A Language for Describing Agentic LLM Contexts

2026-05-03

AI Summary

This work addresses the current lack of standardized methods for precisely characterizing the structure and dynamic evolution of input contexts in large language model (LLM) agent systems. To bridge this gap, the paper introduces ACDL—a formal, human-readable, and implementation-agnostic context description language that enables clear modeling of role-based message sequences, dynamic content, time-indexed references, and conditional or iterative constructs, complemented by visual representations. ACDL fills a critical semantic communication gap in LLM agent design and has been successfully employed to document multiple existing systems and their variants. An accompanying open-source toolchain and illustrative examples are provided to facilitate adoption within the research community and support its integration into academic discourse and system documentation.

Abstract

Large language models are increasingly used within larger systems ("LLM agents"). These make a sequence of LLM calls, each call providing the LLM with a combination of instructions, observations, and interaction history. The design of the encoded information and its structure play a central role in the quality of the resulting system, leading to efforts spent on context engineering. It is therefore critical to communicate the composition of the LLM context in a system, and how it evolves over time. Yet, no standard exists for doing so: context construction is typically conveyed through informal prose, ad hoc diagrams, or direct inspection of code, none of which precisely capture how a prompt evolves across interaction steps or how two context representation strategies differ. To remedy this, we introduce the Agentic Context Description Language (ACDL), a language for specifying the structure and dynamics of LLM input contexts in a precise, readable, and standard manner, along with visualizations. ACDL provides constructs for specifying context aspects such as role message sequences, dynamic content, time-indexed references, and conditional or iterative structure, capturing the full architecture of a prompt independently of any particular implementation. ACDL diagrams can be hand drawn on a whiteboard, or written in formal language which can then be rendered. We describe the language, demonstrate it by documenting several existing systems and their variants, and encourage the community to adopt it for describing LLM systems context, both in day-to-day communication and in papers. Tooling, examples and documentation are available at www.acdlang.org.

Problem

• LLM agents
• context engineering
• prompt evolution
• context representation
• standardized description

Innovation

• Agentic Context Description Language
• LLM agents
• context engineering
• prompt structure
• dynamic context representation