arXiv:2606.17164v1 Announce Type: new Abstract: Prompting has become the primary interface between humans and generative AI, yet many natural language prompts remain fragile: roles, goals, constraints, and expected outputs are often buried in prose or left implicit. In agentic and software development workflows, a misread at the first handoff can propagate through every step, since a significant portion of agent failures stem from context ambiguities rather than model limitations. This paper introduces PromptMN, a pseudo-prompting domain-specific language that annotates natural language with c
The proliferation of generative AI models and intelligent agents has highlighted the critical need for more robust and unambiguous prompt engineering to ensure reliable system performance.
Improving the reliability and clarity of human-AI interfaces is crucial for the adoption and safe deployment of agentic AI systems across various industries.
The introduction of domain-specific languages for prompting will standardize how humans communicate with AI, reducing ambiguity and improving the predictability of AI responses.
- · AI developers
- · Enterprises deploying AI agents
- · Prompt engineering platforms
- · Software developers
- · Ad-hoc prompt engineering approaches
- · Generative AI models with poor interpretability
PromptMN will streamline the development and deployment of complex AI agent workflows by formalizing prompt structures.
Standardized prompting languages could lead to a new layer of tooling and platforms built around prompt management and optimization.
Reduced ambiguity in AI interactions may accelerate enterprise adoption of AI, leading to more sophisticated automated systems and potentially new business models.
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