arXiv:2607.07727v1 Announce Type: cross Abstract: We present SPL (Structured Prompt Language), a declarative language that composes deterministic and probabilistic computation modes in a single specification. While existing frameworks separate these -- orchestration systems (AutoGen, CrewAI, LangGraph) for LLM calls, symbolic tools (SymPy, SageMath, Lean) for computation -- SPL unifies them. It provides GENERATE/EVALUATE for probabilistic computation and SOLVE/ASSERT for deterministic computation, sharing syntax, variable bindings, and runtime routing. A .spl specification runs unchanged acros
The proliferation of Large Language Models (LLMs) has highlighted the need for more sophisticated and unified orchestration of both probabilistic (LLM-based) and deterministic (symbolic computation) workflows, which existing frameworks currently separate.
This development proposes a unified language for orchestrating complex AI workflows, potentially streamlining the creation and deployment of advanced autonomous agentic systems by integrating diverse computational approaches.
Existing distinctions between LLM orchestration systems and symbolic computational tools could blur, leading to more integrated and efficient development paradigms for AI applications.
- · AI developers
- · Robotics
- · Software engineering
- · Academic researchers
- · Fragmented orchestration tool vendors
Developers can more easily build sophisticated AI agents that combine creative LLM outputs with precise symbolic reasoning.
The unified approach could accelerate the development of more reliable and auditable general-purpose AI applications across various industries.
This could lead to a 'standardized API' for designing complex AI systems, fostering greater interoperability and innovation in the AI ecosystem.
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Read at arXiv cs.CL