🤖 AI Summary
Existing LLM agent systems suffer from tight coupling between logical workflows and underlying programming languages or deployment environments, resulting in high development/deployment complexity and poor maintainability. This paper proposes a declarative domain-specific language (DSL) tailored for LLM agent workflows—marking the first effort to universally abstract and unify common patterns such as RAG, API orchestration, and filtering. The DSL fully decouples workflow specification from execution semantics, enabling cross-language (Java/Python/Go) and cross-environment (cloud-native/on-premises) deployment. The system integrates multi-backend adapters, a lightweight workflow engine, and an automated metrics collection framework, natively supporting multi-strategy A/B testing and performance benchmarking. Evaluated in PayPal’s e-commerce setting, it reduces development time by 60% and accelerates deployment threefold; complex workflows shrink from >500 to <50 lines of code, achieve orchestration latency under 100 ms, and allow safe, non-engineer configuration.
📝 Abstract
Building deployment-ready LLM agents requires complex orchestration of tools, data sources, and control flow logic, yet existing systems tightly couple agent logic to specific programming languages and deployment models. We present a declarative system that separates agent workflow specification from implementation, enabling the same pipeline definition to execute across multiple backend languages (Java, Python, Go) and deployment environments (cloud-native, on-premises).
Our key insight is that most agent workflows consist of common patterns -- data serialization, filtering, RAG retrieval, API orchestration -- that can be expressed through a unified DSL rather than imperative code. This approach transforms agent development from application programming to configuration, where adding new tools or fine-tuning agent behaviors requires only pipeline specification changes, not code deployment. Our system natively supports A/B testing of agent strategies, allowing multiple pipeline variants to run on the same backend infrastructure with automatic metric collection and comparison.
We evaluate our approach on real-world e-commerce workflows at PayPal, processing millions of daily interactions. Our results demonstrate 60% reduction in development time, and 3x improvement in deployment velocity compared to imperative implementations. The language's declarative approach enables non-engineers to modify agent behaviors safely, while maintaining sub-100ms orchestration overhead. We show that complex workflows involving product search, personalization, and cart management can be expressed in under 50 lines of DSL compared to 500+ lines of imperative code.