Maestro Adaptive Learning Framework

Maestro Adaptive Learning Framework

Purpose: Platform-level architecture for processing change signals and translating them into safe, measured, reversible changes across the Maestro ecosystem
Version: 1.0
Last Updated: 2026-01-21
Status: Architecture Definition

Executive Summary

The Maestro Adaptive Learning Framework is a unified system architecture for processing signals—both reactive (issues, incidents) and proactive (strategic decisions, governance updates, technology changes)—and translating them into controlled, reversible changes across personas, teams, workflows, and templates.

This framework abstracts the patterns proven in the Issue Log Workflow into a generalized architecture that can handle any type of change signal that should trigger system adaptation.

Core Principle

Every signal that indicates the system should change goes through the same rigorous pipeline: Analysis → Learning Generation → Safety Checks → Controlled Application → Effectiveness Measurement → Auto-Correction

Framework Instantiations

Part 1: Conceptual Architecture

1.1 The Core Engine

Every change signal flows through the same pipeline:

┌─────────────────────────────────────────────────────────────────────────────────────┐
│                        MAESTRO ADAPTIVE LEARNING FRAMEWORK                           │
├─────────────────────────────────────────────────────────────────────────────────────┤
│                                                                                      │
│  ┌─────────────┐    ┌─────────────┐    ┌─────────────┐    ┌─────────────┐          │
│  │   SIGNAL    │    │  ANALYSIS   │    │  LEARNING   │    │   SAFETY    │          │
│  │  INGESTION  │───►│   ENGINE    │───►│ GENERATION  │───►│   LAYER     │          │
│  └─────────────┘    └─────────────┘    └─────────────┘    └──────┬──────┘          │
│        ▲                                                         │                  │
│        │                                                         ▼                  │
│  ┌─────┴─────┐                                           ┌─────────────┐           │
│  │  SOURCES  │                                           │ APPLICATION │           │
│  │           │                                           │   ENGINE    │           │
│  │ • Issues  │                                           └──────┬──────┘           │
│  │ • Intent  │                                                  │                  │
│  │ • Gov     │    ┌─────────────┐    ┌─────────────┐           │                  │
│  │ • Tech    │    │EFFECTIVENESS│◄───│  LEARNING   │◄──────────┘                  │
│  │ • Perf    │    │  TRACKING   │    │  TARGETS    │                              │
│  │ • Market  │    └──────┬──────┘    └─────────────┘                              │
│  └───────────┘           │                  │                                      │
│                          │           ┌──────┴──────┐                               │
│                          │           │  • Persona  │                               │
│                          │           │  • Team     │                               │
│                          │           │  • Workflow │                               │
│                          │           │  • Template │                               │
│                          ▼           └─────────────┘                               │
│                   ┌─────────────┐                                                   │
│                   │AUTO-ROLLBACK│ (if effectiveness < threshold)                   │
│                   └─────────────┘                                                   │
│                                                                                      │
└─────────────────────────────────────────────────────────────────────────────────────┘

1.2 Pipeline Stages

Part 2: Signal Taxonomy

2.1 Signal Categories

Signals are classified into Reactive (responding to problems) and Proactive (responding to opportunities/decisions):

                            CHANGE SIGNALS
                                  │
                 ┌────────────────┴────────────────┐
                 ▼                                 ▼
            REACTIVE                          PROACTIVE
        (Problem-Driven)                  (Opportunity-Driven)
                 │                                 │
    ┌────────────┼────────────┐       ┌───────────┼───────────┐
    ▼            ▼            ▼       ▼           ▼           ▼
  Issue      Incident     Quality   Intent   Governance  Technology
   Bug       Outage      Degradation Change    Update     Evolution
    │           │            │         │          │           │
    └───────────┴────────────┴─────────┴──────────┴───────────┘
                                  │
                                  ▼
                    UNIFIED LEARNING PIPELINE

2.2 Complete Signal Type Reference

2.3 Signal Data Model (Abstract)

class ChangeSignal:
    """Abstract base for all change signals"""
    id: UUID
    signal_type: SignalType          # BUG, INTENT_CHANGE, GOVERNANCE_UPDATE, etc.
    signal_class: SignalClass        # REACTIVE or PROACTIVE

    # Source identification
    source: SignalSource             # JIRA, PAGERDUTY, MANUAL, STRATEGIC_PLANNING, etc.
    external_id: Optional[str]       # External system reference

    # Content
    title: str
    description: str
    context: Dict[str, Any]          # Signal-specific context

    # Impact assessment
    priority: SignalPriority         # Severity OR Impact level
    affected_targets: List[Target]   # Personas, Teams, Workflows, Templates

    # Analysis results
    analysis: SignalAnalysis         # Root cause OR change driver
    mitigating_factors: List[Factor] # What's working well (Safety-II)

    # Lifecycle
    status: SignalStatus
    created_at: datetime
    analyzed_at: Optional[datetime]
    resolved_at: Optional[datetime]

    # Learning outcomes
    learnings: List[Learning]

Part 3: Analysis Framework

3.1 Analysis Modes

Different signal types require different analysis approaches:

3.2 Root Cause Categories (Reactive Signals)

For reactive signals (issues, incidents), identify WHY it failed:

3.3 Change Driver Categories (Proactive Signals)

For proactive signals, identify WHY we should change:

3.4 Safety-II: Mitigating Factors

For ALL signal types, capture what's working well:

Part 4: Learning Actions

4.1 Learning Action Types

Learnings translate analysis into specific, actionable changes:

4.2 Learning Cascade Order

Learnings are applied in a specific order to ensure proper propagation:

LEARNING CASCADE

Level 1: PERSONA_DIRECT
    │     (Personas directly involved in the signal)
    ▼
Level 2: PERSONA_INDIRECT
    │     (Related personas that should learn)
    ▼
Level 3: TEAM
    │     (Teams affected by the signal)
    ▼
Level 4: WORKFLOW
    │     (Workflows/processes affected)
    ▼
Level 5: TEMPLATE
          (Templates/patterns affected)

4.3 Learning Data Model

class Learning:
    """A specific change to be applied"""
    id: UUID
    signal_id: UUID                  # Parent signal

    # What to change
    action_type: LearningActionType  # TRAIT_ADJUSTMENT, RAG_KNOWLEDGE_ADD, etc.
    target_type: LearningTargetType  # PERSONA, TEAM, WORKFLOW, TEMPLATE
    target_id: str                   # Specific target identifier

    # Change details
    change_specification: Dict       # Action-specific change details
    confidence_score: float          # 0.0-1.0 confidence in this learning

    # Approval
    requires_approval: bool
    approval_status: ApprovalStatus  # PENDING, APPROVED, REJECTED
    approved_by: Optional[str]
    approved_at: Optional[datetime]

    # Application
    applied: bool
    applied_at: Optional[datetime]
    rollback_state: Optional[Dict]   # State before change (for rollback)

    # Effectiveness
    effectiveness_score: Optional[float]  # 0.0-1.0 after evaluation
    evaluated_at: Optional[datetime]
    rolled_back: bool
    rollback_reason: Optional[str]

Part 5: Safety Architecture

5.1 Safety Principles

1. Bounded Change: No single signal can make unbounded changes
2. Cooldown Periods: Targets have recovery time between changes
3. Circuit Breaking: Stop if changes consistently fail
4. Auto-Rollback: Ineffective changes automatically revert
5. Human Oversight: High-impact changes require approval
6. Full Auditability: Every change is traceable and reversible

5.2 Over-Correction Guard

Prevents excessive changes from any single signal or over time:

OVER-CORRECTION LIMITS (Configurable)

Per Signal:
├─ MAX_LEARNINGS_PER_SIGNAL = 10
├─ MAX_SINGLE_CHANGE_MAGNITUDE = 20%
└─ MAX_TARGETS_PER_SIGNAL = 5

Per Target (Daily):
├─ MAX_LEARNINGS_PER_PERSONA_PER_DAY = 5
├─ MAX_LEARNINGS_PER_TEAM_PER_DAY = 3
└─ MAX_LEARNINGS_PER_WORKFLOW_PER_DAY = 3

Per Target (Weekly Cumulative):
├─ MAX_CUMULATIVE_CHANGE_PER_PERSONA = 50%
├─ MAX_CUMULATIVE_CHANGE_PER_TEAM = 30%
└─ MAX_CUMULATIVE_CHANGE_PER_WORKFLOW = 30%

Cooldown Periods:
├─ PERSONA_COOLDOWN_HOURS = 4
├─ TEAM_COOLDOWN_HOURS = 12
└─ WORKFLOW_COOLDOWN_HOURS = 24

5.3 Circuit Breaker

Prevents cascading failures when learnings consistently fail:

CIRCUIT BREAKER STATE MACHINE

    ┌──────────────────────────────────────────┐
    │                                          │
    ▼                                          │
┌────────┐  failure_count >= threshold   ┌─────┴────┐
│ CLOSED │ ─────────────────────────────►│   OPEN   │
│        │                               │          │
└────────┘                               └────┬─────┘
    ▲                                         │
    │                                   cooldown expired
    │       success                           │
    │  ◄────────────────────             ┌────▼─────┐
    │                       │            │HALF-OPEN │
    └───────────────────────┴────────────│          │
              failure                    └──────────┘

Configuration:
├─ failure_threshold: 5      # failures to open
├─ window_hours: 24          # counting window
├─ cooldown_hours: 6         # time before half-open
└─ effectiveness_threshold: 0.3  # below = failure

5.4 Effectiveness Tracking

Measures whether learnings achieved their goals:

EFFECTIVENESS EVALUATION

Timeline:
├─ Learning Applied ──► Evaluation Window (14 days) ──► Evaluation
│
└─ Metrics Collected:
   ├─ Issue/Signal recurrence rate
   ├─ Target performance metrics
   ├─ Related quality indicators
   └─ User feedback signals

Scoring:
├─ 0.7 - 1.0: EFFECTIVE     → Retain, reinforce pattern
├─ 0.3 - 0.7: MARGINAL      → Retain, continue monitoring
└─ 0.0 - 0.3: INEFFECTIVE   → AUTO-ROLLBACK triggered

5.5 Approval Matrix

Determines whether human approval is required:

Part 6: Implementation Patterns

6.1 Signal Source Adapters

Each signal source has an adapter that normalizes signals:

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│  JIRA Adapter   │     │PagerDuty Adapter│     │ Manual Entry    │
│                 │     │                 │     │                 │
│ Webhook ──────► │     │ Webhook ──────► │     │ UI/API ───────► │
│                 │     │                 │     │                 │
│ Maps to:        │     │ Maps to:        │     │ Maps to:        │
│ BUG, INCIDENT   │     │ INCIDENT        │     │ Any SignalType  │
└────────┬────────┘     └────────┬────────┘     └────────┬────────┘
         │                       │                       │
         └───────────────────────┴───────────────────────┘
                                 │
                                 ▼
                      ┌─────────────────────┐
                      │  Signal Ingestion   │
                      │      Service        │
                      └─────────────────────┘

6.2 Learning Applicators

Each target type has a specialized applicator:

class LearningApplicator(ABC):
    """Base class for applying learnings to targets"""

    @abstractmethod
    async def can_apply(self, learning: Learning) -> ApplicabilityResult:
        """Check if learning can be applied (safety checks)"""
        pass

    @abstractmethod
    async def apply(self, learning: Learning) -> ApplicationResult:
        """Apply the learning, storing rollback state"""
        pass

    @abstractmethod
    async def rollback(self, learning: Learning) -> RollbackResult:
        """Revert the learning using stored rollback state"""
        pass

    @abstractmethod
    async def evaluate_effectiveness(self, learning: Learning) -> float:
        """Evaluate effectiveness after evaluation window"""
        pass

6.3 EventBus Integration

Signals and learnings publish events for cross-system awareness:

Part 7: Instantiation Guide

7.1 Creating a New Workflow Instantiation

To create a new workflow (e.g., "Intent Change Workflow"):

Step 1: Define Signal Types
├─ What signals will this workflow process?
├─ What sources will provide these signals?
└─ What priority model applies?

Step 2: Define Analysis Mode
├─ Root Cause Analysis OR Change Driver Analysis?
├─ What categories apply?
└─ What analysis methods (AI, rule-based, human)?

Step 3: Define Learning Actions
├─ What changes can this workflow make?
├─ What targets are affected?
└─ What approval rules apply?

Step 4: Configure Safety Parameters
├─ Over-correction limits (may differ from defaults)
├─ Circuit breaker thresholds
├─ Effectiveness evaluation criteria

Step 5: Implement Adapters
├─ Source adapters for signal ingestion
├─ Target applicators (or reuse existing)
└─ EventBus event definitions

Step 6: Create Documentation
├─ Workflow-specific Confluence page
├─ Link to this framework document
└─ Specific SLOs and metrics

7.2 Existing Instantiation: Issue Log Workflow

7.3 Planned Instantiation: Intent Change Workflow

7.4 Planned Instantiation: Governance Change Workflow

7.5 Planned Instantiation: Technology Evolution Workflow

Part 8: Metrics and SLOs

8.1 Framework-Level Metrics

8.2 Per-Instantiation SLOs

Each workflow instantiation defines its own SLOs based on:

• Signal criticality
• Business impact
• Regulatory requirements

Part 9: Governance

9.1 Framework Ownership

9.2 Change Management

Changes to the framework itself follow a structured process:

1. Proposal: Document proposed change and rationale
2. Impact Assessment: Evaluate impact on existing instantiations
3. Review: Architecture review board approval
4. Implementation: Staged rollout with monitoring
5. Documentation: Update this document and affected workflows

9.3 Audit Requirements

All framework operations are auditable:

Part 10: Related Documentation

Appendix A: Glossary

Appendix B: Change Log

This is a living document. As new workflow instantiations are created, patterns and learnings should be fed back into this framework.