How FDRP Works

Manufacturing quality control for planning decisions. Statistical process control, 5S workplace organization, RAMS reliability analysis, Andon stop-the-line signals, and configuration freeze — all applied to the planning process itself.

Overview

FDRP treats every planning decision as a manufactured artifact. Just as a machined part passes through quality gates with measured tolerances, each decision in FDRP passes through a six-phase lifecycle with quantified convergence metrics. When a decision fails its gate criteria, the system triggers an Andon stop — halting forward progress until the root cause is resolved.

The architecture draws on six decades of manufacturing discipline: SPC control charts track decision stability over time, 5S organization classifies and structures the decision workspace, RAMS (Reliability, Availability, Maintainability, Safety) quantifies system dependability, and configuration freeze prevents uncontrolled changes after convergence. Cross-model verification with N≥3 independent LLMs provides the final quality assurance layer.

The result is a planning system with the same measurability guarantees that manufacturing achieved in the 1960s — applied to decisions rather than physical parts.

Convergence Velocity Tensor

The CVT is the core metric that drives FDRP phase transitions. It quantifies how rapidly a planning run is converging toward a stable configuration by measuring three independent signals: domain exploration saturation, expert agreement, and contradiction resolution.

CVT = (1 − new_domain_ratio) × avg_confidence × (1 − contradiction_rate)
Convergence Velocity Tensor

Variables

new_domain_ratio

Fraction of domains in the current wave that did not appear in any previous wave. Approaches 0 as exploration saturates.

avg_confidence

Mean confidence score across all expert findings in the current wave. Range [0, 1]. Higher values indicate stronger expert agreement.

contradiction_rate

Fraction of findings where two or more experts produced contradictory conclusions. Approaches 0 as conflicts are resolved.

Phase Thresholds

CVT < 0.3
Seed
High exploration. Many new domains, low consensus, active contradictions.
0.3 ≤ CVT < 0.6
Growing
Expansion plus refinement. Domain coverage stabilizing, contradictions decreasing.
0.6 ≤ CVT < 0.8
Mature
Refinement dominant. Few new domains, high confidence, minimal contradictions.
CVT ≥ 0.8
Converged
Ready for configuration freeze. Stable baseline, verified by cross-model consensus.

Six-Phase Gate Lifecycle

Every FDRP run progresses through six phases. Transitions are governed by the CVT metric and explicit gate criteria. Phases enforce monotonic convergence — decisions can only tighten, never regress. A regression triggers an Andon stop and root cause analysis.

Phase 1
Seed

Initial problem decomposition. The system analyzes the brief, identifies relevant domains, and nominates the first wave of expert specialists. High exploration, low convergence.

Phase 2
Growing

Wave dispatch and parallel expert analysis. Each wave expands coverage through blind spot detection — experts nominate domains the current team cannot cover. The spiral-out pattern ensures no critical perspective is missed.

Phase 3
Mature

Cross-model verification and contradiction resolution. N≥3 independent LLMs review HIGH and CRITICAL findings. Contradictions are surfaced and resolved through structured debate. CVT tracks convergence toward stability.

Phase 4
Converged

Configuration freeze candidate. CVT ≥ 0.8 and cross-model verification passes. The system generates a frozen baseline proposal for human review. All findings are traceable to their originating expert and wave.

Phase 5
Frozen

Locked baseline. The configuration is frozen after human approval. Any change requires formal review: the proposer must demonstrate that the change improves convergence without regressing other metrics. Change control is strict.

Phase 6
Released

Published artifact. The frozen baseline is released as a versioned deliverable. A retraction and correction protocol remains active indefinitely — errors discovered post-release trigger formal correction notices with full provenance.

Framework Comparison

FDRP occupies a different design point from existing multi-agent frameworks. Where most frameworks focus on task orchestration, FDRP focuses on decision quality measurement and convergence guarantees.

Feature FDRP AutoGPT CrewAI LangGraph MAPE-K
Convergence Metrics CVT + SPC charts None None None Partial (feedback loop)
Traceability Finding-level provenance None Task-level logs State snapshots Knowledge base
Clash Detection Cross-expert contradiction None None None None
Grounding Discipline VL-0 to VL-4 levels None None None None
Self-Evolution Lessons-learned daemon Plugin system None None Adaptation loop
Cross-Model Verification N≥3 models, expert-framed Single model Single model Single model Not applicable
SPC Integration Control charts, drift detection None None None Monitor phase
Expert Expansion Spiral-out blind spot detection Agent spawning Crew assignment Fixed graph Fixed architecture
Native support
Partial support
Absent

Explore the Methodology

Core Algorithms

Pseudocode for CVT computation, gate progression, wave dispatch (spiral-out), and cross-model verification. Copy-ready reference implementations.

View pseudocode →

Full Paper

The complete FDRP research paper with formal definitions, production data from 58 runs, and the antimatter building case study.

Read the paper →

Live Dashboard

Real-time CVT tracking, SPC control charts, and phase progression across active FDRP runs. Production metrics updated continuously.

View dashboard →