Reproducible SWMM execution
Run SWMM in a deterministic, inspectable way. Each stage emits traceable run artifacts and QA-oriented checks before any output is treated as evidence.
Vision
From running models to a workflow you can trust
Agentic SWMM grew from a simple idea — make EPA SWMM easier to run — into something larger: a workflow that also remembers what was done, what assumptions were made, what failed, and whether the results can be trusted.
The initial goal vs. the real problem
When this project started, the goal was simple: make EPA SWMM easier to install, run, reproduce, and check through an agentic workflow.
But as development continued, it became clear that real hydrological modeling is not only about running a model. A modeling workflow should also remember what was done, what assumptions were made, what failed, what was corrected, and whether the results can be trusted.
That shift in thinking is the heart of Agentic SWMM. The runtime can coordinate the work, but model files, SWMM runs, QA checks, plots, provenance records, and audit notes all remain visible as reusable artifacts — never hidden inside a chat. The goal is not to replace SWMM or the modeler, but to make SWMM-based modeling easier to reproduce, audit, remember, and trust.
"To me, this is an important step from 'AI runs a SWMM model' toward 'AI helps build a reproducible, inspectable, and memory-informed modeling workflow.'"
The three-part solution
Execution, memory, and controlled refinement
Audit-based modeling memory
Record provenance and produce Obsidian-compatible experiment notes that summarize repeated assumptions, QA issues, missing evidence, failure patterns, and useful modeling lessons.
Controlled skill refinement
The memory layer uses these audited records to propose refinements to existing workflow skills — staying coupled to the current skill-driven framework.
Human review & safe AI
A key point is that this is not autonomous self-editing. The memory layer can surface recurring patterns and propose refinements to existing workflow skills, but accepted changes still require human review and benchmark verification before they take effect.
Because skills drive the workflow, every proposal stays coupled to the current Agentic SWMM framework — it cannot quietly rewrite itself. This keeps the modeler in control and makes it safe to use AI in real, professional stormwater projects.