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BIO-QMS Work Order System — Research Follow-Up Prompts

Classification: Internal — Research Pipeline Phase 3 Date: 2026-02-16 Artifact: 80 of WO System Series Purpose: 22 categorized follow-up prompts for deep-dive exploration of the BIO-QMS Work Order Management System Method: Each prompt is self-contained with CODITECT context, expected output format, and product value statement Generated by: /research-pipeline Phase 3 (Ideation)

Category 1: Architecture Deep-Dives​

1.1 Architecture: DAG Dependency Engine Scaling​

Context: CODITECT BIO-QMS uses a two-level Master/Linked WO hierarchy where linked WOs form a Directed Acyclic Graph (DAG). ADR-002 specifies in-process DAG validation over external workflow engines (Temporal, Airflow).

Question: At what scale does the in-process DAG management approach break down, and what is the migration path to a distributed DAG engine that preserves the auditability and determinism requirements of FDA 21 CFR Part 11?

Expected Output: Technical analysis with: (1) load test projections for 100/1K/10K concurrent WOs, (2) PostgreSQL advisory lock contention modeling, (3) migration architecture to distributed DAG with backward compatibility, (4) ADR draft for the scale-out decision.

CODITECT Value: Determines the ceiling of the current architecture and prevents a costly rewrite by designing the scale-out path now.

1.2 Architecture: Event Sourcing vs. State-Based Persistence​

Context: ADR-003 chose PostgreSQL with append-only audit trail and transactional outbox for event publishing. The current design stores mutable WO state plus immutable audit events. Full event sourcing was considered but rejected for V1.

Question: What would a hybrid architecture look like that uses event sourcing for the WO lifecycle (enabling temporal queries, audit replay, and point-in-time reconstruction) while keeping the current state-based read model for operational queries?

Expected Output: (1) C3-level component diagram showing event store + projection + read model, (2) CQRS integration pattern with existing REST API, (3) compliance impact analysis (does event replay satisfy FDA Part 11 audit requirements?), (4) migration strategy from current schema.

CODITECT Value: Event sourcing enables regulatory audit replay ("show me the exact state of WO-1234 at 2026-01-15T14:30:00Z") which is a high-value compliance feature for FDA inspections.

1.3 Architecture: Multi-Region Active-Active for WO State​

Context: CODITECT targets healthcare and life sciences organizations that may operate across multiple geographic regions with data residency requirements (EU MDR, Brazil LGPD). The current PostgreSQL-based architecture assumes a single-region deployment.

Question: How should the WO state store be architected for active-active multi-region deployment while maintaining strict consistency for approval workflows and audit trails?

Expected Output: (1) Comparison of Citus distributed PostgreSQL vs. CockroachDB vs. Spanner for WO workload, (2) conflict resolution strategy for concurrent WO mutations across regions, (3) data residency compliance mapping per regulation, (4) latency impact on approval SLAs.

CODITECT Value: Multi-region is a prerequisite for enterprise healthcare customers with global operations — this analysis de-risks the architectural decision before customer commitments.

1.4 Architecture: Job Plan Template Engine Design​

Context: WO Job Plans contain structured execution procedures including step sequences, tool requirements, credential references, and acceptance criteria. The TDD specifies Job Plans as JSONB fields, but tenants need customizable templates.

Question: What template engine architecture best supports: (1) tenant-specific job plan templates with version control, (2) parameterized steps with conditional logic (if equipment_type = X, use steps A-D; else steps E-G), (3) template inheritance for regulatory standards (ISO 13485 base template → tenant customizations)?

Expected Output: SDD-level design with: schema for template definitions, rendering engine options (JSON Schema + conditional logic vs. lightweight DSL), version control and approval workflow for template changes, integration with the WO creation API.

CODITECT Value: Template-driven job plans reduce WO creation time by 60-80% for recurring maintenance procedures — direct productivity gain for customers.

1.5 Architecture: Real-Time WO Dashboard WebSocket Architecture​

Context: Operations managers need real-time visibility into WO status across facilities. The current event bus (NATS) publishes WO lifecycle events, but there's no specification for how these reach browser-based dashboards.

Question: Design the real-time WO dashboard architecture from NATS event bus to browser WebSocket, including: tenant-isolated event streams, authorization for dashboard subscriptions, reconnection handling, and event replay for late-joining clients.

Expected Output: (1) Sequence diagram: NATS → WebSocket Gateway → Browser, (2) Authorization model for dashboard subscriptions (RBAC integration), (3) Event buffering and replay strategy, (4) Scaling analysis for 100/1K concurrent dashboard sessions.

CODITECT Value: Real-time dashboards are table-stakes for operations management — this design enables the "command center" view that operations managers expect.

Category 2: Compliance & Regulatory​

2.1 Compliance: FDA 21 CFR Part 11 Validation Protocol​

Context: CODITECT BIO-QMS must undergo computer system validation (CSV) before deployment in FDA-regulated environments. The system includes electronic signatures (ADR-007), audit trails (ADR-003), and role-based access control (RBAC model documented in 21-rbac-model.md).

Question: Produce a complete IQ/OQ/PQ (Installation Qualification / Operational Qualification / Performance Qualification) protocol template specific to the WO Management System, with test cases that map directly to the 14 Part 11 requirements.

Expected Output: (1) IQ protocol with acceptance criteria for each deployed component, (2) OQ protocol with 50+ test cases covering all Part 11 requirements mapped to WO system features, (3) PQ protocol with operational scenarios using production-like data, (4) Traceability matrix: Part 11 requirement → system feature → test case → evidence.

CODITECT Value: Pre-built validation protocols reduce customer deployment time by 3-6 months — a major competitive differentiator in regulated markets.

2.2 Compliance: HIPAA Minimum Necessary Standard for WO Data​

Context: WO Job Plans may reference or include Protected Health Information (PHI) when managing equipment in clinical environments (e.g., "Calibrate MRI in Radiology Suite B, patient schedule attached"). HIPAA's Minimum Necessary Standard requires limiting PHI exposure to what's needed for the task.

Question: How should the WO system enforce the HIPAA Minimum Necessary Standard across the WO lifecycle — from job plan creation (preventing unnecessary PHI inclusion) through execution (limiting field visibility based on role) to archival (PHI redaction in long-term audit records)?

Expected Output: (1) Data flow diagram showing PHI touchpoints across WO lifecycle, (2) PHI detection rules for job plan content, (3) Dynamic field masking specification per RBAC role, (4) Archival redaction strategy with reversibility for legal hold, (5) ADR draft for PHI handling in WOs.

CODITECT Value: HIPAA compliance for WO data handling is a hard requirement for any healthcare deployment — this analysis closes a gap not addressed in the current ADRs.

2.3 Compliance: EU MDR and IVDR Impact on WO System​

Context: European Medical Device Regulation (MDR 2017/745) and In Vitro Diagnostic Regulation (IVDR 2017/746) impose specific requirements on quality management systems used for medical device lifecycle management. These differ from FDA requirements in several areas.

Question: What modifications or extensions does the current WO system architecture need to comply with EU MDR/IVDR requirements, specifically: (1) Unique Device Identification (UDI) integration in WO asset records, (2) Post-Market Surveillance (PMS) data collection from WO outcomes, (3) EU Authorized Representative notification workflows?

Expected Output: Gap analysis table: MDR/IVDR requirement → current WO system coverage → gap → remediation effort. ADR draft for EU regulatory extensions. Impact on existing data model (Prisma schema changes).

CODITECT Value: EU market access requires MDR/IVDR compliance — this analysis opens the European bioscience market ($47B TAM) for CODITECT BIO-QMS.

2.4 Compliance: SOC 2 Type II Evidence Collection Automation​

Context: CODITECT BIO-QMS generates extensive audit data (append-only audit trail, e-signatures, RBAC enforcement logs). SOC 2 Type II audits require evidence of control effectiveness over a 6-12 month observation period.

Question: Design an automated evidence collection system that continuously extracts SOC 2 control evidence from the WO system's operational data, packages it for auditor review, and generates gap alerts when controls show degradation.

Expected Output: (1) Mapping of SOC 2 Trust Service Criteria to WO system controls, (2) Evidence collection pipeline architecture (what data, where stored, how packaged), (3) Dashboard specification for continuous compliance monitoring, (4) Auditor portal wireframes for evidence review.

CODITECT Value: Automated SOC 2 evidence collection transforms compliance from a painful annual exercise into a continuous assurance capability — a key selling point for enterprise customers.

Category 3: Multi-Agent Orchestration​

3.1 Orchestration: Agent-Initiated WO Creation Patterns​

Context: CODITECT agents can autonomously create WOs for system changes. The agent orchestration spec (25-agent-orchestration-spec.md) defines 6 specialized agents. The key challenge is ensuring agent-initiated WOs have the same compliance rigor as human-initiated ones.

Question: Define the complete interaction protocol for agent-initiated WO creation, including: (1) how an agent determines that a WO is needed (trigger conditions), (2) the approval workflow for agent-proposed WOs (human-in-the-loop gates), (3) rollback semantics if an agent's WO execution fails partway through, (4) audit attribution (recording that an agent, not a human, initiated the change).

Expected Output: Sequence diagrams for 3 scenarios: (a) agent proposes + human approves + agent executes, (b) agent proposes + auto-approved by policy + agent executes, (c) agent execution fails mid-WO + rollback. Message contract specifications for agent-WO API. ADR for agent attribution in audit trails.

CODITECT Value: Agent-initiated WOs are CODITECT's key differentiator — this protocol ensures the autonomous capability doesn't compromise regulatory compliance.

3.2 Orchestration: Circuit Breaker Patterns for WO Agent Workers​

Context: The agent orchestration architecture uses specialized workers (WO Orchestrator, CAPA Investigator, QA Reviewer, Experience Matcher, Vendor Coordinator, Documentation Agent). Worker failures must not cascade.

Question: Design the circuit breaker implementation for WO agent workers, including: (1) failure detection thresholds per worker type, (2) degraded mode behavior (what happens when the Resource Matcher is down? what about the QA Reviewer?), (3) recovery protocols, (4) observability integration for circuit breaker state.

Expected Output: (1) State machine for each worker's circuit breaker (closed/open/half-open transitions), (2) Degraded mode matrix: which workers can be bypassed vs. which are hard requirements, (3) NATS message patterns for health signaling, (4) Grafana dashboard specification for circuit breaker monitoring.

CODITECT Value: Resilience engineering ensures WO processing continues even during partial system failures — critical for 24/7 bioscience operations.

3.3 Orchestration: Token Budget Management for WO Agent Pipelines​

Context: CODITECT's model routing delivers 40-60% token cost reduction through intelligent model selection. The WO agent pipeline involves multiple LLM calls: CAPA investigation, QA review, documentation generation, experience matching.

Question: Design the token budget allocation and enforcement system for WO agent pipelines, including: (1) per-WO-type budget templates (simple maintenance WO vs. complex validation WO), (2) dynamic reallocation when a sub-agent exhausts its budget, (3) cost tracking and tenant-level billing integration, (4) model routing overrides for compliance-critical steps.

Expected Output: (1) Budget allocation table per WO type and agent role, (2) Dynamic reallocation algorithm, (3) Cost tracking schema (extends existing WO data model), (4) Tenant billing integration specification, (5) Model routing decision tree for WO steps.

CODITECT Value: Token economics directly impacts per-WO cost and customer pricing — this design ensures profitability at scale.

3.4 Orchestration: Multi-Agent Consensus for Complex WO Decisions​

Context: Complex WOs (e.g., upgrading a validated production system) may require input from multiple specialized agents: risk assessment, compliance validation, resource optimization, and schedule impact analysis. These agents may produce conflicting recommendations.

Question: Design a consensus mechanism for multi-agent WO decisions, including: (1) voting/weighting scheme for agent recommendations, (2) conflict resolution protocol when agents disagree, (3) escalation to human decision-maker with structured context, (4) audit trail for the decision synthesis process.

Expected Output: (1) Consensus algorithm specification (weighted voting with compliance veto), (2) Conflict resolution flowchart, (3) Human escalation interface specification (what context does the human see?), (4) ADR for multi-agent consensus approach.

CODITECT Value: Multi-agent consensus enables confident autonomous decision-making for complex WOs — reducing human intervention while maintaining compliance.

Category 4: Competitive & Market Intelligence​

4.1 Competitive: Feature-by-Feature Comparison with Blue Mountain RAM​

Context: Blue Mountain RAM (Regulatory Asset Manager) is the incumbent CMMS/EAM solution in life sciences. CODITECT BIO-QMS positions as the AI-first alternative. The competitive landscape analysis (docs/market/) covers market sizing but lacks detailed feature comparison.

Question: Produce a feature-by-feature comparison of CODITECT BIO-QMS vs. Blue Mountain RAM across these dimensions: (1) work order management capabilities, (2) compliance automation, (3) AI/agent capabilities, (4) multi-tenancy and cloud architecture, (5) integration ecosystem, (6) pricing model.

Expected Output: Competitive comparison matrix (table with 50+ features, red/yellow/green ratings for each product). Battle card for sales team. Win/loss talking points for each capability gap.

CODITECT Value: Sales enablement — the team needs detailed competitive intelligence for every prospect conversation where Blue Mountain is the incumbent.

4.2 Competitive: Market Entry Timing Analysis​

Context: The bioscience QMS market is undergoing a generational shift from on-premise CMMS (Maximo, SAP PM, Blue Mountain) to cloud-native platforms. FDA's 2024 guidance on AI/ML in drug manufacturing signals regulatory acceptance of AI-driven quality systems.

Question: Analyze the optimal market entry timing for CODITECT BIO-QMS, considering: (1) regulatory readiness (FDA, EU MDR/IVDR acceptance of AI-assisted QMS), (2) competitive landscape evolution (who else is entering?), (3) customer readiness (enterprise bioscience adoption curve for cloud QMS), (4) technology maturity (are the AI capabilities production-ready?).

Expected Output: (1) Market timing framework with decision criteria, (2) Competitor entry timeline (who is building what, when), (3) Customer adoption curve analysis, (4) Go/No-Go recommendation with conditions.

CODITECT Value: Timing determines whether CODITECT captures the cloud QMS wave or arrives after market consolidation.

4.3 Competitive: Open-Source QMS Threat Assessment​

Context: Several open-source QMS projects are gaining traction (OpenQMS, QualityForge, FreeCMMS). While none currently target FDA-regulated bioscience, the pattern of open-source disruption (Linux, Kubernetes, Terraform) suggests this could change.

Question: Assess the threat of open-source QMS solutions to CODITECT BIO-QMS, including: (1) current capability gap between open-source and CODITECT, (2) trajectory analysis (where will open-source be in 2-3 years?), (3) defensible moats (what's hard to replicate in open-source?), (4) potential response strategies (contribute to open-source, acquire, ignore).

Expected Output: Threat assessment matrix with probability/impact ratings. Strategic response recommendation with 3 scenario plans.

CODITECT Value: Proactive competitive strategy prevents disruption from below — the most dangerous competitive threat is the one you don't see coming.

Category 5: Product Feature Extraction​

5.1 Product: Predictive Maintenance Integration​

Context: The WO system manages reactive maintenance (fix when broken) and preventive maintenance (fix on schedule). Predictive maintenance (fix before failure, based on sensor data) is the next evolution and a key value driver for bioscience customers.

Question: Design the integration architecture for predictive maintenance within the WO system, including: (1) sensor data ingestion pipeline (IoT → CODITECT), (2) ML model for failure prediction (input features, model type, training data requirements), (3) automatic WO creation from predictions, (4) prediction confidence thresholds for auto-creation vs. human review.

Expected Output: (1) Integration architecture diagram (sensor → edge → cloud → WO), (2) ML model specification with feature engineering, (3) WO creation policy engine specification, (4) ROI model: predictive vs. preventive maintenance cost savings, (5) ADR for predictive maintenance architecture.

CODITECT Value: Predictive maintenance is a $10B+ market — integrating it into the WO system makes CODITECT BIO-QMS a platform, not just a QMS.

5.2 Product: Digital Twin Integration for Equipment WOs​

Context: Digital twins — virtual replicas of physical equipment — are emerging in bioscience manufacturing. Equipment WOs could be enhanced with digital twin context: current sensor readings, maintenance history overlay, and simulation of proposed changes before execution.

Question: How should the WO system integrate with digital twin platforms (Azure Digital Twins, AWS IoT TwinMaker, NVIDIA Omniverse) to provide contextual equipment data during WO execution?

Expected Output: (1) Integration patterns for 3 digital twin platforms, (2) WO UI specification showing twin data alongside job plan steps, (3) Simulation-before-execution workflow (run the proposed change on the twin first), (4) Data model extensions for twin references.

CODITECT Value: Digital twin integration positions CODITECT at the intersection of Industry 4.0 and regulatory compliance — a unique market position.

5.3 Product: Mobile-First WO Execution Interface​

Context: Field technicians execute WOs on lab floors and manufacturing areas where desktop access is limited. The current architecture specifies REST APIs but no mobile-specific design.

Question: Design the mobile-first WO execution experience, including: (1) offline-capable architecture for areas with poor connectivity, (2) barcode/QR scanning for asset identification, (3) photo/video evidence capture integrated into job plan steps, (4) e-signature capture on mobile devices, (5) synchronization protocol for offline WO updates.

Expected Output: (1) Mobile architecture diagram (offline-first with sync), (2) UI wireframes for 5 key screens (WO list, job plan execution, evidence capture, signature, sync status), (3) Sync conflict resolution strategy, (4) Technology stack recommendation (React Native vs. Flutter vs. PWA).

CODITECT Value: Mobile execution is a must-have for field technicians — without it, WO completion rates drop and compliance data quality degrades.

5.4 Product: WO-Driven Training Management​

Context: Regulated environments require that personnel executing WOs have current training certifications. The Experience Matcher agent validates qualifications, but there's no feedback loop to training systems when skill gaps are identified.

Question: Design the integration between the WO system and a training management module, including: (1) automatic training gap detection during WO assignment, (2) training recommendation engine based on WO execution patterns, (3) certification expiry alerting integrated into WO scheduling, (4) training completion as a WO prerequisite gate.

Expected Output: (1) Integration architecture with existing WO lifecycle, (2) Training gap detection algorithm, (3) API specifications for LMS integration (SCORM/xAPI), (4) Dashboard specification for training compliance by department.

CODITECT Value: Training management is a compliance requirement in GxP environments — building it into the WO workflow creates a sticky, high-value feature.

Category 6: Risk & Mitigation​

6.1 Risk: Vendor Lock-In Assessment for Core Dependencies​

Context: CODITECT BIO-QMS depends on: PostgreSQL (state store), NATS (event bus), HashiCorp Vault (credentials), GCP Cloud Run (deployment). Each dependency creates a lock-in risk.

Question: Assess the vendor lock-in risk for each core dependency, including: (1) switching cost analysis (effort to replace each component), (2) abstraction layer design that minimizes lock-in, (3) multi-cloud deployment strategy, (4) contractual risk mitigation (licensing, pricing lock).

Expected Output: Risk matrix: dependency × lock-in severity × switching cost × mitigation strategy. Abstraction layer architecture (repository pattern for state, interface for messaging, secret provider interface). ADR for multi-cloud strategy.

CODITECT Value: Enterprise customers in regulated industries demand deployment flexibility — lock-in to a single cloud provider is a deal-breaker for many procurement teams.

6.2 Risk: Data Breach Impact Analysis for WO System​

Context: The WO system stores sensitive data: personnel records, equipment configurations, credential references, audit trails with PII, and potentially PHI. A data breach would have regulatory (HIPAA, GDPR), legal (notification requirements), and reputational consequences.

Question: Conduct a data breach impact analysis for the WO system, including: (1) data classification of all WO data elements (public/internal/confidential/restricted), (2) breach scenario modeling (database compromise, API key exposure, insider threat), (3) notification timeline requirements per regulation, (4) technical controls gap analysis.

Expected Output: (1) Data classification matrix for all WO tables/fields, (2) Three breach scenario analyses with impact assessment, (3) Notification requirement timeline per jurisdiction, (4) Technical controls checklist (encryption at rest, in transit, field-level, tokenization), (5) Incident response playbook for WO data breach.

CODITECT Value: Pre-built incident response and data classification accelerate security compliance reviews — reducing enterprise sales cycles.

6.3 Risk: Regulatory Change Impact Framework​

Context: Regulated industries face continuous regulatory evolution (new FDA guidances, EU MDR amendments, ISO standard revisions). The WO system must adapt to regulatory changes without architectural rework.

Question: Design a regulatory change impact framework that: (1) monitors regulatory updates from FDA, EMA, ISO, and maps them to WO system features, (2) assesses impact severity (cosmetic, moderate, architectural), (3) generates change requests (WOs) for system updates triggered by regulatory changes, (4) maintains a regulatory change audit trail.

Expected Output: (1) Regulatory monitoring architecture (RSS/API feeds → classification → impact assessment), (2) Impact assessment rubric with scoring criteria, (3) Auto-generated WO template for regulatory-driven changes, (4) Dashboard specification for regulatory change tracking.

CODITECT Value: Proactive regulatory change management is a premium feature that justifies higher pricing — customers pay for certainty that they'll stay compliant as regulations evolve.

Summary​

CategoryPromptsID Range
Architecture Deep-Dives51.1 — 1.5
Compliance & Regulatory42.1 — 2.4
Multi-Agent Orchestration43.1 — 3.4
Competitive & Market Intelligence34.1 — 4.3
Product Feature Extraction45.1 — 5.4
Risk & Mitigation36.1 — 6.3
Total23

Each prompt is self-contained, includes CODITECT BIO-QMS context, specifies expected output format, and identifies product value. Prompts can be executed individually via /research-pipeline or in batches using the research pipeline orchestrator.

Copyright 2026 AZ1.AI Inc. All rights reserved. Developer: Hal Casteel, CEO/CTO Product: CODITECT-BIO-QMS | Part of the CODITECT Product Suite Classification: Internal - Confidential