GxP and Part 11 on Make.com: Validating Agentic Workflows in Life Sciences

GxP and Part 11 on Make.com: Validating Agentic Workflows in Life Sciences

1. Problem / Context

Life sciences companies are under pressure to modernize operations while staying compliant with GxP and 21 CFR Part 11. Teams want to orchestrate agentic workflows—automations that can reason, take actions, and coordinate across systems—using accessible platforms like Make.com. But without a clear validation strategy, these workflows can create audit risk: missing evidence, ungoverned prompts, weak audit trails, and uncontrolled changes. Mid-market firms, in particular, must balance lean resources with rigorous documentation, vendor oversight, and durable e-records.

2. Key Definitions & Concepts

• GxP: Umbrella for Good Practices such as GMP, GLP, and GCP. It dictates how processes, data, and systems should be controlled to protect patient safety and product quality.
• 21 CFR Part 11: U.S. FDA regulation defining requirements for electronic records and electronic signatures (e-records/e-sigs), including validation, audit trails, time-stamped events, security, and record retention.
• Agentic workflow: An orchestrated automation that can decide next best actions, call tools, and route exceptions to humans-in-the-loop (HITL), with clear boundaries and oversight.
• Make.com scenario: A low-code/no-code flow connecting systems (e.g., QMS, LIMS, ERP, e-signature tools). In a validated context, scenarios, configurations, connection tokens, and prompts must be treated as controlled configuration items.
• Validation: IQ/OQ/PQ. Installation Qualification (IQ) confirms the platform and environment are installed/configured as intended. Operational Qualification (OQ) proves controls and functions operate as specified. Performance Qualification (PQ) demonstrates the end-to-end process consistently performs in the intended use with representative data.
• ALCOA+: Data integrity principles: Attributable, Legible, Contemporaneous, Original, Accurate, plus Complete, Consistent, Enduring, Available.

3. Why This Matters for Mid-Market Regulated Firms

Mid-market life sciences organizations face enterprise-grade scrutiny with smaller teams. Auditors expect evidence that low-code automations are validated and governed like any GxP system. The stakes include batch release delays, deviation backlogs, or inspection citations. A repeatable, right-sized validation approach for Make.com prevents “pilot sprawl,” reduces manual effort, and builds confidence that agentic steps (including prompts and model policies) are under change control and can be defended in audits.

4. Practical Implementation Steps / Roadmap

1) Classify use cases by GxP impact

• Map candidate workflows (e.g., deviation triage, supplier change notifications, stability study reminders, controlled vocab synchronization) to GxP categories. Decide which are GxP-relevant and which are business-only. Apply Part 11 rigor to any workflow that creates, modifies, or relies upon e-records used in regulated decision-making.

2) Supplier qualification and quality agreement

Qualify Make.com as a supplier according to your QMS. Document platform SLAs, security posture, availability, and change notification processes. Establish a quality (or technical) agreement defining responsibilities for uptime, backups/exports, and access controls.

3) Define URS and risk-based validation plan

Write a User Requirements Specification (URS) focused on Part 11: e-record generation, time-stamped audit trails, HITL approvals, and e-signature integrations (e.g., with a validated QMS or e-signature tool). Tie each requirement to risk and plan testing accordingly.

4) Architect the governed scenario

Separate environments (DEV/TEST/PROD). Use service accounts with least privilege. Centralize secrets and rotate tokens. Design HITL steps for critical decisions. Route all records and logs to a validated repository (e.g., QMS, EDMS) for retention.

5) IQ for Make.com

Capture platform version, provisioned regions, enabled features, and integrations. Record environment setup, SSO configuration, roles/permissions, and time synchronization. Baseline the scenario and connectors as configuration items.

6) OQ for controls and functions

Verify access control, role-based restrictions, session timeouts, and IP allowlisting if applicable. Test audit trail completeness: each run should produce time-stamped events with user/service attribution. Challenge the workflow with negative tests (bad data, failed API calls) and verify error handling and alerting.

7) PQ for intended use

Execute end-to-end runs using representative data. Include HITL steps where humans review or e-sign. Confirm outputs (e.g., deviation classifications) match controlled vocabularies and are stored in the authoritative system of record. Document evidence with screenshots, log exports, and result summaries.

8) E-records, e-signatures, and repositories

Do not treat Make.com as the final records system. Store e-records in a validated EDMS/QMS with version control and retention. Integrate with a compliant e-signature solution and ensure signature meaning, time, and identity are captured and traceable to records.

9) Data integrity and controlled vocabularies

Implement ALCOA+ by design: use immutable IDs, persist original payloads, time-stamp at source and at ingestion, ensure legibility and traceability. Synchronize controlled vocabularies (e.g., reason codes, lot status) from the master data system, and validate that only approved values flow through.

10) Backup and disaster recovery

Schedule exports of scenario definitions and connection metadata. Send execution logs to a centralized, backed-up repository (e.g., data lake + EDMS snapshots). Periodically test restoration of scenarios and records retrieval.

11) Change control for prompts and policies

Treat prompts, routing logic, model policies, and transformation rules as software items. Version them, require impact assessments, regression tests, and approvals before promoting to PROD. Maintain a Requirements Traceability Matrix (RTM) linking changes to URS and test evidence.

12) Pilot-to-production with periodic review

Run a time-boxed pilot under controlled conditions. After PQ acceptance, promote to PROD with training, SOP updates, and monitoring. Schedule periodic reviews (e.g., quarterly) to re-verify controls, re-run key OQ tests, and confirm third-party changes (APIs, platform updates) haven’t affected validated state.

5. Governance, Compliance & Risk Controls Needed

• Access and segregation of duties: separate designers, approvers, and operators. Use named accounts; avoid shared credentials.
• Configuration management: baseline scenarios, connectors, prompts, and mappings; require change requests and reviews.
• Audit trails and retention: ensure tamper-evident, time-stamped logs with user attribution are exported to an immutable or versioned store.
• Model and prompt governance: define allowed models, temperature, context windows, and redaction policies. Document rationale and include in OQ tests.
• Vendor lock-in mitigation: export scenario JSON and maintain human-readable configuration specs; keep test harnesses independent where possible.
• Incident/CAPA: capture deviations, root causes, and corrective actions tied to the automation; re-validate when needed.
• Training and SOPs: ensure staff are trained on HITL steps, e-sign procedures, and recovery.
• Ongoing monitoring: metrics, alerting, periodic OQ re-checks after platform or API changes.

Kriv AI, as a governed AI and agentic automation partner for mid-market organizations, often helps teams codify these controls so that validation evidence is produced as a byproduct of daily operations.

6. ROI & Metrics

Regulatory-grade automation must justify itself with measurable outcomes:

• Cycle time reduction: e.g., deviation triage from 3 days to 1.9 days (≈35% improvement) through automated intake, classification, and routing.
• Error rate: reduction in misclassified events or missing attachments by 50–70% with controlled vocab checks and mandatory fields.
• First-pass yield for documentation: increase the percentage of records accepted without rework by 15–25% via HITL checkpoints and template enforcement.
• Labor savings: replace manual data gathering across QMS/LIMS/ERP with agentic orchestration—often freeing 0.3–0.7 FTE per workflow.
• Audit readiness hours: cut pre-inspection prep time by consolidating logs, evidence, and traceability into a single pack generated by the workflow.
• Payback period: under a risk-based, right-sized scope, mid-market firms frequently see payback in 3–6 months for high-friction quality workflows.

To make ROI credible, define baselines during discovery, instrument metrics in the workflow (timestamps, counts, error flags), and review them monthly. Kriv AI’s governance-first approach helps ensure these metrics are trustworthy and auditable.

7. Common Pitfalls & How to Avoid Them

• Treating prompts as “not code”: Version and validate prompts/policies exactly like software.
• Relying on platform logs alone: Export and preserve audit trails in a validated repository with retention and access controls.
• Uncontrolled vocabularies: Sync master lists and harden scenarios to reject unknown values.
• Skipping supplier qualification: Establish quality agreements to cover uptime, change notifications, and data handling.
• Weak backup strategy: Regularly export configurations and test restores; keep e-records in an EDMS/QMS, not only in Make.com.
• Pilot forever: Move from pilot to PQ to PROD with documented evidence and periodic review; retire or re-validate deprecated flows.

30/60/90-Day Start Plan

First 30 Days

• Inventory candidate workflows and classify by GxP impact.
• Draft URS emphasizing Part 11 controls, HITL steps, and e-sign needs.
• Begin supplier qualification and a quality agreement with Make.com.
• Stand up DEV/TEST environments; define roles, SSO, and secrets management.
• Baseline controlled vocabularies and data sources; collect current metrics.

Days 31–60

• Build pilot scenarios with agentic orchestration and HITL checkpoints.
• Execute IQ and OQ; instrument audit trails, error handling, and monitoring.
• Integrate with EDMS/QMS for e-record storage and with a compliant e-signature tool.
• Draft SOPs and train pilot users; prepare RTM and test scripts.
• Run PQ with representative data; document evidence and outcomes.

Days 61–90

• Promote accepted pilot(s) to PROD under change control.
• Establish periodic review cadence; re-run targeted OQ after platform/API changes.
• Expand to adjacent workflows (e.g., supplier changes, CAPA updates) using reusable validation patterns.
• Operationalize ROI reporting—cycle time, error rate, first-pass yield, and payback tracking.
• Align stakeholders on roadmap and maintenance responsibilities.

9. Industry-Specific Considerations

• Pharmaceuticals and Biotech: Focus on batch record adjuncts, deviation/CAPA, stability, and QC data handoffs. Emphasize traceability and chain of custody.
• Medical Devices: Tie workflows to ISO 13485/14971; prioritize complaint handling, UDI, and design history file updates.
• Clinical: Ensure source data traceability, protocol amendments handling, and site documentation routing with time-stamped evidence.

10. Conclusion / Next Steps

Agentic automation on Make.com can be validated and Part 11-ready when approached with risk-based controls: supplier qualification, IQ/OQ/PQ, robust e-records and audit trails, ALCOA+ by design, and disciplined change control for prompts and policies. The result is faster, more reliable quality operations without compromising compliance.

If you’re exploring governed Agentic AI for your mid-market organization, Kriv AI can serve as your operational and governance backbone—helping with data readiness, MLOps, and validation workflows so your team scales automation with confidence.