David Wolf · Project Use Case
AI SECURITY · PRODUCT SECURITY · CONFIDENTIAL AI AUTOMATION PROGRAM
Confidential AI Automation Program
Agentic Workflow Migration & DSL Automation Platform
Migrating brittle AI automation experiments into governed, Git-based, containerized agent workflows with schemas, verification, scoring, and acceptance...
Designed and implemented a migration path from brittle n8n and AutoGPT-style automations toward more governable Flowise and Sim Studio workflows, backed by a custom workflow DSL, containerized cron-based harvesters and...
Client
Confidential / Internal AI Automation Program
Engagement Type
Consulting / Research / Buildout
Period
2025–2026
Role
AI Security Architect / Agentic Workflow Engineer
Focus Areas
Agentic Workflow Migration, Workflow DSL Design, Flowise Workflow Modeling, Sim Studio Workflow Modeling
The Research Narrative
Strategic Problem
The problem was not simply moving nodes from one tool to another. The real challenge was preserving intent while making triggers, skills, tools, schemas, prompts, outputs, scoring, and acceptance criteria...
What David Did
David defined a custom workflow DSL that represented agent workflows as portable software artifacts. Legacy workflows were mapped into common primitives, then converted toward Flowise and...
What Became Clearer
The result was a migration architecture and operating model for governed agentic workflows. It made AI automation more portable, reviewable, testable, and promotable through explicit...
Consulting Proof
This is evidence of turning messy security telemetry into explainable dashboards, alert-quality improvements, and executive-ready operating views.
The Context
Early agentic automation often begins in tools like n8n, AutoGPT-style scripts, and visual workflow builders. That is useful for exploration, but the resulting workflows can become hard to test, hard to migrate, hard to secure, and hard to explain. The project turned that chaos into a more disciplined workflow engineering model.
The Challenge
The problem was not simply moving nodes from one tool to another. The real challenge was preserving intent while making triggers, skills, tools, schemas, prompts, outputs, scoring, and acceptance criteria explicit. Without that structure, migration would only recreate the same brittle automation in a newer interface.
What I Did
David defined a custom workflow DSL that represented agent workflows as portable software artifacts. Legacy workflows were mapped into common primitives, then converted toward Flowise and Sim Studio targets. Containerized cron jobs handled harvesting and conversion, while LLM skill definitions and schemas made translation and validation more reliable.
- •Mapped existing n8n and AutoGPT-style automations into common workflow primitives: triggers, tasks, tools, skills, memory, state, input schemas, output schemas, assertions, and acceptance gates
- •Designed a custom DSL for describing agentic workflows independently of any one visual workflow platform
- •Defined translation patterns from legacy workflow definitions into Flowise and Sim Studio-compatible structures
- •Built containerized harvesters and converters that ran as scheduled cron jobs to collect, normalize, transform, and emit workflow artifacts
- •Defined LLM skills as explicit, reusable units with names, descriptions, input contracts, output contracts, constraints, examples, and failure modes
- •Created JSON-schema-style validation expectations for workflow definitions, generated outputs, tool calls, and conversion artifacts
- •Implemented Git-based workflow promotion where changes could be reviewed, diffed, scored, and accepted through explicit criteria
- •Used AI-assisted agents to identify workflow bugs, propose fixes, explain failures, and generate corrected artifacts
The Outcome
The result was a migration architecture and operating model for governed agentic workflows. It made AI automation more portable, reviewable, testable, and promotable through explicit gates. The same pattern can support internal automation, productized AI workflows, secure agent deployments, and future compliance evidence.
Research Outcomes
Signal Quality
Improved the trustworthiness of operational security signals
Operational Clarity
Translated complex security data into clearer operating views
Stakeholder Visibility
Made technical risk and status easier to explain
Operational Impact
Turned raw telemetry into actionable security intelligence
Capabilities Demonstrated
Dashboard Development
Operational and executive views
Operational Reporting
Actionable views for security operations
Security Analytics
Signal investigation and event analysis
IAM / Access Control
Identity telemetry and access insights
SIEM Alert Debugging
Noise reduction and signal validation
Executive Reporting
Security data translated for leadership
Telemetry Normalization
Consistent and trusted data
Public-Safe Evidence
Shareable insights without sensitive data
Key Deliverables
- •Custom agentic workflow DSL
- •Workflow migration model from n8n and AutoGPT-style automations to Flowise and Sim Studio
- •Containerized cron-based harvester pattern
- •Containerized converter pattern for workflow artifact transformation
- •LLM skill definition schema
- •Workflow input and output schema conventions
- •Git-based workflow review and promotion model
- •AI-assisted bug-detection and bug-fix workflow
Tools & Technologies
Consulting Translation
The reusable pattern is not Disney-specific: normalize fragmented security telemetry, debug low-signal alert behavior, build trusted operating views, and give leadership evidence they can act on without exposing sensitive systems.