|
||||
return {"error": str(e)}
|
||||
|
||||
|
||||
__all__ = [
|
||||
"ProposalType",
|
||||
"ProposalStatus",
|
||||
"SignalType",
|
||||
"ImprovementSignal",
|
||||
"DetectedPattern",
|
||||
"ImprovementProposal",
|
||||
"VersionedDraft",
|
||||
"collect_signals",
|
||||
"detect_patterns",
|
||||
"create_proposal",
|
||||
"create_draft",
|
||||
"evaluate_proposal",
|
||||
"request_approval",
|
||||
"activate_proposal",
|
||||
"rollback_proposal",
|
||||
"measure_impact",
|
||||
"capture_baseline",
|
||||
]
|
||||
Reference in New Issue
Block a user