arXiv:2508.02721v2 Announce Type: replace-cross Abstract: While powerful, the inherent non-determinism of large language model (LLM) agents limits their application in structured operational environments where procedural fidelity and predictable execution are strict requirements. This limitation stems from current architectures that conflate probabilistic, high-level planning with low-level action execution within a single generative process. To address this, we introduce the \textsc{Source Code Agent} framework, a new paradigm built on the ``Blueprint First, Model Second'' philosophy that dec
The proliferation of LLMs highlights their non-deterministic nature as a critical barrier to deployment in sensitive, highly-structured operational environments.
Achieving deterministic and predictable LLM behavior is essential for their adoption in enterprise workflows and safety-critical applications, enabling reliable automation.
This framework proposes a paradigm shift in LLM workflow design, separating high-level planning from low-level execution to ensure procedural fidelity and verifiable outcomes.
- · Software engineering
- · Enterprise AI platforms
- · Automation companies
- · Regulated industries
- · LLM applications requiring high determinism
- · Ad-hoc LLM integration approaches
Enterprise adoption of LLMs accelerates as reliability and predictability increase.
New tooling and best practices emerge for designing, deploying, and verifying deterministic LLM workflows.
The scope of problems amenable to LLM-driven automation expands significantly, impacting white-collar work previously deemed too sensitive for AI.
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Read at arXiv cs.AI