Problem
Automation systems become brittle when routing, validation, retries, and human override are scattered across prompts, background jobs, and ad hoc glue code.
R&D case study
DGM
Workflow orchestration layer in active development for managing state, decision flow, and human review inside StormIQ.
At a glance
Delivery stage
R&D
Current state
Active Build
My role
Sole architect and engineer building the orchestration layer
In-progress proof surface
DGM is under active development, with proof added as it becomes real.
This case study is structured to accept real walkthroughs, diagrams, and operational artifacts later. Until those exist, the page stays text-led and explicit about what is still in progress.
System Walkthrough (planned)
A walkthrough will be added when the orchestration loop is stable enough to demonstrate real state transitions instead of mocked steps.
Architecture / Flow
The architecture direction is already concrete: stateful orchestration, controlled branches, and explicit review seams.
What was built
DGM is being built as the orchestration backbone that will coordinate StormIQ workflows. The current direction is a graph-driven execution model that can move work through deterministic steps, agent-assisted branches, validation checks, and human review without losing system state or hiding decisions inside one opaque process.
Result
Execution model is defined for graph-driven workflow state, validation boundaries, and human review seams; implementation is in progress
How the system was structured
This section shows the operational logic behind the build, not just the user-facing surface.
Key system pieces
Graph-based execution model for multi-step workflow state.
Validation seams between agent output, business rules, and human review.
Retry-safe orchestration intended to keep workflow state inspectable.
Core constraint
State integrity: orchestration has to keep workflow state, validation, and human intervention inspectable instead of letting decisions disappear inside one agent loop
Stack
PythonFastAPIWorkflow GraphsQueue-backed JobsValidation Layers
Proof status
This page separates what is already visible from what is still being prepared, so the proof layer can grow without pretending unfinished artifacts already exist.
Proof surfaces
Real artifacts stay visible. Missing artifacts are labeled directly so this page stays honest and ready for stronger proof later.
System Walkthrough (to be added)
In progressA walkthrough will be added when the orchestration loop is stable enough to demonstrate real state transitions instead of mocked steps.
- Walkthrough to be added once graph execution can be shown with durable state transitions.
- Current state: the execution model is defined, but the public walkthrough would still be too early.
Architecture / Flow
Available nowThe architecture direction is already concrete: stateful orchestration, controlled branches, and explicit review seams.
- Graph-driven workflow state as the core execution model.
- Agent-assisted branches bounded by validation and deterministic rules.
- Human review seam built into the orchestration path rather than bolted on later.
Operational Surfaces
Available nowThe system is being designed for real operator visibility instead of hidden background automation.
- Workflow state inspection for checking where a task is and why.
- Validation checkpoints for high-risk or ambiguous transitions.
- Retry and recovery boundaries so failed steps do not corrupt the whole workflow.
Artifacts & Evidence (to be added)
In progressProof is being staged honestly: no fake screenshots, no fake outcomes, and no premature claims.
- Execution graph artifact in progress.
- State transition examples to be added when durable runs are available.
- Operator review surface screenshots to be added once the UI stabilizes.
Related case studies
More work at a similar delivery stage.
Active Build
Proof surface in progress
Architecture / Flow
The current architecture direction is already clear even though the full artifact set is not published yet.
R&DActive BuildApplied AI & Automation Systems
WeatherForge
Weather-triggered signal and routing system in active development to turn storm activity into usable operational input for StormIQ.
My Role
Sole architect and engineer building the system foundation
Outcome
System boundaries are defined for signal ingestion, normalization, territory relevance, and downstream routing; implementation is in progress
PythonFastAPIGeospatial ProcessingEvent Routing
Read case study
R&DArchitecture in ProgressApplied AI & Automation Systems
StormIQ
Broader lead-automation direction being built on top of WeatherForge and DGM rather than treated as a finished standalone system.
My Role
Sole architect defining the system direction and building the foundation layers
Outcome
StormIQ is now framed as a serious in-progress program direction, with WeatherForge and DGM acting as the concrete systems under active development
PythonFastAPIWorkflow OrchestrationQueue-backed Jobs
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