Applying Domain-Driven Design to a Telephone Robot Project: Challenges, Implementation Steps, and Team Benefits

Introduction

Domain‑Driven Design (DDD) is a methodology with many meta‑models and concepts that aim to solve concrete development problems; beginners often get lost in the terminology, so it is important to focus on the problems DDD addresses.

Background

Since 2018 the data‑architecture team built a telephone‑robot platform serving dozens of business units, handling hundreds of thousands of outbound calls daily. The platform grew to over 100 different robots, but the team encountered many challenges that led them to refactor the system using DDD.

Challenges

1. High business‑logic complexity due to diverse scenarios and AI‑driven intent recognition. 2. Unclear code architecture caused by growing codebase and inconsistent module boundaries. 3. Unclear product value of many requirements. 4. Rapidly changing logic requiring frequent redesign. 5. Inconsistent business terminology leading to high communication cost.

Why DDD

DDD addresses complexity by classifying it into scale, structure, and change. It provides a full‑process guidance from requirement analysis to modeling and implementation, introducing bounded contexts, context mapping, layered architecture, and proactive design for change.

DDD Implementation Steps

1. Pre‑research phase : team members study DDD concepts and share knowledge. 2. Introduce guiding principles and standards : adopt the 5W model for requirement analysis and define service contracts that double as test specifications. 3. Determine architecture solution : split responsibilities using bounded contexts, apply a layered COLA 4.0 architecture, and use a diamond‑shaped design with north‑south gateways. 4. Consensus on naming standards : agree on class, request/response naming conventions. 5. Identify bounded contexts based on business features : perform event‑storming to map domains. 6. Modeling and implementation : use test‑driven development (red‑green‑refactor) and follow DDD’s three laws.

Team Improvements

• Requirement analysis becomes proactive using the 5W principle, improving value assessment and change control. • Unified language reduces communication overhead across teams. • Layered architecture and bounded contexts clarify code structure, mitigating the earlier tangled module dependencies. • Technical implementation benefits from clearer domain/service layer decisions and optional rich‑model usage. • Documentation is standardized through service contracts, serving both requirement clarification and test bases. • Coding conventions are established for naming, layering, and documentation.

Conflicts Between Theory and Practice

• Debate over anemic vs. rich domain models; the team prefers rich models where feasible but does not enforce it strictly. • Strict data‑conversion constraints versus pragmatic shortcuts for stable external data. • Cache sharing across bounded contexts must be evaluated for proper isolation. • Service contracts sometimes clash between front‑end and back‑end expectations; the team chose back‑end ownership while moving validation to the interface layer. • Responsibility for contract correctness shifted from product owners to technical leads due to practical considerations.

Future Directions

The team plans to refine aggregate, entity, and value‑object designs, apply DDD retroactively to legacy projects, and continue balancing DDD rigor with development efficiency.