Toward Conversational System Design

Title: From Validation to Emergence — insights from 18 months of independent systems research

Note: This post outlines ongoing research. Technical mechanisms are intentionally abstracted. Terminology and design structure are under refinement.

Rethinking Validation

Event Modeling and related approaches have transformed how we visualize and define software systems. They allow teams to model events, commands, timelines, and read states with clarity. Validation tools built on these principles help catch modeling errors, ensure completeness, and enforce conventions.

But what if validation could do more than detect mistakes? What if it could surface insights—automatically, in context, during design?

Illustration of conversational system design, showcasing how insights emerge interactively during the design process. The image focuses on user registration and email verification scenarios, emphasizing the shift from validation to collaborative inference.

A Shift in Interaction

Most system design today assumes a linear pipeline: describe intent, build models, run validation, correct errors. But real design work is nonlinear. It’s exploratory, fragmented, contextual.

We’re developing a system that supports conversational emergence—where insights arise interactively as you describe your domain.

Example Interaction

You: "I need to handle user registration"

System: "That introduces opportunities for follow-up actions.
Have you considered email verification?"

You: "Interesting. What would that involve?"

System: "Email verification could lead to onboarding steps, such as welcome messages or delayed feature access.
This might affect how other processes interact."

This isn’t correction after the fact. It’s collaborative inference during design. Not validation as policing—but validation as a co-architect.

Semantic Discovery

This approach treats business scenarios as rich semantic input. Even in vague or informal language, a scenario contains:

Intent
Causality
Actor roles
Outcome dependencies

By extracting and aligning these patterns, the system surfaces:

Architectural structure (e.g., process boundaries, trigger points)
Dependency chains (e.g., time-based effects)
Integration points (e.g., external system touchpoints)
Behavioral assumptions (e.g., retries, confirmations, reversals)

This isn’t about replacing system designers. It’s about compressing the gap between domain description and architecture inference.

Why This Matters

Traditional tools require upfront modeling fluency. Conversational emergence reduces that barrier.

Domain experts speak in business terms. The system infers technical consequences and flags gaps.

This:

Lowers cognitive load
Prevents design drift
Surfaces implicit system behavior early
Reduces dependence on post hoc documentation
Shifts architectural thinking from reactive to generative

Origin and Influence

This work draws from event-driven architecture, systems thinking, and outcome-oriented modeling. It builds on—but does not replicate—prior frameworks in event modeling, event sourcing, and domain storytelling.

The contribution is not a new theory, but a new structure of interaction: Declarative input → Pattern inference → Structural feedback → Design emergence.

Next Phase

Current focus:

Testing with live domain scenarios
Measuring design velocity under guided feedback
Abstracting inference logic from specific modeling formats
Formalizing validation constraints as composable patterns

Toward a New Paradigm

We’re not replacing traditional design. We’re replacing its rigidity.

The system is not the architect. But it makes the architect’s thinking visible, traceable, and testable—in real time.

Architecture doesn’t need to begin with diagrams. It can begin with conversation.

For research updates or collaboration, stay connected. Full technical details remain under controlled release pending validation.